To Register for any of the Viral Pandemics Working Group seminars please visit: 
https://iu.zoom.us/meeting/register/tZYqd-2srD8tGtCXDem4Cka08rBz5fDW0EQR

You can access the entire set of Viral Pandemic WG seminars at our YouTube Playlist: 
https://www.youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat  
(click the "Subscribe" button if you would like to be notified when new videos are posted)

December 12, 2024 Meeting

1. Valeriu Damian, Independent Consultant, will discuss: Digital Twin approaches in Quantitative Systems Pharmacology.
   Originally developed in the manufacturing industry, digital twin concepts have emerged as a transformative tool in quantitative systems pharmacology (QSP). This talk explores how digital twins can estimate population responses in clinical trials and enable personalized treatments, particularly for rare diseases and severe conditions where large-scale trials are impractical. We will compare digital twin methodologies with traditional virtual population algorithms in QSP modeling, highlighting how these approaches integrate patient-specific clinical data, and treatment history to build individualized models. Using a pediatric bone marrow transplant model as an example, we will discuss the challenges of balancing model accuracy with timely utility, emphasizing the importance of actionable predictions within the clinical decision-making window.
   YouTube and Slides.

October 31, 2024 Meeting

1. Nav Nidhi Rajput, Stony Brook University, will discuss: Materials Informatics for Structure-Property Relationships (MISPR) for Liquid Solution. 
   Liquid solutions are critical components of various chemical, materials science, engineering, and biological applications such as batteries, fuel, food industry, and drug discovery. Optimizing the performance of these technologies often necessitates exploring vast chemical and parameter spaces to avoid bias and derive trends only accessible by systematic datasets. In addition, understanding the structure, transport, and thermodynamic properties of chemical species comprising the solution is paramount to optimizing the performance of liquid applications. However, identifying the underlying correlations between the functional properties and the atomistic interactions can be a daunting task for multi-component complex liquid solutions, even by using advanced experimental and computational techniques. Driven by these needs, we developed an open-source high-throughput computational framework coined MISPR (Materials Informatics for Structure–Property Relationships) for guiding and accelerating materials discovery, optimization, and deployment of complex multicomponent liquid solutions.1-4 MISPR seamlessly integrates density functional theory (DFT) calculations with classical molecular dynamics (MD) simulations to robustly predict molecular and ensemble properties in complex multi-component liquid solutions. Functionalities of MISPR include (i) full automation of DFT and MD simulations, (ii) creation of computational databases for establishing structure-property relationships and maintaining data provenance and reproducibility, (iii) automatic error detection and handling, (iv) support for flexible and well-tested DFT workflows for computing properties such as bond dissociation energy, binding energy, and redox potentials, and (v) derivation of ensemble properties such radial distribution functions, ionic conductivity, and residence time. We demonstrate the unique features of MISPR by highlighting on one of its hybrid workflows that predicts stable species in liquid solutions from their nuclear magnetic resonance (NMR) chemical shifts.5 This workflow automatically extracts and categorizes hundreds of thousands of atomic clusters from MD simulations, identifies the most stable species in solution, and calculates their NMR chemical shifts via DFT calculations. The result is an output database of computed chemical shifts for liquid solutions across a wide chemical and parameter space. Our automated high-throughput framework overcomes various limitations of current NMR computational techniques,2 such as the Edisonian approach to building solvation structures, the difficulty in accounting for representative explicit solvation, and the substantial time required for manual job management. I will also discuss our recent work on using natural language processing (NLP) for extracting spectroscopy data of liquid solutions from literature. 
   * Atwi, R., Bliss, M., Makeev, M. et al. MISPR: an open-source package for high-throughput multiscale molecular simulations. Sci Rep 12, 15760 (2022). https://doi.org/10.1038/s41598-022-20009-w  
   * Atwi R, Chen Y, Han KS, Mueller KT, Murugesan V, Rajput NN. An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions. Nat Comput Sci. 2022 Feb;2(2):112-122. doi: 10.1038/s43588-022-00200-9. Epub 2022 Feb 28. PMID: 38177518. (Preprint available at https://www.researchsquare.com/article/rs-893249/v1)  
   * MISPR home page: https://www.molmd.org/mispr/html/index.html  
   YouTube and Slides.

October 17, 2024 Meeting

1. Vladana Vukojevic, Karolinska Institute: Quenching Small-Amplitude Limit Cycle Oscillations for Predictive Modeling of Complex Molecular Mechanisms Underlying Chemical and Biochemical Oscillatory Reactions. Insights into the Effects of Ethanol on the Hypothalamic-Pituitary-Adrenal (HPA) Axis Dynamics. 
   Quenching Small-Amplitude Limit Cycle Oscillations, i.e., Quenching Analysis (QA), is a method specifically designed to take the advantage of the oscillatory dynamics that emerges in the vicinity of a supercritical Hopf bifurcation and harvest it to acquire unique quantitative information about the investigated system that can easily be compared with model predictions. To this aim, QA relies on a series of controlled additions of a compound that is inherent to the system or reacts with a reactive species in the system, to immediately, yet temporarily suppress the oscillations. For each species, QA yields two values, the quenching concentration (qi), i.e., the amount of species i needed, and the quenching phase (ji), i.e., the phase angle with respect to a reference point in an oscillation, at which this amount is to be added to temporarily pause the oscillations. By comparing the experimentally derived quenching concentrations and quenching phases with results obtained from mechanistic models, one can determine whether key reaction pathways are correctly identified and incorporated in the model of the reaction mechanism and assess how realistically the model reflects the true mechanism of the investigated system. The aim of this presentation is twofold: to present the theoretical background behind QA and to demonstrate the potential of this method for understanding specific features of a complex neuroendocrine dynamical system, the Hypothalamic-Pituitary-Adrenal (HPA) axis, under normal physiology and response to ethanol-induced stress. 
   1.    Čupić Ž, Stanojević A, Marković VM, Kolar-Anić L, Terenius L, Vukojević V. The HPA axis and ethanol: a synthesis of mathematical modelling and experimental observations. Addict. Biol. 2017 22(6):1486-1500. doi: 10.1111/adb.12409 
   2.    Abulseoud OA, Ho MC, Choi D-S, Stanojević A, Čupić Ž, Kolar-Anić Lj, Vukojević V. Corticosterone oscillations during mania induction in the lateral hypothalamic kindled rat experimental observations and mathematical modelling. PLoS One 2017, 12(5):e0177551. doi: 10.1371/journal.pone.0177551 
   3.    Stanojević A, Marković VM, Čupić Ž, Kolar-Anić Lj, Vukojević V. Advances in mathematical modelling of the hypothalamic–pituitary–adrenal (HPA) axis dynamics and the neuroendocrine response to stress. Current Opinion in Chemical Engineering 2018, 21:84–95. doi:10.1016/j.coche.2018.04.003 
   YouTube and Slides.

October 3, 2024 Meeting

1. Yoram Vodovotz, University of Pittsburgh. Critical Illness Digital Twins: Insight from the Fusion of Data-driven and Mechanistic Modeling.  
   Inflammation is the body’s way of informing itself of changes in homeostasis, either from without or within. It is necessary for proper healing and regeneration and a key component of the response to stress. A puzzle: inflammation can be both good and bad. 
   

   * JW Cannon, et al. Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies. Commun Med 4, 113 (2024). https://doi.org/10.1038/s43856-024-00535-6 
   * AM Shah et al. Computational inference of chemokine-mediated roles for the vagus nerve in modulating intra- and inter-tissue inflammation. Front. Syst. Biol., 14 February 2024, Sec. Data and Model Integration, Vol 4 (2024). https://doi.org/10.3389/fsysb.2024.1266279 
   YouTube and Slides. 

September 19, 2024 Meeting

• Cameron Griffiths, University of Virginia, will discuss: Systems biology insights into viral heart infection susceptibility and host responses. 
  Multiple viruses can infect the human heart and infection causes a range of outcomes from asymptomatic disease to heart failure. These outcomes are driven by patient-to-patient variation in viral susceptibility and responses to infection. In this presentation, I will highlight how data science-based systems biology approaches identify three fundamental ways human hearts respond to viral heart infection. In addition, I will describe how combining statistical modeling of RNA-protein relationships with a mechanistic model of coxsackievirus B3 infection can inform patient-specific susceptibilities to viral heart infection. Collectively, this work provides a framework for predicting and interpreting viral heart infections, which may lead to interventions that halt the progression to heart failure. 
   
  * Proteome-wide copy-number estimation from transcriptomics,  https://www.biorxiv.org/content/10.1101/2023.07.10.548432v1 
  * Three Modes of Viral Adaption by the Heart, https://www.biorxiv.org/content/10.1101/2024.03.28.587274v1 
  YouTube and Slides.

September 5, 2024 Meeting

1. John Metzcar, Indiana University, will discuss: Translating and applying Boolean network control theory in the multiscale setting. 
   Intracellular systems process cellular-level information and control cell fate. Understanding and controlling their dynamics is a main goal of computational systems biology. Often these systems are represented with Boolean networks due to their amenability for using relatively sparse data and fast simulation times. In our proof of concept work, we solve the Boolean network target control problem for the T-LGL leukemia cell survival network of Zhang et al (2008), yielding multiple node- and edge-level strategies to control cell fate (induce apoptosis). However, due to inherent limitations of the algorithms, these interventions are only suitable for cell-level determinations, contrasting with the more typical multicellular settings of both experiments and tissue. To address this, we developed a pipeline to translate these cell-level models to agent-based models and computationally explore interventions in high-throughput. Putting this all together produced a computational laboratory to develop phenotype control strategies and evaluate their robustness in the multicellular setting. Furthermore, we found interesting differences in the dynamics between the attractor and target controls we uncovered. Attractor controls acted in a “slow but steady” way to inhibit population growth while target controls sometimes quickly controlled the population, but were unable to control it in the long run. We also saw differences in agents’ spatial distribution with larger regions of efficacy corresponding to both the type of control (attractor versus target) and decreasing network distance between the intervention target and the controlled node. Overall, this work expands the toolkit of multiscale modeling, highlights aspects of network control that may be overlooked in the single-cell setting, and advances techniques for understanding and controlling multicellular systems biology. 
   YouTube and Slides.

August 8, 2024 Meeting

1. Bishal Paudel, University of Virginia will discuss: Systems Insights into Molecular Variability and Divergent Cancer Phenotypes. 
   Understanding how heterogeneity arises in cells of similar origin within a uniform environment is a major challenge in biology. In the context of cancer, identifying the processes that lead to tumor heterogeneity is crucial, as it significantly impacts tumor progression and treatment resistance. In this presentation, I will explore the mechanistic details behind the origins and consequences of heterogeneity in cancer using experimental, bioinformatics, and computational methods. In the first example, I will discuss the emergence of a multicellular phenotype in premalignant breast cancers, resulting from a cross-inhibitory feedback between oncogenic receptors and nucleocytoplasmic transport regulators. In the second example, I will present an integrative framework that combines multimodal measurements, machine learning, and systems pharmacology to identify two clinically relevant metabolic phenotypes in acute myeloid leukemia. Overall, this presentation will demonstrate how an integrative, systems biology approach provides insights into the early stages of tumorigenesis and the clinically relevant heterogeneity in cancers. 
   1. Paudel BB, Tan SF, Fox TE, Ung J, Shaw J, Dunton W, Lee I, Sharma A, Viny A, Barth BM, Tallman MS, Cabot M, Garrett-Bakelman FE, Levine RL, Kester M, Claxton D, Feith DJ, Janes KA, Loughran, TP. “Acute myeloid leukemia stratifies as two clinically relevant sphingolipidomic subtypes.” Blood Advances, 8(5), pp.1137-1142. 
   2. Wang L#, Paudel BB#, McKnight RA, Janes KA. “Nucleocytoplasmic transport of active HER2 causes fractional escape from the DCIS-like state.” Nat Commun 14, 2110 (2023). #(Equal contributions) 
   YouTube and Slides.

June 27, 2024 Meeting

1. Paul Roberts, Centre for Systems Modelling and Quantitative Biomedicine at the University of Birmingham, will discuss: Mathematical models to elucidate the mechanisms underlying sight loss 
   The retina is a tissue layer at the back of the eye that uses photoreceptor cells to detect light. Photoreceptors can be characterised as either rods or cones. Rods provide achromatic vision under low light conditions, while cones provide high-acuity colour vision under well-lit conditions. The term retinitis pigmentosa (RP) refers to a range of genetically mediated retinal diseases that cause the loss of photoreceptors and hence visual function. RP leads to a patchy degeneration of photoreceptors and typically directly affects the rods (which express a mutant gene), but not the cones. During the course of the disease, degenerate patches spread and the cones also begin to degenerate. The cause underlying these phenomena is currently unknown; however, several key mechanisms have been hypothesised, including oxygen toxicity, trophic factor depletion and the release of toxic substances by dying cells. Here we present mathematical models, formulated as systems of PDEs, to investigate the trophic factor hypothesis. Using a combination of numerical simulations and mathematical analysis, we determine the geographic variation in retinal susceptibility to degeneration, predict the effects of various clinically-relevant treatment strategies, predict spatio-temporal patterns of degeneration and solve an inverse problem to determine the conditions under which in vivo spatio-temporal patterns of degeneration are replicated by our models. 
   YouTube and Slides.

June 13, 2024 Meeting

1. Lorenz Adlung, UMC Hamburg-Eppendorf, will discuss: scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data 
   Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of biological processes. However, despite their high throughput, these measurements represent only a snapshot in time. But longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modeling to mechanistically describe molecular or cellular dynamics. In my talk, I will present two examples of how we are using time-resolved single-cell datasets to gain a better understanding of cellular signaling, immune responses, and tissue regeneration. Our multidisciplinary efforts are focused on developing methods for applying predictive models in biomedical contexts. For example, we envision that deconvolution of time-resolved bulk mRNA sequencing data could complement scRNA-seq resources, e.g. from the Human Cell Atlas, for ODE-based modeling to leverage large-scale single-cell data in clinical practice. 
   YouTube and Slides.
   • Dr. Adlung was the winner of the 2023 BioModels Computational Model of The Year Awardfor "Cell-to-cell variability in JAK2/STAT5 pathway”. The model is available at BioModels at MODEL2103080001.

May 30, 2024 Meeting

1. Michael Getz, Indiana University, will discuss: Towards a Virtual Cornea 
   Predicting corneal injury and recovery following chemical exposures is crucial for risk assessment and regulatory decision-making. Following chemical or physical injury the cornea invokes complex autocrine or paracrine interactions relating biophysical, electrophysiological, and physiological cues which can be difficult to recapitulate in vitro or extrapolate from data-based Machine Learning or molecular-level computer simulations. Solving this spatiotemporal nature of corneal wound healing is not trivial requiring multicellular simulations. To investigate this issue, we present Virtual Cornea, a two-dimensional agent-based model developed in CompuCell3D and Tissue Forge. Virtual Cornea is a Virtual Tissue (VT) model that aims to simulate cellular interactions and processes underlying homeostasis, injury response, and long-term recovery to further risk prediction methods. The model includes tear film, epithelium, epithelial basement membrane, and the stroma and their associated components. Of interest,  is the mechanical contribution of the extracellular matrix in the stromal layers and their effect on both the opacity and long-term recovery of the VCornea and the effect of chemical barrier functions through the basement membrane. 
   YouTube and Slides.

May 2, 2024 Meeting

1. Randy Heiland, Indiana University. “OpenVT – A Standardized Ecosystem for Virtual Tissue Simulation" 
   We present an overview, initial progress, and plans for Open Virtual Tissues (OpenVT), a NSF-funded project that seeks to improve the ecosystem of multicellular modeling frameworks. There exist several such frameworks, covering different fundamental modeling approaches, programming languages, model specification formats, workflows, varying degrees of user support, etc. This presents challenges to new modelers who want  to understand what these frameworks can do (or can't do) and also to experienced modelers who would like to "reproduce" model results using more than one framework. Indiana University leads two of the frameworks, CompuCell3D and PhysiCell (PIs Glazier and Macklin), providing a hub of initial activity: seeking shared concepts, modules, data formats/standards, and reproducibility. However, the project also includes other modeling frameworks as we want this to be a community effort. The OpenVT project kicked off with an intensive NSF I-Corps program which we will also briefly summarize. 
   YouTube and Slides.

April 18, 2024 Meeting

1. James Sluka, Indiana University, will discuss: Predicting disease with ‘broken’ models, a work in progress. 
   The development of computational models for biological processes often focuses on specific target states, such as normal and disease states. However, these models may have the capacity to represent additional states and outcomes. Indeed, the model may be able to represent multiple normal and disease states including those that the modeler did not develop their model for. Researchers often explore parameter space extensively, generating thousands of results. While numerical outputs can be clustered and classified using various approaches, simulations that primarily produce spatial models, such as 2D models, pose a different challenge. In such cases, results can be clustered based on user-defined metrics, like cell count or morphological characteristics. An alternative approach involves using modern image clustering tools based on artificial intelligence (AI) and machine learning (ML) algorithms. Here, I discuss the use of VGG16, a pretrained image classification AI/ML model, to recode simulation snapshots from a cell sorting Potts model. The recoded images, typically represented as short vectors, can be further simplified (e.g., using principal component analysis, PCA) and clustered (e.g., using hierarchical clustering) into sets of images. This approach provides an unbiased method for grouping a wide range of simulation results, especially when the simulation output primarily consists of images. We will compare this AI/ML-based clustering approach with clustering based on user-defined metrics for cell sorting. 
   YouTube and Slides. (Note: The PowerPoint slides work best if you download a copy to your computer instead of viewing in a browser.)

April 4, 2024 Meeting

1. Rebecca Morrison, Univ Colorado. "Highly reduced models of random ODEs: A framework, some examples, and analysis" 
   Consider a modeler's job when tasked to describe a system of interacting species, such as compartmental models in epidemiology: in many cases, interaction coefficients are unknown, relevant data are unavailable, inclusion of all possible species is computationally infeasible, etc. Thus, the modeler will have to model the system with just a small subset of the active species. This would seem to make the modeler's job extremely difficult, or even perhaps a lost cause. Surprisingly, though, we have examined several cases of random nonlinear ordinary differential equations where this type of extreme reduction---along with a simple, data-driven, embedded enrichment operator---still results in descriptive and predictive models. In this talk, I'll review a framework for calibration and validation of these coupled systems, show a few specific numerical examples, and give some analysis that supports such reductions. 
   YouTube and Slides.
   • Morrison, R. E., & Cunha, A. (2020). Embedded model discrepancy: A case study of Zika modeling. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(5).
   • Bandy, R., and R. Morrison. Stochastic model corrections for reduced Lotka–Volterra models exhibiting mutual, commensal, competitive, and predatory interactions. Chaos: An Interdisciplinary Journal of Nonlinear Science 34, no. 1 (2024).
   • Bandy, R., & Morrison, R. (2023, June). Quantifying model form uncertainty in spring-mass-damper systems. In Society for Experimental Mechanics Annual Conference and Exposition (pp. 9-19). Cham: Springer Nature Switzerland.
   • E. Basor, R. Morrison, Analytic solutions to nonlinear ODEs via spectral power series. Oct. 2023. https://arxiv.org/abs/2401.02401

March 21, 2024 Meeting

1. Hugo Geerts, Certara. 
   "The Neuroplatform: Towards a comprehensive mechanistic model of Alzheimer's disease for supporting drug discovery and development" 
   The success of amyloid antibodies has generated a lot of excitement in the Alzheimer (AD) community. Twenty years after the approval of the symptomatic treatments, patients and health care providers are looking forward to a new generation of disease modifying treatment. However, there is still a long way to go. To support these efforts, we developed a comprehensive mechanistic Quantitative Systems Pharmacology of the different major pathologies in AD; (amyloid, tau and neuroinflammation) combined with a computational neuroscience model that predict efficacy on a clinically relevant scale. The amyloid aggregation model, coupled with a Physiology-Based Pharmacokinetic (PBPK) model for drug exposure in the human brain demonstrated that lecanemab or Lequembi was sufficiently different from the previous failed antibodies both in efficacy and side-effects. The model models the cross-talk between amyloid and tau and is currently being used for supporting clinical decision in real world situations with readouts both in biomarker change and functional clinical scales. The progression of misfolded tau proteins is modeled using both a traditional ODE approach as well as a spatial Mont-Carlo approach that takes into account realistic geometry of the brain synapse and provides a reasonable biological explanation for the failure of many anti-tau antibodies. To account for neuroinflammation targets such as TREM2, the amyloid model is coupled with an extensive microglia model allowing to simulate intracellular pathways and phenotype switching. Finally, this all comes together in a computational neuroscience model, where biophysically realistic action potentials in relevant neuronal networks are simulated and drive cognitive outcomes. Amyloid, tau pathology and microglia derived cytokines affect different voltage-gated and ligand gated channels, the effect of which is calibrated using clinical data of successful and negative trials with amyloid antibodies. This allows for generating virtual twin profiles with specific comedications and common genotype variants, ultimately reducing variability in clinical trial readouts. 
   Hugo Geerts is Head Neuroscience Applied Biosimulation at Certara. His studies included a physics degree, a Biophysics PhD, a Bachelor Degree in Medicine, a Master in Pharmaceutical Sciences, and a Pharma Executive MBA. He worked for 17 years with Dr. Paul Janssen, the greatest drug hunter in history at the Janssen Research Foundation heading the Alzheimer Discovery research. He identified galantamine (Razadyne-Reminyl) for inlicensing and supported its successful clinical development. His team was one of the first in industry to initiate R&D projects focused on tau pathology for Alzheimer’s Disease and to publish on transgene mouse models with tau pathology. In 2002, he co-founded In Silico Biosciences (ISB) to provide mechanistic disease modeling services in CNS R&D, combining his degrees in physics and medicine with the discipline of computational neurosciences into a comprehensive Quantitative Systems Pharmacology approach for Alzheimer’s, Parkinson’s disease and schizophrenia. He has published over 120 papers and is an inventor on multiple patents related to modeling. The acquisition of ISB by Certara combined state-of-the-art PBPK modeling with mechanistic simulation. 
   YouTube and Slides.

March 7, 2024 Meeting

1. Belinda Akpa, University of Tennessee, Knoxville. 
   "Every model has its place: combining systems, data-driven, and generative models for therapeutic design" 
   Drug discovery is a molecular search task with a challenging objective: modify the function of a complex biological system to interrupt disease processes. Conventionally, it is a costly, high failure-rate process – with molecular candidates clearing preclinical safety and efficacy hurdles only to fail upon delivery to humans. This happens partly because early screens in the discovery pipeline fall short of capturing the ultimate therapeutic value of new molecular candidates. For a molecule to become a successful drug, it should: (1) bind to a desired target protein; (2) be deliverable from a desired site of administration (oral, intravenous, etc.) to the physiological site of activity, with sufficient concentration for a sufficient duration of time; and (3) promote the desired pharmacological effect without causing unwanted toxicity. The chemical space that meets one of these objectives likely requires compromises in another, as binding, delivery, and activity depend on coupled and dynamic biophysical and biochemical interactions. To help improve the success rate of drug discovery, we should ideally look at design through the lens of human physiology. In this presentation, I will discuss our work on integrating mechanistic systems models with data-driven machine learning and generative AI models to empower physiology-informed design of potential therapeutics.

    
   YouTube and Slides.

February 22, 2024 Meeting

1. Lourens Veen, Netherlands eScience Center. “Coupled simulations in theory and practice.” 
   Simulations are used widely in engineering and in the related engineering sciences, but computational approaches are increasingly prevalent in other fields as well. Global challenges like climate change and the energy transition, land use change and biodiversity loss, and keeping an aging and strongly interconnected population healthy lead to scientists being asked not just to understand the complex systems underlying these changes, but to give concrete advice on how to steer these systems towards a better future. A key technical challenge in building the complex simulation models required to do this, is how to combine simulations of different but interacting processes. Modelling the interactions is system-specific and part of creating the model, but typically doesn't solve the question of what information to communicate when from where to where when it comes time to implement the simulation. 
       In this presentation I will introduce the Multiscale Modelling and Simulation Framework (MMSF), a theory of (multiscale) model coupling that explains how different coupling patterns can be used to connect submodels simulating processes across domains and spatiotemporal scales. I will show examples of different kinds of couplings from applications in different fields, and present MUSCLE3, a model coupling tool that implements the MMSF in an easy-to-use software package that can run simulations locally and on High-Performance Computing machines. 
       Lourens Veen is a Senior Research Software Engineer at the Netherlands eScience Center. Originally a computer scientist, he now mostly works in various branches of computational science. He is interested in coupled simulations, validation, verification and uncertainty quantification (VVUQ), and accessible high-performance computing. 
   https://doi.org/10.1007/978-3-030-50433-5_33 
   https://muscle3.readthedocs.io 
   YouTube and Slides.

January 25, 2024 Meeting

1. Adam Halasz, West Virginia University. Effect of Spatial Inhomogeneities on the Membrane Surface on Receptor Dimerization and Signal Initiation in the context of the paper: "Spatial Stochastic Model of the Pre-B Cell Receptor". We employed spatial stochastic simulations to examine how plasma membrane inhomogeneities impact receptor dimerization and phosphorylation, in the context of tonic signaling by pre-B cell receptors. This approach was developed and validated using data from single-particle tracking experiments. I will focus on the computational methodologies applied in our study. Specifically, we utilized a rule-based framework to capture the complex interactions and potential oligomer formations of pre-B cell receptors, Lyn, and Syk. Our simulations were spatially resolved, integrating domain shapes from experimental data to accurately simulate molecular aggregate diffusion and collisions. This methodology enabled us to reveal key insights into receptor behavior, underscoring the importance of membrane domain organization in signaling processes. 
   Kerketta et al., IEEE / ACM Trans. Comput. Biol. Bioinf. 20 (1), 683-693 (2023).  
   https://doi.ieeecomputersociety.org/10.1109/TCBB.2022.3166149

    

     YouTube and Slides.

January 11, 2024 Meeting

1. Jake Beal, Raytheon BBN Technologies, “Agile Data Curation for Modeling and Design.” Investigating complex biological systems requires integrating many different types of information in an iterative process of design and experimentation. In synthetic biology, this has often been enunciated as a “Design-Build-Test-Learn” (DBTL) loop, but the current reality typically falls far from its promise. One key reason for this is the field’s lack of effective established practices for curation, quality control, and integration across designs, experiment plans, metadata, and data. 
       Software engineering has historically had analogous challenges relating to testing, documentation, and integration. Over the past two decades, however, the agile software community has radically transformed professional software development by developing processes that bring management of correctness, completeness, and compatibility into the core activities of software development and supporting them with complementary automation tools. We observe that, with appropriate choices of representation and process controls, the same processes and tools can be directly applied to synthetic biology designs, data, metadata, and models. 
       Early application of this approach have given promising results, we illustrate with three examples: collective development of genetic designs for the iGEM 2022 distribution, model-driven analysis of tunable CRISPR safety switch architectures, and automation-assisted analysis of flow cytometry experiments. 
   Bio: 
   Dr. Jacob Beal is an Engineering Fellow at Raytheon BBN Technologies. His work in synthetic biology includes development of standards for representation and communication of biological designs and experiments, signature-based detection of controlled pathogens, methods for calibrated flow cytometry, precision analysis and design of genetic regulatory networks, and engineering of biological information processing devices. 
   YouTube and Slides.

December 14, 2023 Meeting

1. Tongli Zhang, University of Cincinnati. “Applying digital twins to unleash ML&AI for solving biomedical challenges.” As machine learning and artificial intelligence (ML&AI) show great promise in addressing biomedical challenges, their current application remains just the tip of the iceberg, constrained by unique challenges inherent to biomedical systems. In this talk, I propose a hypothesis: realistic digital twins could effectively address many of these challenges and unlock the full potential of ML&AI. I hope this discussion sparks an open dialogue on integrating ML&AI with mechanistic modeling and digital twin development, and exploring strategies to best assist patients in need. 
   YouTube and Slides.

November 30, 2023 Meeting

1. Doug Chung, Certara. Predicting Disease Activity Scores in Inflammatory Bowel Disease Using Quantitative Systems Pharmacology. Quantitative Systems Pharmacology (QSP) models are typically used to guide dosing regimens based on the relationship between the drug PK/PD and disease-specific biomarkers. Using elements of AI/ML, our QSP Inflammatory Bowel Disease (IBD) model can predict clinical endpoints enabling clinical trial simulations of untested dosing regimens, combination therapies, and trial designs. In this example, our disease activity score classifier can predict the Crohn's Disease Activity Index (CDAI) and Mayo Score for virtual populations matched to published clinical trials (CODEX). The mechanistic, multistate model simulates the immune cell and cytokine dynamics of the inflammatory immune response in the lamina propria of inflamed, active IBD. Together with our collaborators, the QSP IBD model has guided drug development from target validation to Phase II dosing to reach the right target at the right dose with the right action. 
   YouTube and Slides.

November 2, 2023 Meeting

1. Liesbet Geris, Uni Liege and KU Leuven will discuss: EDITH: building an ecosystem for realizing the integrated virtual human twin. The use of digital twins in healthcare (DTH) is rapidly increasing. One application area is the personalisation of medical care, where DTH can take the shape of in silico models of organs and organ systems used to test various treatment options, to customise therapy or plan surgery. In the context of the development of medical therapies, they can be used as a tool throughout the entire R&D process to identify knowledge gaps and flaws, obtain a holistic and better understanding of a patient’s disease, design novel strategies, optimise therapies, optimise therapy production, increase safety (by providing additional scrutiny) and shorten the time to market. DTH can be used to improve healthcare organisations by driving efficiency, optimising operational performance and enhancing both patient and caregiver experience. Current solutions labelled as DTH are mostly single-scale, single-organ, single disease systems simply because going beyond this is still too complicated and time consuming, and hence prone to be neither realistic nor reliable. However, the human body is highly entangled: events occurring at one anatomical location at a given time may influence processes occurring at different locations and at different times. Therefore, the number of clinically relevant questions answerable with single-scale, single-organ, single disease models is relatively limited. Integrating several scales and levels of organisations generates huge challenges to modelling. To accelerate the adoption of an integrated Virtual Human Twin, it must first become easier to develop them, even when they need to be multi-scale, multi-organ, and multi-disease. The challenges related to developing a Virtual Human Twin call are too substantial to be handled by any one research group or even collaborative research project. They call for an ecosystem approach. The European Commission’s Coordination and Support Action EDITH (101083771) has as its objective to foster such an ecosystem and develop a roadmap towards the integrated Virtual Human Twin. The first step is an extensive mapping of relevant actors and initiatives, available resources (models, data sets, methods), infrastructures, DT-based solutions and services, as well as detecting technical and non-technical barriers to the uptake of DTH. This will allow to focus on the creation of a functional ecosystem bringing together all relevant stakeholders, including solution developers in academia and industry, technology/resource providers, end-users (particularly healthcare professionals and patients), regulatory agencies and HTA bodies. Leveraging the budding ecosystem, the consortium is working on a roadmap for accelerating the uptake of the DTH-based solutions and their further integration. The roadmap will contain a blueprint of the Virtual Human Twin and will identify the required (technical) developments, including but not limited to interoperability, computability, and integration of health models & data. The previously identified stakeholder needs and implementation barriers will be addressed. Additionally, an analysis of areas of applicability will be conducted, targeting especially applications representing high unmet medical needs and/or high societal benefits or clinical values. Finally, instruments such as funding, policies, standards, and specific recommendations will be specified for short and mid-term, taking into account the current legal, ethical, social and regulatory framework and country-specificities. After going through extensive review by the ecosystem, the first draft of the roadmap has been released and will be further elaborated through community activities in the coming year. 
       * First draft of the roadmap: https://zenodo.org/records/8200955 
       * Find out more about EDITH on the official website: https://www.edith-csa.eu 
       * Call for resources for the VHT catalogue/repository: https://www.edith-csa.eu/call-for-use-cases/ 
       * Contact form to sign up for newsletter, meeting invites etc: https://www.edith-csa.eu/contact 
   YouTube and Slides.

October 19, 2023 Meeting

1. Bruce Y Lee, City University of New York. “Multi-scale Modeling from the 2009 H1N1 Influenza Pandemic through the COVID-19 Pandemic: A Retrospective for the Future” Over the past two decades, our PHICOR team has developed and used multi-scale mathematical and computational models to help pandemic-related decision-making at the local, state, national, and global levels. This presentation will cover some examples of this work starting with when our team members were embedded in the U.S. Health and Human Services (HHS) to help with the national response to the 2009 H1N1 flu pandemic to the present. This will include insights generated from the modeling and the potential for the future. 
      PHICOR's website 
       Bruce's website 
       Bruce's Forbes page 
       Bruce's Psychology Today page 
       Bruce's Minded by Science page 
   YouTube and Slides.

October 5, 2023 Meeting

1. Lorenzo Veschini, King's College London. “Advancing Personalized Cancer Diagnosis, Prognosis and Therapy with AI: Unraveling Tumor Morphology, Microenvironments, and Sharing Knowledge”. This seminar will address recent advances in digital/computational histopathology which harnesses the power of artificial intelligence (AI) aiming to expedite and enhance cancer diagnosis, prognosis, and clinical decision-making to the benefit of patients. Additionally, it explores the transformative potential of digital/computational histopathology techniques as effective educational tools for students at every level. Lastly, the seminar outlines a path to translate high-content histopathology data into better mechanistic understanding of cancer microenvironments, with a perspective towards developing digital Virtual Cancer Tissue (VTC) models, and paving the way for personalized Digital Twins of Cancer.  YouTube and Slides
   • Multifactorial estimation of clinical outcome in HPV-associated oropharyngeal squamous cell carcinoma via automated image analysis of routine diagnostic H&E slides and neural network modelling https://pubmed.ncbi.nlm.nih.gov/37098302/
   • Continuously perfusable, customisable, and matrix-free vasculature on a chip platform https://pubmed.ncbi.nlm.nih.gov/36722906/
   • High-content image analysis to study phenotypic heterogeneity in endothelial cell monolayers https://pubmed.ncbi.nlm.nih.gov/34982151/
   • The impact of intensity-modulated radiation treatment on dento-alveolar microvasculature in pharyngeal cancer implant patients https://pubmed.ncbi.nlm.nih.gov/32841377/

September 21, 2023 Meeting

1. François Graner, University Paris Diderot, Evidence-based investigation of SARS-CoV-2 proximal origin. We will argue that the question of the SARS-CoV-2 proximal origin should be discussed widely within the scientific community, and not entirely given up to general media. In absence of proofs, it is useful to propose refutable formulations of scientific questions, separate facts from opinions, critically examine hints. Possible evidence-based investigations, including those based on computational modeling (epidemiological reconstruction, modeling evolution of viral genomes), will be presented. The importance of the ongoing debate, its current status, and the reaction of the scientific community will be briefly discussed. 
   https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)02019-5/fulltext 
   https://www.piecesetmaindoeuvre.com/IMG/pdf/graner_viruses_and_search_en.pdf 
   YouTube and Slides.

September 7, 2023 Meeting

1. Yu Feng, School of Chemical Engineering, Oklahoma State University. “Predicting Health Endpoints for Respirable Aerosols using Multiscale CFPD-PBPK/TK/PD Virtual Human Model”. The interaction between respirable aerosolized particulate matter (e.g., aerosolized medications for inhalation therapy and airborne transmissible viruses) and the human respiratory system is a complex process that spans multiple spatial and temporal scales. This presentation will introduce the research efforts done by Dr. Yu Feng’s lab on the developments in the coupling of computational fluid-particle dynamics (CFPD) simulations with Physiologically Based Pharmacokinetic/Toxicokinetic/Pharmacodynamic (PBPK/TK/PD) models to capture the journey of aerosols from emission to lung transport/deposition, systemic translocation, and potential body responses. A key aspect of this integration is using CFPD to predict the transport and deposition of inhaled particulate matter. This approach contributes to the PBPK/TK/PD model by accounting for patient-specific and disease-specific variabilities, enhancing its accuracy. The integrated framework offers a noninvasive and cost-effective tool for inhalation dosimetry, complementing traditional in vitro and in vivo experiments. The CFPD-PBPK/TK/PD modeling framework can provide a noninvasive, cost-effective, and time-saving inhalation dosimetry tool, which will significantly help accelerate the revolution in pulmonary healthcare and occupational exposure risk assessment, e.g., innovation in lung disease diagnosis, inhalation therapy, and toxic aerosol exposure risk mitigation. Three examples will be presented in this webinar: (1) Emission and Transmission: Predicting the airborne transmission of virus-laden droplets in various indoor environments between individuals. (2) Transport and Deposition in the Lung: Estimating the administered dose of inhaled aerosols using an elastic whole-lung model, and (3) After-Deposition Dynamics (Host-Cell Dynamics): Forecasting the transport, deposition, and immune responses triggered by inhaled Influenza A virus and nasal spray vaccine droplets. 
   YouTube and Slides.

August 24, 2023 Meeting

1. Gary An, Univ. Vermont, “Generating synthetic molecular time series data for ML and AI applications: Considerations” The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (or synthetic mediator trajectories, or SMT); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the Causal Hierarchy Theorem, which intrinsically limits the ability of data-centric methods to make statements about generative mechanisms that cross-scales (as is the case from cellular-molecular biology to an individual person’s state of health and disease). Alternatively, we present a rationale for using complex multi-scale mechanism-based simulation models, constructed and operated on to account for perpetual epistemic incompleteness and the need to provide maximal expansiveness in concordance with the Principle of Maximal Entropy. These procedures provide for the generation of SMT that minimizes the known shortcomings associated with neural network AI systems, namely overfitting and lack of generalizability. The generation of synthetic data that accounts for the identified factors of multi-dimensional time series data is an essential capability for the development of mediator-biomarker based AI forecasting systems, and therapeutic control development and optimization through systems like Drug Development Digital Twins. 
   https://www.frontiersin.org/articles/10.3389/fsysb.2023.1188009/full 
   YouTube, Slides and follow-up Q&A

July 20, 2023 Meeting

1. Dan Crowley (R Plowright Lab), Cornell, “Diet and defenses: low affinity antibodies in bats are affected by food quality.” Bats are reservoirs of many zoonotic viruses that are fatal in humans but do not cause disease in bats. Moreover, bats generate low neutralizing antibody titers in response to experimental infection, although more robust antibody responses have been observed in wild caught bats during times of food stress. Here we compare the antibody titers and B cell receptor (BCR) diversity of Jamaican fruit bats (Artibeus jamaicensis; JFBs) and BALB/c mice in response to T-dependent and T-independent antigens. We then manipulated the diet of JFB bats and infected them with H18N11 influenza A-like virus. Bats generated a weaker antibody response and possessed more BCR mRNA diversity compared to mice. However, withholding protein from JFBs enhanced serum antibody titers specific to H18 and reduced BCR mRNA diversity. Our results suggest that T cell help to B cells is dampened in bats resulting in low affinity antibodies, but this phenotype can be manipulated with dietary changes. 
   YouTube and Slides.

July 13, 2023 Meeting

1. Reinhard Laubenbacher and Anna Niarakis will report on the recent 3-week workshop: 'Building Immune Digital Twins' at  the Institut Pascal, Universite Paris-Saclay, France. 
   For more information visit the meeting's web site:
   • https://www.institut-pascal.universite-paris-saclay.fr/en/scientific-programs/building-immune-digital-twins
   • https://indico.ijclab.in2p3.fr/event/9017/
   • The September workshop: 
     Mechanistic and AI digital twins in personalized medicine - two sides of the same coin (full day) https://www.bc2.ch/tutorials-workshops
   • YouTube and Slides.

July 6, 2023 Meeting

1. Thomas Yankeelov, University of Texas. “MRI-based digital twins for predicting the response of breast cancer to therapy” Our lab is focused on developing tumor forecasting methods by integrating advanced imaging technologies with mathematical models to predict tumor growth and treatment response. In this presentation, we will discuss how quantitative magnetic resonance imaging data (MRI) can initialize and constrain mathematical models built on first-order effects related to proliferation, migration/invasion, vascular status, and drug-related treatment effects. More specifically, we will present some of our recent results through four vignettes focusing on breast cancer: 1) incorporating patient-specific data into mechanism-based mathematical models, 2) simulating outcomes via patient-specific digital twins, 3) rigorously guiding interventions through optimal control theory, and 4) updating interventions through data assimilation. The long-term goal is to provide a practical methodology that allows for optimizing therapeutic interventions on a patient-specific basis. 
   See also "Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology" https://pubs.aip.org/aip/bpr/article/3/2/021304/2835531. 
   A complete list of recent publications is available at https://cco.oden.utexas.edu/publications/ and you can follow the group on twitter at @UTCompOnco 
   YouTube and Slides.

June 22, 2023 Meeting

1. Job H. Berkhout and Harm J. Heusinkveld, RIVM, the Netherlands. Towards a virtual embryo: Computational modelling of neural tube closure defects. Closure of the caudal neural tube is a critical event that occurs early in development, around day 27 human gestation. Its failure underlies spina bifida and other neural tube defects (NTD), which are among the most prevalent human congenital malformations. Human and environmental safety assessment of chemicals relies primarily on legally required in vivo studies in pregnant animals. Ethical concerns, the need for greater chemical coverage whilst utilizing less resources, and growing insight in the limited predictability of animal models for human health, drive the need to find alternatives for animal experiments. However, the complexity of mammalian physiology often hampers one-on-one replacement of individual animal studies with inherently reductionistic in vitro assays. The ONTOX project proposes an approach from the perspective of human biology, physiology and toxicology, that takes an open view towards the knowledge that is needed to sufficiently cover all aspects necessary for chemical safety assessment. Within ONTOX, we focus on building and testing a computational multicellular agent-based model (ABM), based on a physiological map of human neural tube closure. This map was created using the systems biology tool CellDesigner and revolves around all-trans-retinoic acid (ATRA)-related molecular pathways for neural tube closure and disruption [Heusinkveld et al. (2021)]. Here, the morphogenetic events are recapitulated in the Compucell3D.org modeling environment by translating the physiological map into a dynamic ABM that depicts physical aspects of neurulation (neural fold elevation, folding and fusion). Model input parameters include different perturbation scenarios (eg, dosing the model with apoptosis-inducing compounds). Output parameters include effects of simulated chemical exposure on developmental processes critical for neural tube closure, such as median and dorsolateral hinge points formation. These are modeled as a function of key morphoregulatory signals and recapitulate NTDs known from in vivo vertebrate genetic models. By simulating a complex biological process such as the neural tube closure, we demonstrate that computational models of biological processes will provide a revolutionary approach to chemical safety assessment in the near future with less reliance on animal testing. This work is part of the the ONTOX project  (https://ontox-project.eu/). Disclaimer: this abstract does not necessarily reflect USEPA policy. 
   A paper on this work is available at: 
   Heusinkveld HJ, Staal YCM, Baker NC, Daston G, Knudsen TB, Piersma A. An ontology for developmental processes and toxicities of neural tube closure. Reprod Toxicol. 2021 Jan;99:160-167. doi: 10.1016/j.reprotox.2020.09.002. Epub 2020 Sep 11. PMID: 32926990; PMCID: PMC10083840.  
   https://doi.org/10.1016/j.reprotox.2020.09.002 
   YouTube and Slides.

June 8, 2023 Meeting

1. Megan Haase, University of Virginia. Muscle Regeneration Agent-Based Model Predicts Enhanced Regeneration Outcomes with Altered Cytokine Dynamics. Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to determine optimal treatments for muscle injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 200 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in-silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that a combined alteration of specific cytokine decay and diffusion parameters results in greater fibroblast and SSC proliferation and increased fiber recovery at 28 days as compared to the baseline condition. These results may guide development of therapeutic strategies that similarly alter muscle physiology during regeneration to enhance muscle recovery after injury. 
   YouTube and Slides.

May 11, 2023 Meeting

1. David Basanta, Moffitt Cancer Center. "Modeling the ecosystem in cancer with agent-based modeling." Multiscale modeling provides us with powerful tools to study various aspects of cancer. For instance, agent-based models (ABMs) allow us to use data at various scales (ie: molecular and cellular) to make predictions at a different scale (ie: tissue). Here I will introduce a variety of ABMs which we have developed to capture the tumor and the ecosystem it inhabits with the goal of understanding how cancers grow, change, take advantage of their environment and eventually adapt and evolve resistance to cancer treatments. 
   YouTube and Slides.
   • Frankenstein, Z., Basanta, D., Franco, O.E. et al. Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression. Nat Ecol Evol 4, 870–884 (2020). https://doi.org/10.1038/s41559-020-1157-y  (preprint is available here as a direct download)

April 27, 2023 Meeting

1. Claudio Monteiro, Novadiscovery. Jinkō modeling & simulation: an in silico trial simulation platform. During this talk, we will showcase how knowledge is leveraged directly within jinkō to help lower the cost and ethical constraints as well as achieve greater trial efficiency through the design and simulation of in silico trials. This will be illustrated with two practical use cases using nova's non small cell lung cancer (NSCLC) disease model of epidermal growth factor receptor (EGFR) mutated lung adenocarcinoma (LUAD). First, by exploring a large range of doses and then by testing eligibility criteria refinement and its impact on treatment effectiveness. 
   YouTube, there were no slides since this was a live demo. You can try Jinkō at https://www.novadiscovery.com/try-jinko/.
   1. Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma. Palgen, JL., Perrillat-Mercerot, A., Ceres, N. et al. Acta Biotheor 70, 19 (2022). https://doi.org/10.1007/s10441-022-09445-3
   2. Knowledge-based mechanistic modeling accurately predicts disease progression with genitib in EGFR-mutant lung adenocarcinoma. Adèle L'Hostis et al.  2023. https://doi.org/10.21203/rs.3.rs-2405647/v1 (preprint)

April 13, 2023 Meeting

1. Mikael Benson, lead of the Digital Twins Group @Karolinska Institute, Stockholm. The Swedish Digital Twins initiative. The overall aim is to develop digital twins of individual patients for predictive preventive and personalized medicine. The twins are constructed based on integrating routine clinical and genome-wide data down to the single cell level. Each twin is computation are treated with thousands of drugs to find the drug that is optimal for the patient. The ultimate aim is to construct dynamic digital twins that show health and disease processes, for predictive and preventive medicine. Publications and a recorded talk at Harvard can be found at our website: sdtc.se. 
   YouTube and Slides.

March 30, 2023 Meeting

1. Julio Saez-Rodriguez & Daniel Dimitrov, Uni Heidelberg. "Knowledge-based machine learning to extract molecular mechanisms from single-cell and spatial multi-omics." Single-cell and spatially resolved omics technologies provide unique opportunities to the key study intra- and inter-cellular processes that drive immunological systems and their deregulation in disease. The use of prior biological knowledge allows us to reduce the dimensionality and increase the interpretability of the data, in particular by extracting from the data features describing the activity of molecular processes such as signaling pathways, gene regulatory networks, and cell-cell communication events. In this talk, I will present resources and methods from our group that combine multi-omic single cell and spatial data with biological knowledge and illustrate them on medically relevant cases. 
   YouTube and Slides.

March 16, 2023 Meeting

1. Nana Owusu-Boaitey, Case Western Reserve University. "Modeling seroreversion for assessing SARS-CoV-2 seroprevalence". Serology represents an important tool for determining prevalence and thus metrics such as fatality risk following infection. Seroreversion, or waning of antibody levels with time, can bias longitudinal assessments of prevalence. Though prior research examined seroreversion trends for particular immunoassays targeting antibodies against SARS-CoV-2, the viral cause of the COVID-19 pandemic, we know of no systematic determination of how much assay characteristics contribute to seroreversion risk. We present data quantifying the contribution of antigen target and assay design to seroreversion following an initial SARS-CoV-2 infection; this enables assay-specific seroreversion modeling for assays not included in our analysis but for which antigen target and assay design are known. We then use our modeled adjustments to account for seroreversion in serology-based longitudinal assessments of hospitalization and fatality risk following SARS-CoV-2 infection, shielding of the elderly from infection, and prevalence in children. We hope this framework can provide guidance in seroreversion modeling for future pathogen outbreaks. 
   A draft of Dr. Owusu-Boaitey's accepted Eurosurveillance paper is here: 
   https://www.medrxiv.org/content/10.1101/2022.09.08.22279731v3.full-text 
   YouTube and Slides.

March 9, 2023 Meeting

1. Shantanu Gupta from USP, Brazil. "Network analysis reveals tumor suppressor lncRNA GAS5 acts as a double-edged sword in response to DNA damage in gastric cancer." The lncRNA GAS5 acts as a tumor suppressor and is downregulated in gastric cancer (GC). In contrast, E2F1, an important transcription factor and tumor promoter, directly inhibits miR-34c expression in GC cell lines. Furthermore, in the corresponding GC cell lines, lncRNA GAS5 directly targets E2F1. However, lncRNA GAS5 and miR-34c remain to be studied in conjunction with GC. Here, we present a dynamic Boolean network to classify gene regulation between these two non-coding RNAs (ncRNAs) in GC. This is the first study to show that lncRNA GAS5 can positively regulate miR-34c in GC through a previously unknown molecular pathway coupling lncRNA/miRNA. We compared our network to several in-vivo/in-vitro experiments and obtained an excellent agreement. We revealed that lncRNA GAS5 regulates miR-34c by targeting E2F1. Additionally, we found that lncRNA GAS5, independently of p53, inhibits GC proliferation through the ATM/p38 MAPK signaling pathway. Accordingly, our results support that E2F1 is an engaging target of drug development in tumor growth and aggressive proliferation of GC, and favorable results can be achieved through tumor suppressor lncRNA GAS5/miR-34c axis in GC. Thus, our findings unlock a new avenue for GC treatment in response to DNA damage by these ncRNAs. 
   https://doi.org/10.1038/s41598-022-08900-y 
   YouTube and Slides.

March 2, 2023 Meeting

1. Maria Abou Chakra, University of Toronto. Virtual Human Development: simulating early development is an international effort. The Virtual Human Development Consortium bridges multidisciplinary, international experts with the goal of creating a computer-based simulator of human embryonic development. The simulator will have the ability to predict the outcome of cellular systems, allowing for computational experimentation with stem cells and human embryos without sacrificing living materials. Such a simulator will be the key tool to catalyze rapid rounds of computational modeling in the biotechnology sector, fueling the design-build-test cycle for lab-grown cells and tissues. We believe that the impact of the simulator will be analogous to that of established simulation platforms for physics and chemistry, which have facilitated the rapid growth of complex engineered systems, such as computing platforms. It will serve as a tool for simulating engineered developmental systems, leading to robust bioprocesses for manufacturing designer cells, tissues, and organs on-demand. 
   YouTube and slides.

February 16, 2023 Meeting

1. Pras Pathmanathan, US FDA, Credibility Assessment of Patient-Specific Computational Models. Patient-specific models (PSMs) are computational models that have been personalized to represent individual patients and are closely related to the concept of the Digital Twin. While there has been much recent efforts on credibility assessment of models for medical devices, these efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. This talk will overview our recent publication that was the first, to our knowledge, focused on the topic of credibility of PSMs. Using cardiac modeling as an exemplar field, we consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs, and then perform mesh resolution and uncertainty quantification studies using PSMs. Findings were then used to consider how PSMs can be evaluated using the approach of the ASME V&V40 Standard. 
   Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010541 
   YouTube and Slides.

February 1, 2023 Meeting

1. Open Discussion of National Academies’ online workshop: “Opportunities and Challenges for Digital Twins in Biomedical Sciences” that occurred January 30, 2023. Viral Pandemics WG members James Glazier, Reinhard Laubenbacher, and Gary An were members of panels. The recording of the full workshop is posted at the workshop event page. 
   YouTube

January 19, 2023 Meeting

1. Wayne Koff. The Human Immunome Project.  
   The recent convergence of technological advances across human immunology, systems biology, artificial intelligence (AI) and machine learning offers an unprecedented opportunity to unravel the immense complexity of the human immune system and transform the future of human health. On 27-29 September 2022, 65 leading scientists from the biomedical and computing fields met in La Jolla, California to discuss plans for a Human Immunome Project with the mission of creating AI models of the human immune system. https://www.humanimmunomeproject.org/ 
   YouTube and Slides.

December 8, 2022 Meeting

1. James A. Glazier, Indiana University, Bloomington and Tomas Helikar, University of Nebraska, Lincoln: 
   MSMViral: Charting a Path to Immune Digital Twins and Pandemic Preparedness. 
   Since its inception in Fall 2020, MSMViral has built a lively transdisciplinary community of modelers, experimentalists, clinicians, technologists, regulators, journalists, and others. Organized around our weekly virtual seminar series and subgroup meetings, MSMViral has jump-started numerous interdisciplinary collaborations that have led to more than 35 scientific articles, opinion pieces and editorials. The YouTube recordings of the seminars are a publicly available resource of over 130 talks on all aspects of infection and immune response. Two ongoing subgroups on immune response modeling and immune digital twins have flourished and led to funding for workshops in Florida and Paris and a major Center for Immune Digital Twins at the University of Nebraska Lincoln. To go further we need to move forward beyond our current volunteer effort. We have a critical need for large-scale collaboration to take immune modeling from research to clinical deployment. To further this effort, we are establishing a Global Alliance for Immune Prediction and Intervention. We welcome your discussion of past achievements, present activities and short term plans and suggestions for the most practical way for MSMViral and GLIMP to contribute to the future of immune prediction and regulation and pandemic preparedness. 
   YouTube and Slides and IMAG/MSM Presentation page.

December 1, 2022 Meeting

1. Baylor Fain, Texas Christian University. Your computer may be more powerful than you know: An Example of a GPU accelerated agent-based model in Virology. Here an agent-based model (ABM) model that utilizes graphical processing units (GPUs) is developed for simulating viral infections of influenza in a mono layer of a million MDCK cells. The speed increase from utilizing GPUs is shown and an example of fitting the model to H1N1pdm09-WT in vitro data is given. Then the feasibility of simulations in a doctor's office is discussed, based on growing GPU power and computer availability. Finally, future research that stems from this work is commented on. 
   YouTube and Slides.

November 17, 2022 Meeting

1. Claudio Monteiro Novadiscovery. Jinkō knowledge: the knowledge management module of the jinkō modeling platform. Jinkō's knowledge management feature allows scientific knowledge to be extracted from trusted sources to create a complete, fully transparent "knowledge-model". Scientific extracts and claims are expertly curated and scored by scientists, in a systematic and collective fashion, ensuring the knowledge underpinning any model is as robust and comprehensive as possible. These extracts and claims are then added to a fully collaborative document editor, alongside the global description of the model and its mathematical equations. This creates structured and transparent documentation, all the way to the scientific sources, including comments from internal and external experts involved in the research. 
   YouTube and Slides.

November 3, 2022 Meeting

1. Russell Schwartz, Carnegie Mellon University. Modeling and inferring somatic evolution in cell lineages. This talk will examine computational tools for study of somatic mutability, with application particularly to cancer progression.  We will first examine some of the background on somatic evolution and the progress of computational tools for reconstructing it from various forms of genetic variation data, with particular focus on the rise of methods for multiomic data sources.  We will then consider how these reconstructions contribute to a growing understanding of variability in somatic mutability and its connection to disease progression.  Finally, we will examine how simulation and simulation-based optimization can be used to better inform the problem of study design to better resolve variations in somatic mutability within and between organisms.  We will close with consideration of ongoing open problems and potential next steps. 
   YouTube and Slides.

October 27, 2022 Meeting

1. No Meeting.

October 20, 2022 Meeting

1. No Meeting.

October 13, 2022 Meeting

1. Michael Yeaman & Aaron Meyer, UCLA. Immunobiology and Tensor Modeling of Persistent MRSA Bacteremia. Bacteremia due to methicillin-resistant Staphylococcus aureus (MRSA) is a relatively common life-threatening infection.  Yet, up to 30% of cases fail to resolve in a timely manner despite appropriate therapy using antibiotics to which the isolate is susceptible in vitro.  Such infections are termed persistent MRSA bacteremia (PB), contrasting with resolving MRSA bacteremia (RB).  Our recent studies have examined independent factors shaping PB vs. RB outcomes, uncovering genetic, epigenetic and immunological correlates.  However, the integration of distinct types of experimental data to uncover potential multi-dimensional correlates of PB vs. RB has been relatively unexplored.  To address this knowledge gap, we developed a tensor-based strategy for computational modeling of diverse data to seek new insights into PB vs. RB outcomes.  Results suggest immunological pathways and targets involved in PB outcomes that may not have been detected using any single dataset alone.  Such parallax in understanding host–pathogen dynamics driving disease outcomes holds promise for innovative anti-infective and vaccine strategies to meet the challenge of MRSA infection and beyond.   
   YouTube and Slides.

October 6, 2022 Meeting

1. Liane Dos Santos Canas, King's College London. From early detection to disease profiling: How can machine learning models detect early signs of COVID-19, and be used to profile the post-COVID syndrome? Self-reported symptoms during the SARS-CoV-2 pandemic have shown to be effective in training artificial intelligence (AI) models to identify possible foci of infections. Such models can be further used to early identify SARS-CoV-2 infected individuals, helping to contain the spread of the pandemic and efficiently allocate medical resources. Furthermore, self-reported symptom studies rapidly increased our understanding of SARS-CoV-2 during the pandemic and enabled the monitoring of long-term effects of COVID-19 outside the hospital setting.  It is now evident that post-COVID syndrome presents heterogeneous profiles, which need characterization to enable personalized care among the most affected survivors. In this talk, we will be firstly presenting our Hierarchical Gaussian Process model designed for the specific task of early detection of COVID-19, which outperforms the symptoms-based criteria considered in clinical practice for test referencing. Secondly, we will focus on the description and phenotyping of post-COVID symptom profiles, which we have achieved using unsupervised machine learning techniques. 
   YouTube and Slides.
   1. ExeTera code at GitHub
   2. Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study
   3. Profiling post-COVID syndrome across different variants of SARS-CoV-2

September 29, 2022 Meeting

No meeting.

September 22, 2022 Meeting

No meeting.

September 15, 2022 Meeting

1. Herbert M Sauro, U Washington, co-authors: Steve Wiley, PNNL; Michael Kochen, UW; Steve Andrews, UW. Dynamics and Sensitivity of Signaling Pathways. Signaling pathways are protein networks that respond to external signals that modulate cell behavior. This is particularly the case with mammalian cells, where communication is essential for individual cells to cooperate as a cohesive unit. Genetic mutations to one or more proteins in a signaling pathway is often one of the hallmarks of uncontrolled tumor growth. As a result, proteins found in signaling networks are often considered targets for therapeutic agents. However, whether a given protein is a suitable target depends on how it communicates perturbations to the rest of the network and, ultimately, the cell's outward phenotype. In this presentation, I will describe some existing and new results in understanding how the sensitivities in a signaling network are influenced by feedback loops and how such work can lead to a better understanding of the operational characteristics of signaling pathways. 
   YouTube and Slides.

September 8, 2022 Meeting

1. Paul D Thomas, U Southern California. GO Causal Activity Modeling framework and application to viral and host response processes. The Gene Ontology Causal Activity Modeling (GO-CAM) framework is a structured framework for linking multiple GO annotations into an integrated model of a biological system (https://www.nature.com/articles/s41588-019-0500-1). All elements of the models are represented using standard ontology terms and identifiers, and supported by evidence. Expert biocurators from the GO Consortium have developed a large number of models of biological systems, including almost 50 in the domain of viral processes and host immune response processes. An example can be found at: http://noctua.geneontology.org/workbench/noctua-visual-pathway-editor/?model_id=gomodel%3A5e72450500004019. I will present the GO-CAM framework, the Noctua tool for creating and editing GO-CAM models, and an example pathway, to explore potential collaboration opportunities in refining or creating new GO-CAM pathways for viral and host processes. 
   YouTube and Slides.

September 1, 2022 Meeting

Summer break, no meeting.

August 25, 2022 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

1. Carolyn Cho, Merck. Immunological Modeling: Bridging Gaps in Clinical Data. The potential impact of modeling in clinical research is more profound than in discovery where approaches to controlled experimental perturbation are available, in a relatively short period of time. In particular, questions during pharmaceutical development often arise in a timeframe that does not allow for data to be generated in a dedicated clinical trial. Clinical data are often generated from healthy volunteers, are observational rather than interventional, and undergo varied quality control. Yet these data are intended to inform predicting responses of interventions in patient populations. Two vignettes of modeling – one, data mining, the other mechanistic – to address pharmaceutical development questions will be discussed, to illustrate considerations for clinical data preparedness. 
   YouTube and Slides.

August 18, 2022 Meeting

Summer break, no meeting.

August 11, 2022 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Nancy Baker, Center for Computational Toxicology and Exposure US EPA. Using the PubMed Abstract Sifter for Computational Modeling of Complex Biological Processes. Modeling complex biological processes can require retrieving, organizing, and extracting entities and relationships from thousands of PubMed citations. The USEPA’s Center for Computational Toxicology and Exposure’s Virtual Tissue project team has developed an Excel-based literature mining tool called the PubMed Abstract Sifter that facilitates each literature task in complex biological modeling. We will demonstrate how the publicly available tool can be used to identify gene and protein participants in a biological process, to build out a network of gene relationships, and to identify the chemical stressors that can be used to perturb that network to produce adverse outcomes. DISCLAIMER: does not necessarily reflect Agency policy. 
   YouTube and Slides.

August 4, 2022 Meeting

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Presentations

1. Patrick Kinnunen, Chemical Engineering PhD Candidate, University of Michigan, Jennifer Linderman lab. Analysis of Heterogeneous, Single-cell Kinase Signaling Dynamics. Cell-to-cell signaling heterogeneity is an important component of healthy and diseased physiology. Signaling activity in individual cells can be measured using fluorescent reporters, which quantify the degree of activity in specific pathways. These reporters offer real-time, live-cell outputs, meaning that they can be used to measure dynamic activities, as opposed to endpoints. Because they function in living cells, they can be used in experiments where cells are subject to continuous perturbations to measure how responses to an early stimulus bias later responses. Nevertheless, there are numerous difficulties associated with analyzing these data. It’s not always clear how to extract features from time-series data, and analysis of periodic or stochastic time-series behavior requires different approaches than traditional signaling analysis. In this talk, I’ll discuss a variety of analysis techniques for heterogeneous, single-cell data gathered from fluorescent reporters. I’ll start by describing data acquisition, and then present a variety of methods with a range of complexities and constraints, including data-driven and mechanistic modeling. I’ll also highlight a range of applications from my own work and others, with particular focus on the analysis of single-cell signaling and immune interactions. Finally, I’ll connect analysis methods described here with analysis of heterogeneous multiscale modeling. 
   YouTube and Slides.

July 28, 2022 Meeting

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Presentations

1. Serkawt Khola, Evoplexusmedics. The European Health Data Space: a MedTech Perspective: Real-world health data holds a substantial potential for driving forward personalised, preventive and evidence-based healthcare. It will also support research and innovation, support disease prevention and the overall paradigm shift towards a holistic approach to healthcare. It also supports public authorities, regulators and industry to make healthcare practices, systems and products more sustainable with improved outcomes for patients and better use of resources.  The European Health Data Space (EHDS) initiative, by the European Commission, is an attempt to sets out rules, common standards, infrastructures and a governance framework for the use of electronic health data within the member states in the European Union for healthcare, research, innovation and policy making. There are, however, also bottlenecks that need to be further addressed, and the huge challenge to strike a balance between privacy, security, ethics, regulation, standards etc., while also aiming to extract the full potential of the European health data for primary and secondary use. Furthermore, the potential for a significant growth in the health data economy, from better access and exchange of health data in healthcare; and use for research, innovation and policy making, is also expected. In this webinar we will present the EHDS and some of its benefits as well as challenges, from a MedTech perspective. 
   DISCLAIMER: The information and views set out in this webinar are those of the author(s) and do not necessarily reflect the official opinion of the European Commission.  
   YouTube and slides.

July 21, 2022 Meeting

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Presentations

1. Nate Jacobs, Flashpub.io. Smaller, better, faster: atomizing research communication with micropublications: Scientific progress used to be defined by what was published. Today, how we publish has become almost as important to the progress of science. It affects who can read your work, who can afford to publish in which venues, how the content can be used, reused, and built upon, and even how scientific studies are structured and executed. Micropublishing is an innovation in publishing workflows that seeks to make publishing more reflective of how science actually progresses in real time. A micropublication is a single result, published rapidly in a format designed for reuse and integration with results emerging from other groups. While open, rapid, atomized communication solves many of the systemic challenges of scientific publishing (reproducibility, access and equity, efficiency, to name a few), it presents acute opportunity costs for individual researchers that need to advance their career and win grants. The flashpub.io team is building micropublishing workflows that focus on collaborative research data storytelling, integration with novel funding mechanisms, and effective incentive structures that empower researchers to advance their careers by actively leading their peers with rapid publications to share new results early and often. In this seminar learn more about how flashpub.io approaches these challenges and is helping scientist return to a purer, more rigorous and collaborative style of publishing scientific discoveries. 
   YouTube and slides.

July 14, 2022 Meeting

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Presentations

1. Thomas Knudsen, Developmental Systems Biologist, USEPA, Center for Computational Toxicology and Exposure. 
   Computational Model of Microglial Cell Dynamics During Morphogenesis of the Brain Microvasculature: 
   Biologically inspired multicellular systems models that are fully computable (eg, virtual embryo) can be used to titrate critical phenomena during tissue development, homeostasis, and disease; however, in silico reconstitution of a complex, self-organizing morphogenetic system from unidimensional data (embryogeny) remains a challenge. Microglial cells are essential building blocks of microvascular development (angiogenesis) and permeability (barriergenesis), and are often altered in neurodevelopmental disorders (e.g., RTT, FAS, ASD). To begin to unravel their complex roles in fetal brain homeostasis, a small working prototype of perineural vascular development was constructed in CompuCell3D.org to translate microglial function into consequences on emergent microvascular patterning. Our vision is to execute the models for predictive biology of neurodevelopmental disorders, including chemical exposures during pregnancy. 
   DISCLAIMER: does not necessarily reflect Agency policy. 
   YouTube and slides.
2. Kaitlyn Barham, ORAU Student Services Contractor, USEPA, Center for Computational Toxicology and Exposure. 
   Computational Model of Cell Fate Specification in a 3D Synthetic Gastruloid Simulator: 
   The embryonic body plan is ‘decoded’ during gastrulation, the hallmark of which is primitive streak formation in the epiblast. Using CompuCell3D.org, we modeled the human epiblast evolving a primitive streak through epithelial-mesenchymal transition of pluripotent stem cells and self-organizing endo-mesodermal progenitors through a network of morphogenetic signals (e.g., FGF, WNT, NODAL). Executing the simulation drives a synthetic HOX clock that patterns the emergence mesodermal cell fates (chordamesoderm, paraxial, lateral plate, extraembryonic). Synthetic perturbations introduced into the model simulate quantitative genetic and/or environmental influences on positional information to essentially ‘recode’ the mesodermal topography. This small working prototype model translates global or local perturbations to the system into mesodermal fate based on spatio-temporal colinearity of a synthetic HOX clock. 
   DISCLAIMER: does not necessarily reflect Agency policy.  
   YouTube and slides.

June 23, 2022 Meeting

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Presentations

1. Fiona MacFarlane, St Andrew University. A single-cell mathematical model of SARS-CoV-2 induced pyroptosis: Pyroptosis is an inflammatory mode of cell death that can contribute to the cytokine storm associated with severe cases of coronavirus disease 2019 (COVID-19). The formation of the NLRP3 inflammasome is central to pyroptosis, which may be induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Inflammasome formation, and by extension pyroptosis, may be inhibited by certain anti-inflammatory drugs. In this study, we present a single-cell mathematical model that captures the formation of the NLRP3 inflammasome, pyroptotic cell death and responses to anti-inflammatory intervention that hinder the formation of the NLRP3 inflammasome. Our results demonstrate that an anti-inflammatory drug can delay the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory (e.g., apoptosis). The single-cell model is further implemented at a multicellular level within a SARS-CoV-2 tissue simulator in collaboration with a multidisciplinary coalition investigating within host-dynamics of COVID-19. YouTube and Slides.

June 16, 2022 Meeting

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Presentations

1. Clare Bryant, Cambridge University. "Seeing and responding to danger: Detection of pathogen infection by a host." Once a pathogen infects the host the immune system has to sense it to try and control, then kill, the microorganism.  Pattern Recognition Receptors (PRRs) in innate immune cells, such as macrophages, detect microbes which drives inflammation to control the infection as well as facilitate the formation of an appropriate adaptive immune response.  These receptors also detect endogenous damage associated molecules which are released upon tissue injury.  Dysregulation of inflammatory signalling through PRRs is now thought to underpin many common diseases such as Alzheimer’s disease, cancer, diabetes and cardiovascular disease.  This makes PRRs excellent targets for developing new drugs against many diseases.  There are many differences across mammalian and other species in PRRs, compared to humans, and this may be important in understanding the basic biology of how a zoonotic pathogen is not detected in one species, but causes disease in humans. 
   YouTube and Slides.

June 9, 2022 Meeting

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Presentations

1. Laurence Calzone, Institute Curie, Paris. How Boolean models can be used to model heterogeneity in cancer studies: There are many challenges to address when modelling cancer evolution. Heterogeneity is one key aspect to consider and can be studied at the level of the patients (all patients have different molecular profiles) or/and at the level of the tumour (with the coexistence of multiple clones inside the tumour). All these differences can be enhanced depending on the status of the tumour microenvironment and how the tumour cells interact with it. I will show some examples of how these flavours of heterogeneity are treated in mathematical models using a stochastic Boolean formalism and how omics data can be integrated into these models to provide patient-specific models. 
   YouTube and Slides.

June 2, 2022 Meeting

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Presentations

1. Gary An, University of Vermont. What it will take to cross the Valley of Death: Translational Systems Biology, “True” Precision Medicine, Medical Digital Twins, Artificial Intelligence and In Silico Clinical trials: I assert that the greatest methodological challenge to improving human health is increasing the efficiency in translating the output of basic biomedical research into effective clinical therapeutics, specifically the translation from ostensibly effective drug candidates from pre-clinical studies to clinical application: the Valley of Death. This talk provides a background for the concept of Translational Systems Biology, the explicit use of mechanism-based dynamic computational models in clinically-relevant contexts,  and how this term is linked with and to a great degree encompasses subsequent developments in biomedical research, such as personalized/precision medicine, digital twins, biomedical application of machine learning/artificial intelligence and in silico trials. Translational Systems Biology and its manifestation through the design and execution of in silico clinical trials, has an explicit aim of representing and overcoming key features of clinical populations that form the basis of the Valley of Death. I present three Grand Challenges for the community that I believe are critical if we are to substantively enhance the ability to effectively and efficiently evaluate drug candidates (this includes both new therapeutic agents and drug repurposing for diseases in which they were not already tested) in a clinically relevant fashion. This talk will also emphasize the cognitive/mental/academic/political barriers that mechanism-based dynamic computational modeling, a fundamental tool that underpins nearly all advances in every other technological field other than biomedicine by allowing for the practice of engineering, faces in biomedicine, and how it is subject to the tyranny of terminology. 
   YouTube and Slides.

May 26, 2022 Meeting

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Presentations

1. James A Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University at Bloomington. Using CompuCell3D as a Platform to Construct Multicellular Virtual Tissues to Explore the Interactions between Infection, Host Tissues and Immune Response: Multiscale, multicellular Virtual Tissue models built using modeling frameworks like CompuCell3D are versatile tools for exploring the complex interactions between intracellular signaling and gene-regulatory networks, inter-cellular signaling through contact and diffusible signals, and force generation, cell migration and shape change. Several prior speakers have discussed a variety of models of infection, immune response, and drug treatment. Here, I focus on how CompuCell3D can simplify the construction of complex, extensible and reusable Virtual Tissue models. Interested members of the audience can follow along by downloading the software from (www.compucell3d.org) or run it on-line at (https://nanohub.org/resources/cc3dbase4x; usage is free but does require you to register in advance). I will also briefly discuss advances in CompuCell3D to enable it to be used in a Jupyter notebook environment and our new Mechanica software package. Dr. Glazier received his B.A. in Physics and Mathematics from Harvard University and his M.S. and Ph.D. in Physics from the University of Chicago. His research focuses on experimental and computational approaches to pattern formation in embryology. He has held faculty appointments at the University of Notre Dame and Indiana University, Bloomington, where he is founding director of the Biocomplexity Institute, Professor of Intelligent Systems Engineering and Adjunct Professor of Physics, Informatics and Biology. 
   YouTube and Slides.

May 19, 2022 Meeting

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Presentations

1. Qiang Zhang, Rollins School of Public Health, Emory University, Atlanta. Computational Modeling of Germinal Center Response: The germinal center response (GCR) is a critical event in adaptive humoral immunity involving activation and differentiation of B cells. Many environmental pollutants may disrupt the B cell response. GCR is a T cell-dependent process, involving cell-to-cell interactions with spatial complexity. The formation of GC requires activated B cells to migrate between the dark and light zones for multiple cycles, during which they undergo clonal expansion, somatic hypermutation, and apoptosis. This spatial behavior is crucial to the formation of long-lived plasma cells that secrete high-affinity immunoglobulins. The process is underpinned by a complex gene network responding to chemoattracting signals and cytokines from T cells and follicular dendritic cells (FDC). Here I present a mathematical model of GCR using the CompuCell3D platform. Tellurium is used to simulate the intracellular gene network. The model captures the key events in GCR, including B cell proliferation and somatic hypermutation in the dark zone, migration of B cells to the light zone where they interact with FDC and T cells to check for antibody affinity and make decisions for apoptosis, survival or returning to the dark zone. The migration of B cells within GC is driven by the gradients of chemoattractants CXCL12 and CXCL13, and the receptors CXCR4 and CXCR5, whose expression, along with cell proliferation, is regulated by the interaction with FDC and T cells. Our simulations predict the morphological and functional consequences of GCR when key events are disrupted, such as inhibition of B cell proliferation by environmental immunotoxicant polychlorinated dibenzo-p-dioxins. In summary, the virtual GCR model has the potential to be used to better understand the mechanism of GCR, its disruption and enhancement by environmental and pharmaceutical chemicals. YouTube and Slides.

May 12, 2022 Meeting

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Presentations

1. Joy Phillips, San Diego State University. Acetylcholine regulates inflammation and tissue repair during respiratory viral infection: Acetylcholine acts as a powerful anti-inflammatory agent in the brain/immune circuit known as the cholinergic anti-inflammatory pathway.  Acetylcholine binding to a7 nicotinic acetylcholine receptors on tissue macrophages decrease nuclear translocation of the critical transcription factor Nf-kB. This decreases ongoing production of inflammatory cytokines such as TNF, IL1b, and IL6.  In the spleen, acetylcholine production by CD4T cells is a critical protective factor in preventing lethal sepsis.  We explored the role of acetylcholine and cholinergic lymphocytes in the lungs during influenza, expecting that acetylcholine would be produced in response to the innate immune inflammatory burst.  Instead, we found that airway acetylcholine concentration increased between days 8-11 post-infection.  This coincided with the appearance of cholinergic CD4 T cells and conventional B cells. When acetylcholine production was inhibited, pulmonary inflammation was increased, and recovery was delayed.  Furthermore, the lungs showed evidence of aberrant tissue repair.  Conversely, increasing acetylcholine blocks influenza-associated morbidity.  These results indicate the unsuspected role of acetylcholine and cholinergic lymphocytes in regulating macrophage-mediated inflammation and pulmonary repair during recovery from respiratory viral infection. YouTube and Slides.

May 5, 2022 Meeting

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Presentations

1. Hayden Fennel, Indiana University. Creating educational materials for cloud-based tools: Aligning audience and learning objectives: Cloud-based simulation tools enable easy and accessible use of complicated computational software to students, instructors, and experienced researchers alike. However, developing effective instructional content to include alongside online simulations can pose both conceptual and technological challenges, as audiences of cloud-based simulation software often represent a wide variety of backgrounds and experience levels. This session will introduce theory and best practices for the development and deployment of meaningful supporting resources for online simulation tools. An overview method for developing computational learning activities will be discussed, along with suggestions for scalable, education-focused documentation strategies and other supports. Practical examples from current nanoBIO tools on nanoHUB will be provided. Attendees are encouraged to bring thoughts and/or questions about instructional content for their own simulation tools (current or prospective) for discussion. YouTube and Slides.

April 28, 2022 Meeting

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Presentations

1. Lucas Bottcher, Frankfurt School for finance & Management & UCLA. AI Pontryagin or: How Neural Networks Learn to Control Dynamical Systems: The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. In this talk, I will present AI Pontryagin, a versatile control framework based on neural ordinary differential equations that automatically learns control signals that steer high-dimensional dynamical systems towards a desired target state within a specified time interval. I will discuss examples that demonstrate the ability of AI Pontryagin to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. I will also discuss possible applications of AI Pontryagin in computational biology and medicine, where neural-network-based control frameworks can help solve a wide range of control and optimization problems, including those that are analytically intractable. YouTube and Slides.
2. David Gibbs, Institute for System Biology, Seattle. Patient specific cell-cell networks suggest important links in disease progression: Cell-cell communication is involved with regulating inflammation, promoting proliferation and differentiation, tissue repair, and to guide cell migration in the body. Abnormal cell-cell communication can cause disease, and in the opposite direction, diseases can alter communication. Cancer, once seen as a disease of genetics, is now recognized as being inexorably connected to the complex host of cellular interactions within the tumor microenvironment, which shape tumor growth and response to therapeutics. One approach to studying cell interactions is through the use of quantitative network models. In this work, we combined multiple sources of data with a probabilistic method for computing patient level weighted networks that provide predictive features. In total, we constructed 9,234 weighted networks using the TCGA PanCancer data set, containing 64 cell types and 1,894 ligand-receptor pairs. Using robust statistics, informative network features can be found that are associated with disease progression. The entire collection of data, network weights, and results are stored in BigQuery database tables, hosted in the google cloud by ISB-CGC. YouTube and Slides.

April 21 2022 Meeting

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Presentations

1. Daniel Reeves, Fred Hutchinson Cancer Research Center. Multi-scale modelling reveals that early super-spreader events are a likely contributor to novel variant predominance: SARS-CoV-2 variants of concern have been characterized to varying degrees by higher transmissibility, worse infection outcomes and evasion of vaccine and infection-induced immunologic memory. Here we present a multi-scale model of SARS-CoV-2 dynamics that describes population spread through individuals whose viral loads and numbers of contacts (drawn from an over-dispersed distribution) are both time-varying. This stochastic framework allows us to explore how super-spreader events contribute to variant emergence. YouTube and Slides.
2. Nitin Baliga, Institute for System Biology. Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using EGRIN and PRIME:  The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. I will present two predictive models called EGRIN and PRIME to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. (Time permitting) I will also show how the models were used to uncover how combinatorial gene regulation enables C. difficile growth relative to commensal colonization in the mouse gut. YouTube and Slides.

April 14 2022 Meeting

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Presentations

1. Natasha D Sheybani, University of Virginia. Ushering Therapeutic Sound Waves into the Era of Precision Cancer Immuno-Oncology: Despite the promise of cancer immunotherapy, a significant fraction of cancer patients are yet unable to realize its unparalleled benefits. Focused ultrasound (FUS) is a versatile, emerging technology for the non-invasive, non-ionizing, and precisely targeted deposition of acoustic energy into tumor tissues. Recent years have unveiled the capacity of FUS to potentiate cancer immunotherapy through immuno-modulation and targeted drug delivery. In this talk, I will discuss our translational efforts to systematically interrogate the impact of thermal and mechanical FUS regimes on immunological sequelae and extracellular vesicles in solid tumor settings (e.g. metastatic breast cancer, glioblastoma). I will also introduce our goal of ushering FUS into the era of precision immuno-oncology using advanced imaging, liquid biopsy, and artificial intelligence. YouTube and Slides.
2. Joao Xavier, Memorial Sloan Kettering Cancer Center. Modeling species interaction in the gut microbiota using data from hospitalized patients: The gut microbiota is a microbial ecosystem amenable to mathematical modeling. We will discuss our work at the Memorial Sloan-Kettering Cancer Center where we use data from patients to quantify the ecological interactions. The data consists of timeseries of microbial populations and clinical metadata such as the antibiotics given to patients and their number of white blood cells on each day. The model reveals which microbes are most important for patient immunity. Studying ecology directly from patient data can help drive new treatments to modulate immunity by engineering the gut microbiome. YouTube and Slides.

April 7 2022 Meeting

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Presentations

1. Jonas Hue, King's College London. Machine learning powered high content image analysis of routine H&E slides provide novel indicators to predict unfavourable outcome in HPV+ oropharyngeal squamous cell carcinoma patients: Aim: Patients with Human Papillomavirus positive oropharyngeal Squamous Cell Carcinoma (HPV+opSCC) have better prognosis than HPV- counterparts, raising the possibility of treatment de-escalation in the former. About 20% of HPV+opSCC patients demonstrate unfavourable outcomes, contraindicating de-escalation regimes in this subgroup; however, diagnostic procedures to identify such patients are currently unavailable. To address this issue, we developed an automated workflow for quantitative high content image analysis (HCA) of H&E stained sections of HPV+opSCCs. We quantified various known prognostic features such as number and spatial distribution of tumour-infiltrating lymphocytes (TILs) in the stromal and intra-tumour regions. In addition, we measured and attempted to identify other less established features such as stromal plasma cells, tumour nuclei features and morphological heterogeneity within tumour cells. Finally, we trained and validated a model to retrospectively prognosticate outcomes in a cohort of 58 HPV+opSCC patients. Results: Univariate and multivariate statistical analyses revealed that plasma cells, stromal and intra-tumour TILs were more numerous in favourable outcome (FO) patients. Tumour cell nuclei were rounder, less eccentric in morphology and packed closer to one another in patients with FO. Tumour nuclei in FO had more nucleoli and higher texture and granularity features than patients with unfavourable outcomes (UO). UO patients had greater tumour heterogeneity in morphological, spatial and textural measurements. To attempt separating the groups according to these variables we performed statistical discriminant analyses (either LDA or QDA). QDA had an accuracy of 81.7% and 88.1% in predicting UO and FO on our cohort. We validated our analysis by k-fold cross-validation, revealing an estimated overall accuracy of 76.2% and a Kappa statistic of 0.523, indicating a good model considering the complexity of the problem at hand. Conclusions: Single-cell quantitative image analysis of HPV+opSCC allows us to identify prognostic factors and quantify their heterogeneity within the tumour. We have shown that some of these measures are predictive in their own right, and that their variance within a tumour can itself be prognostic and improve the accuracy of statistical discriminant models. Our open-source HCA workflow on routine H&E slides and statistical modelling can aid prognostication of HPV+opSCCs outcome with promising accuracy. Our work supports the use of ML-powered HCA followed by statistical modelling in digital pathology to exploit clinically relevant features in routine diagnostic pathology without additional biomarkers. YouTube and Slides.
2. Yuefan Deng, Stony Brook University. Multi-scale and Machine Learning Algorithms for Modeling Large Blood Clots: Multiscale modeling in biomedical engineering is gaining momentum because of progress in supercomputing, applied mathematics, and quantitative biomedical engineering. For example, scientists in various disciplines have been advancing, slowly but steadily, the simulation of blood including its flowing and the physiological properties of such components as red blood cells, white blood cells, and platelets. Aggregated platelets stimulate blood clotting that causes heart attacks and strokes, resulting in more than 20 million deaths annually (for a comparison, the lethal Covid-19 causes 5.7 million deaths as of Jan. 2022). To reduce such deaths, we must discover new drugs. To discover new drugs, we must understand the mechanism of platelet activation and aggregation. To model platelets’ dynamics involves setting up the basic space and time discretization in huge ranges of 5-6 orders of magnitudes, resulting from the relevant fundamental interactions at atomic, to molecular, to cell, to fluid scales. To achieve the desired accuracy at the minimal computational costs, we must select the correct physiological parameters in the force fields such as the Morse potential and Hooke’s law as well as the spatial and temporal discretization, by machine learning. We demonstrate our results of a multiscale 250-platelet (125 million particles) aggregation simulation and their corroborations with in vitro experiments. YouTube and Slides.

March 31 2022 Meeting

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Presentations

Open discussion on Future Directions of the WG, which will be moderated by James Glazier and Reinhard Laubenbacher. Slides and YouTube.

• Update on WG and other activities.
• Report on Steering Group discussions about a broader organizational structure around predictive/computational immunology, including the connection between individual and environment.
• Discussion about broadening membership.
• Discussion of Seminars and request for ideas for speakers and topics.
• General discussion about challenges/opportunities related to computational immunology.

March 24 2022 Meeting

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Presentations

1. Susan A Shriner, Wildlife Disease Dynamics, Epidemiology, and Response Project, USDA APHIS WS National Wildlife Research Center. Influenza infections dynamics in wild birds. Influenza A viruses (IAVs) are endemic in wild birds but can spillover into poultry and cause serious economic harm. Quantifying infection kinetics is critical to developing predictive disease models aimed at understanding pathogen spread in an effort to prevent spillover. In this study, we evaluated whether IAV exposure dose mediates infection dynamics in mallards, a common IAV reservoir host. We experimentally inoculated 3 groups of 10 mallards with either 103, 104, or 105 EID50 of an H6N2 IAV collected from North American waterfowl during surveillance operations. Each inoculated mallard was housed with three naïve contacts. We collected fine scale viral RNA shedding information throughout the infection in a scheme designed to capture the eclipse, exponential growth, and waning phases of infection. All samples were tested by qPCR. We compared viral RNA output curves by assessing viral RNA peak load, total load, peak day, and shedding period for each dosage group. We modeled log-transformed cumulative viral loads using an exponential asymptote function. In general, viral RNA shedding patterns varied across each of the metrics evaluated with significant individual heterogeneity evident across individuals. On average, the infection curves for mallards inoculated at 104 and 105 EID50 were more similar to each other than the infection curves of the birds infected at 103, suggesting a possible saturation effect at higher exposure doses. In a subsequent experiment, we examined environmental transmission by inoculating a focal mallard and assessing infections in contact ducks added and removed at regular intervals throughout the infection. We replicated this scenario five times. Modeling results indicate that water is the primary driver of transmission and that the concentration of virus in the water is predictive of transmission. We also found that transmission probability varied over time and that mallards became infected at relatively low concentrations of virus in water. As a second follow-up experiment, we collected blood samples from 28 experimentally infected mallards for more than 18 months post exposure to test for antibodies at approximately 4-week intervals. We re-infected the same individuals with the same virus and dose after a year to investigate long-term homosubtypic immunity. After the initial infection, more than half of the ducks exhibited detectable antibodies on day 7 and all ducks were positive on day 10 and remained so through day 28. By day 56, only 39% of ducks were positive by ELISA. Only three individuals had detectable antibodies throughout the year. After the re-challenge, most ducks were antibody positive on day 4, all were antibody positive by day 10, and nearly 70% still showed detectable antibodies on day 140. These results are consistent with an anamnestic response (i.e., a more rapid production of antibodies in greater titers and persistent over a longer time period). Female mallards consistently showed a stronger ELISA response compared to males, but this difference was minor with respect to the percent of positive individuals. Overall, these results indicate antibodies may only be detectable in the short-term in many individuals, but a strong humoral memory may be present. These results have important implications for interpreting surveillance schemes based on serology and shed light on seasonal strain dynamics in mallards. YouTube and Slides.

March 17 2022 Meeting

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Presentations

1. Solly Siebert, Sage Bionetworks. Crowdsourcing and benchmarking to understand viral susceptibility: Data sharing requirements by funders and publishers has unprecedented amounts of genomic and biomedical data available and democratized access by researchers. This, in turn, has likely been a contributing factor to the massive growth in publications over the last decades, despite relatively stable funding dollars. However, in this increasingly computation-heavy world, identification of optimal solutions and methods becomes difficult when researchers use different data and employ different approaches to evaluation, often choosing those that paint their methods in the best light. Crowd sourcing through challenges is one approach to solve important biomedical problems and identify optimal solutions using unbiased apples-to-apples evaluation. Here I describe the approach taken by DREAM Challenges. I highlight several examples, including insights from a 2016-2017 challenge to identify gene expression-based prediction of susceptibility to respiratory viral infection. YouTube and Slides.
2. Julia Arciero, Indiana University. Predicting experimental sepsis survival with a mathematical model of acute inflammation: Sepsis is characterized by an overactive, dysregulated inflammatory response that drives organ dysfunction and often results in death.  Here, a system of four ordinary differential equations (ODEs) was used to simulate the dynamics of bacteria, the pro- and anti-inflammatory responses, and tissue damage. The ODE model was calibrated to experimental data from E. coli infection in genetically identical rats and was validated with mortality data for these animals.  The model demonstrated recovery, aseptic death, or septic death outcomes for a simulated infection while varying the initial inoculum, pathogen growth rate, strength of the local immune response, and activation of the pro-inflammatory response in the system.  The model demonstrated that small changes in parameter values, such as those governing the pathogen or the immune response, could explain the experimentally observed variability in mortality rates among septic rats.  A local sensitivity analysis was conducted to understand the magnitude of such parameter effects on system dynamics.  Despite successful predictions of mortality, simulated trajectories of bacteria, inflammatory responses, and damage were closely clustered during the initial stages of infection, suggesting that uncertainty in initial conditions could lead to difficulty in predicting outcomes of sepsis by using inflammation biomarker levels. YouTube and Slides.

March 10 2022 Meeting

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Presentations

1. Benjamin tenOever, NYU, From your nose to your toes: SARS-CoV-2-mediated systemic inflammation in the absence of viremia. Virus infections can result in inflammation that far exceeds the pathogen’s natural tropism as evident by the heterogeneity of diseases that cause mortality following SARS-CoV-2 infection. To understand this dynamic, we characterized the systemic longitudinal response to SARS-CoV-2 in the golden hamster. We find that while infectious virus is largely restricted to the airways, a strong inflammatory response is evident in the lung, olfactory bulb, kidney, spleen, liver, pancreas, heart, lung, intestines, and most areas of the brain. While no viremia could be detected, profiling circulating immune cells indicate that distal immune priming is a product of circulating “viral debris’.  We postulate that the magnitude and duration of this host response to SARS-CoV-2 reflects the unique life cycles of this virus family.  The lecture will end with some suggestions where modeling may address some of the remaining unknowns as it relates to COVID-19 biology. YouTube and Slides.
   • https://www.science.org/doi/10.1126/sciimmunol.abm3131
   • https://www.nature.com/articles/s41684-020-0614-1
   • https://pubmed.ncbi.nlm.nih.gov/32408338/
   • https://www.science.org/doi/10.1126/sciimmunol.abm3131
   • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846242/

March 3, 2022 Meeting

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Presentations

1. Alexander Hoffmann, UCLA, Modeling innate immune signaling: some emerging regulatory principles: Immune cells must respond appropriately to diverse pathogens or immune stimuli. Four signaling pathways function combinatorially and dynamically to transmit information about the immune threat to nuclear immune response genes. We have developed mathematical models of the molecular networks that recapitulate their stimulus-specific signaling dynamics of the transcription factors NFκB and ISGF3. A number of circuit motifs and regulatory principles give rise to hallmark dynamic features.  Their target genes decode these dynamics via molecular mechanisms that may also be accounted by mathematical models. YouTube and Slides.

February 24, 2022 Meeting

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Presentations

1. Hayriye Gulbudak, University of Louisiana at Lafayette, Modeling across-scale feedbacks of infectious diseases: A current challenge for disease modeling and public health is to understand pathogen dynamics across infection scales from within-host to between-host.  Viral and immune response kinetics upon infection impact transmission to other hosts and feedback into population-wide immunity, all of which influence the severity, trajectory, and evolution of a spreading pathogen.  In this talk, I will introduce structured partial differential equation models linking immunology and epidemiology in order to investigate coevolution of virus and host, multi-scale data fitting, and impacts of dynamic host immunity from an individual to the whole population.  We apply the models to vector-borne diseases, such as Rift Valley fever (RVF) and dengue (DENV), with immunological and epidemiological data.  Using invasion dynamics analysis and multi-scale numerical methods, we characterize different scenarios of virus-host evolution and coexistence of viral strains under waning host cross-immunity.  In the case of DENV, we recapitulate how intermediate levels of pre-existent antibodies enhance infection within a host, and how to scale up to distributions of antibody levels among epidemiological classes in the host population to determine risk of severe DENV prevalence.  These results have implications for optimal vaccination policy, and the modeling framework developed here is currently being applied to examine the emergence of COVID-19 variants partially resistant to antibodies induced by host infection or vaccination. YouTube and Slides.
   • "Infection severity across scales in multi-strain immuno-epidemiological Dengue model structured by host antibody level" https://link.springer.com/article/10.1007/s00285-020-01480-3
   • "Vector-Borne Pathogen and Host Evolution in a Structured Immuno-Epidemiological System" https://link.springer.com/article/10.1007/s11538-016-0239-0
   • "An Immuno-Epidemiological Vector–Host Model with Within-Vector Viral Kinetics" https://www.worldscientific.com/doi/abs/10.1142/S0218339020400021 
2. Abba B Gumel, Arizona State University, Mathematics and the renewed quest for malaria eradication: The widespread use of insecticide-based interventions against malaria mosquitoes over the last two decades has led to a dramatic reduction in global malaria burden, prompting a renewed quest to eradicate the disease by 2030 or 2040. Unfortunately, such heavy use of insecticides has also resulted in widespread resistance (in the malaria vector population) to all the currently available insecticides used in vector control.  In this lecture, I will briefly present a genetics-epidemiology modeling framework for assessing the impacts of insecticide resistance and climate change on malaria transmission dynamics, with emphasis on determining whether the malaria eradication objective can be achieved using existing vector control resources. YouTube and Slides.
   • “Weather-driven malaria transmission model with gonotrophic and sporogonic cycles” https://pubmed.ncbi.nlm.nih.gov/30691351/
   • “Insecticide resistance and malaria control: A genetics-epidemiology modeling approach” https://pubmed.ncbi.nlm.nih.gov/32437715/

February 17, 2022 Meeting

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Presentations

1. Juliano Ferrari Gianlupi, Indiana University, Coupled PK model of an antiviral and agent-based model reveal the importance of inter-cellular metabolism heterogeneity on treatment outcomes. Coupling a pharmacokinetic model of an antiviral therapy with an agent-based model of viral replication and immune response reveals the importance of inter-cellular metabolic heterogeneity on treatment outcomes. We extend our established CompuCell3D based multicellular agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of Remdesivir. We model Remdesivir treatment for COVID-19; however, our methods generalize to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, time of treatment initiation, antiviral half-life, and variability in cellular uptake and metabolism of Remdesivir and its active metabolite, GS--443902, on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models, which treat all cells of a given class as identical can clarify how treatment dosage and timing influence infection dynamics and treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, moderate cell-to-cell variation in drug uptake and elimination requires higher systemic drug doses (from 50% to 3 times the dose for the homogeneous case) to achieve the same level of control of infection within the tissue patch. Heterogeneity reduces treatment efficacy because the cells with the lowest internal levels of active metabolite can act as super-spreaders within the tissue. YouTube and Slides.

February 10, 2022 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Kevin Janes Univ. of Virginia, Title: Stress testing complete kinetic models of individualized enteroviral infection. Abstract: Enteroviruses are responsible for a wide array of human diseases, including viral myocarditis, infantile paralysis, hand-foot-and-mouth disease, and the common cold.  The enteroviral RNA genome is small, and the single polyprotein it encodes is sufficient to infect permissive host cells within a matter of hours.  Since research on cultured enteroviruses began in 1949, we have accumulated a wealth of quantitative information about their biochemistry, cell biology, and genetics.  By comparison, there have been only limited attempts to synthesize this information holistically through mathematical modeling and analysis.  We recently built a complete kinetic model for coxsackievirus B3 (CVB3), an enterovirus that infects cardiomyocytes to cause heart inflammation and failure (Cell Syst 123:304-23 [2021]).  The model encodes detailed mechanisms for three modular pillars of the viral life cycle—host-cell delivery, translation–replication, and encapsidation - along with host-cell antiviral feedbacks that are antagonized during infection.  Over 90% of the numbers parameterizing the rate equations are drawn directly from the literature or our own experiments.  Model simulations captured the kinetics of infection and made predictions about host-cell susceptibilities that were later verified.  Although useful, the model’s construction revealed several unknowns about the critical early steps of enteroviral infection.  I will present ideas for a grant proposal that seeks to tackle the uncertainties head-on by integrating quantitative experiments with mathematical revisions that “stress test” the model to adapt for more-generalized applications.  In doing so, we hypothesize that more detail will yield more insight about open questions in enterovirology. YouTube and Slides.  https://www.sciencedirect.com/science/article/pii/S2405471221000788

    

   and https://www.sciencedirect.com/science/article/pii/S2666166721006468.

    

2. William Waites, Univ. of Strathclyde, Title: Compositional modelling of adaptive immune response to and disease transmission of SARS-CoV-2 Abstract: We show how a simple stochastic model of adaptive immune response recovers individual heterogeneity in viral load in a population. This model can be easily coupled to a transmission model from which we can observe the changing distributions of viral load over the course of an epidemic. This is joint work with, among others, Ruchira Datta and Veronika Zarnitsyna of the innate and adaptive immune response subgroup of the MSM working group. YouTube and Slides.

February 3, 2022 Meeting

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Presentations

1. Hana Dobrovolny, Texas Christian University. Title: Modeling viral coinfections of the respiratory tract. Abstract: Many patients hospitalized with influenza-like illnesses have been found to be infected with more than one virus. Clinical studies have found mixed results when investigating whether coinfections lead to more severe disease, with some studies even suggesting that coinfections are less severe than mono-infections. We use mathematical modeling to investigate the range of possible dynamics during viral coinfections in an effort to better understand clinical outcomes. YouTube and Slides. 
   Other papers on co-infection: https://pubmed.ncbi.nlm.nih.gov/32352574/, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3576904

January 27, 2022 Meeting

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Presentations

1.  Stacey Smith?, University of Ottawa. Title: Is a COVID-19 Vaccine Likely to Make Things Worse? Abstract: In order to limit the disease burden and economic costs associated with the COVID-19 pandemic, it is important to understand how effective and widely distributed a vaccine must be in order to have a beneficial impact on public health. To evaluate the potential effect of a vaccine, we developed risk equations for the daily risk of COVID-19 infection both currently and after a vaccine becomes available. Our risk equations account for the basic transmission probability of COVID-19 (β) and the lowered risk due to various protection options: physical distancing; face coverings such as masks, goggles, face shields or other medical equipment; handwashing; and vaccination. We found that the outcome depends significantly on the degree of vaccine uptake: if uptake is higher than 80%, then the daily risk can be cut by 50% or more. However, if less than 40% of people get vaccinated and other protection options are abandoned—as may well happen in the wake of a COVID-19 vaccine—then introducing even an excellent vaccine will produce a worse outcome than our current situation. It is thus critical that effective education strategies are employed in tandem with vaccine rollout. YouTube and Slides.
2. Vivek Kapur, Pennsylvania State University. Title: Bambi Got Covid. 
   SV Kuchipudi et al. "Multiple spillovers from humans and onward transmission of SARS-CoV-2 in white-tailed deer" Proc Natl Acad Sci U S A. 2022 Feb 8;119(6):e2121644119. doi: 10.1073/pnas.2121644119. PMID: 35078920. https://www.pnas.org/content/119/6/e2121644119.long YouTube and Slides.

January 20, 2022 Meeting

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Presentations

1. Catherine Beauchemin, Ryerson University, Toronto, Canada. Title: Isolating and quantifying the efficacy of virus replication steps.  Abstract: We have arrived at a small set of simple in vitro experiments which, combined with a mathematical analysis, allow us to isolate and quantify the properties of key steps in the virus replication cycle. I will introduce this framework and demonstrate the insights it provides through its application to identify the effect of either a single amino acid viral mutations or the efficacy of an antiviral compound. I will also introduce our new, more biologically meaningful measure of a virus sample's infectivity using the TCID50 assay. YouTube and Slides. Check out midSIN here.
2. Austin J Baird, PhD, Research Assistant Professor , Division of Healthcare Simulation Sciences, Department of Surgery | Univ. Washington Medicine. Title: Modeling the whole-body response to infection and associated acute inflammation, investigating clinical treatments and outcomes. Abstract: I will present a model of inflammation in the BioGears human physiology engine and the impact on treatment to the patient. This model considers a wide range of pro- and anti-inflammatory mediators implicated in human models of inflammation, such as tumor necrosis factor alpha (TNF) and interleukins 6 and 10 (IL-6, IL-10). Consideration of these factors in conjunction with activation of macrophages and neutrophils increases the variability in virtual patient outcomes supported by the model. We present an analysis of IL-6 and IL-10 regarding typical treatment protocols for septic patients. Of critical importance to the usefulness of this model, I’ll show how virtual patient outcomes differ according to model parameterization and the timing and types of actions applied. I’ll try and showcase how models of the nervous system, blood gas, and local autoregulatory function all play a role in impacting the patient response. I’ll also present how distinct inflammatory responses can be generated from the same level of infection by varying a small subset of model parameters. YouTube and Slides. 
   https://github.com/BioGearsEngine/

January 13, 2022 Meeting

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Presentations

1. Robert Hester, University of Mississippi Medical Center. Title: Multiscale modeling of Human Physiology. Abstract: HumMod is a model of human physiology comprising over 9000 dependent variables, and over 1500 independent parameters.  It spans all major body systems, including circulatory, renal, respiratory, hepatic, gastrointestinal, neural, endocrine and muscular systems. Each system is built on one or more working hypotheses/models developed in the literature with reasonable support. These systems combine multiple tissues and their responses to hormones, metabolites, and physical forces. In the laboratory, such responses are considered under controlled circumstances. In the model, the simple mechanisms are tied together to produce feedback loops approximating the body’s own responses to physiological challenges. Current projects include: 1) simulations to understand how medical devices work and 2) simulating virtual populations for in silico clinical trials. YouTube and Slides.

January 6, 2022 Meeting

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Presentations

1. Jeremie Guedj, INSERM, France. Title: Quantifying the relationship between SARS-CoV-2 viral load and infectiousness. Abstract: The relationship between SARS-CoV-2 viral load and infectiousness is poorly known. Using data from a cohort of cases and high-risk contacts, we reconstructed viral load at the time of contact and inferred the probability of infection. The effect of viral load was larger in household contacts than in non-household contacts, with a transmission probability as large as 48% when the viral load was greater than 1010 copies per mL. The transmission probability peaked at symptom onset, with a mean probability of transmission of 29%, with large individual variations. The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by two- to eightfold with variants of concern and assuming no changes in the pattern of contacts across variants, the model predicts that larger viral load levels could lead to a relative increase in the probability of transmission of 24% to 58% in household contacts, and of 15% to 39% in non-household contacts. YouTube and Slides.

A recent relevant link: "Effect of Covid-19 Vaccination on Transmission of Alpha and Delta Variants" https://www.nejm.org/doi/full/10.1056/NEJMoa2116597

December 16, 2021 Meeting

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Presentations

1. Russ Taylor, Johns Hopkins University. Title: Autonomy and Semi-Autonomous Behavior in Surgical Robot Systems. Abstract: This talk will discuss an emerging three-way partnership between physicians, technology, and information to improve treatment processes.  Computer-integrated interventional medicine (CIIM) systems combine innovative algorithms, robotic devices, imaging systems, sensors, and human-machine interfaces to work cooperatively with surgeons in the planning and execution of surgery and other interventional procedures.  Two crucial issues in managing this partnership are 1) how can the human physician specify what the robot is to do and 2) how can the computer controlling the robot ensure that the robot performs the specified task correctly and safely.  This talk will discuss several common paradigms for approaching these questions and will illustrate the approaches with examples drawn from our past and current work. YouTube and Slides.
2. Jane Parker, University of Reading, Department of Food and Nutrition Services. Title: Post covid olfactory dysfunction. Abstract: Anosmia (loss of all olfactory function) affects at least 5% of the general population rising to 20% of those aged over 60. One of the most common aetiologies is post-viral infection (17% of cases) [1], particularly of the upper respiratory tract, as evidenced during the recent Covid-19 pandemic [2]. Recovery often begins with parosmia, a condition where smells become distorted and objectionable, with those severely affected rejecting food, losing weight, leading to clinical depression [3]. Little is known about the underlying mechanisms, but coffee has been identified as one of the most common triggers [4]. In this work, we use a novel approach to test the current hypothesis that parosmia is a result of “mis-wiring” of the olfactory bulb. YouTube and Slides. 
   Some links: 
   Parma et al https://www.medrxiv.org/content/10.1101/2020.05.04.20090902v3 
   Brann et al https://www.science.org/doi/10.1126/sciadv.abc5801

December 9, 2021 Meeting

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Presentations

1. Slim Fourati, Emory University School of Medicine. Title: Ab initio molecular signatures predictive of susceptibility to viral infection. Abstract: The response to respiratory viruses varies substantially between individuals, and little is known on molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveals little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses. YouTube and Slides. 
   https://www.nature.com/articles/s41467-018-06735-8
2. Bingyang Wei, Texas Christian University. Title: Just Enough Requirements Engineering for Non-Computer Science Majors. YouTube and Slides.

December 2, 2021 Meeting

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Presentations

1. Clare Bryant,  Professor of Innate Immunity, The University of Cambridge. Title: Conservation of host cell death in response to bacterial infection: carnivores and other species. Abstract: Some bacterial species readily induce host cell death upon infection.  Salmonella is an efficient cell killer that primarily drives inflammasome cleavage of gasdermin D to induce membrane pores and hence kill macrophages.  Cell death is critical to control Salmonella infection in vivo (a) and macrophages from many species die in response to infection with this bacterium yet many of the genes central to inflammasome cell death are either altered or absent across different animals (b).  This is surprising for what should be critical signaling pathways for host defense and this talk will cover our work in this area and our approaches to these evolutionary differences. YouTube and Slides. 
   a.    Doerflinger,  M., et al. (2020) Flexible Usage and Interconnectivity of Diverse Cell Death Pathways Protect against Intracellular Infection.  Immunity doi.org/10.1016/j.immuni.2020.07.004 
   b.    Digby, Z., et al. (2021) Evolutionary loss of inflammasomes in the Carnivora and implications for the carriage of zoonotic infections.  Cell Reports 36, 109614. Doi:10.1016/j.celrep.2021.109614s.

   
   Additional citations on the inflammasome, and on caspase evolution and species distribution: 
   1.  "Identification of Novel Mammalian Caspases Reveals an Important Role of Gene Loss in Shaping the Human Caspase Repertoire" https://academic.oup.com/mbe/article/25/5/831/1195498 
   2.  "Cytosolic Recognition of Microbes and Pathogens: Inflammasomes in Action" https://journals.asm.org/doi/full/10.1128/MMBR.00015-18 
   3.  "Diet modulates the relationship between immune gene expression and functional immune responses" https://www.sciencedirect.com/science/article/pii/S0965174818304909

2. Reinhard Laubenbacher, University of Florida. Title: New focus, New subgroup. YouTube and Slides.

November 18, 2021 Meeting

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Presentations

1. Elebeoba E. May, University of Houston. Title: Model-based Investigation of the Proinflammatory Microenvironment and Response to Gram-negative Bacteria. Abstract:  Intracellular pathogens like Francisella tularensis (Ft), a gram-negative Class A biothreat agent can trigger the release of cytokines, chemokines, and effector molecules into the microenvironment surrounding the infected cell, contributing to the formation of a proinflammatory microenvironment (PME). Immune cells recruited into the PME can be primed and activated by cytokine exposure promoting a more robust interaction between infiltrating immune cells and infected cells or, in the case of phagocytic cells, priming the cell to more effectively eliminate subsequent Ft infection. Macrophages and NK cells are central to the innate immune response to Ft and primary producers of TNF-α and IFN-γ, respective.  Focusing on these key PME cytokines, which are found to modulate the in vivo response to Ft, we developed in silico and in vitro models to investigate the role of PME in macrophage activation and outcome of infection. YouTube and Slides. 
   https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153289 
    
2. Christian Forst, Icahn School of Medicine at Mount Sinai. Title: The interplay between the human microbiome and respiratory viruses: A multi scale story of influenza and COVID-19. Abstract: The ongoing SARS-CoV-2 pandemic poses a threat to public health and economy, thus urges the scientific community to join efforts in the search of cures. Meanwhile, both influenza and COVID-19 are respiratory diseases caused by airborne RNA viruses. Microbes in the respiratory system have been proven to contribute to the outcome of the diseases. However, scientific advances from studying influenza infection have potentials to benefit the search of cure for SARS-CoV-2 infections. Here we present a comprehensive, multi-scale network analysis of the systems response to the virus. We have developed methods that integrate single-cell and bulk transcriptomic data. These integrated data were further related to the microbiome and clinical outcomes. By this approach we were able to identify cell-population specific key-regulators and host-processes that are hijacked by the virus for its advantage and that contribute to the severity of these infectious diseases. YouTube and Slides.

November 11, 2021 Meeting

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Presentations

1. Lorenzo Veschini, King's College London. Title: “High content image analysis and multiscale cell simulations to investigate emerging cellular heterogeneity in endothelia” Abstract: Understanding healthy and pathological tissue development and functions requires measuring cell molecular signalling, movements, and interactions. Gold standard single cells NGS allows evaluating cell transcriptome and metabolism with great depth and have highlighted the extent of cell heterogeneity within tissues. However, current NGS technologies fail to capture spatial relationships and dynamic phenotypic changes in cells within tissues. Image analysis techniques powered by machine learning allows capturing spatial relationships among cells and evaluating cellular crosstalk. Imaging data are also translatable to multiscale in silico tissue simulations to estimate dynamics of cell movements and molecular crosstalk. We have developed an open-source platform, the endothelial cells profiling tool (EC-PT), to measure individual EC phenotype within endothelia. ECPT includes tools to measure spatial autocorrelation of features such as NOTCH signalling at single cell level allowing to estimate dynamic molecular crosstalk between neighbouring EC and across the whole endothelium. ECPT seamlessly integrates with the multiscale cellular simulation environment Compucell3D. We will present data demonstrating previously unappreciated degrees of EC heterogeneity within the same EC monolayer suggesting a high degree of plasticity. Furthermore, we have developed simulations of NOTCH signalling in cell monolayers using Compucell3D and suggesting that such heterogeneity can result from dynamic and relatively fast phenotypic adjustments at the single cell level. These results provide possible molecular explanations of how EC within the same vascular bed can exert different functions such as proliferation (self-renewal), maintenance of endothelial barrier as well as differential responses to drugs and potentially to pathogens. YouTube and Slides.

November 4, 2021 Meeting

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Presentations

1. Ulrich Schwarz, Univ. Heidelberg, Germany. Title: Multiscale modeling of malaria parasites. Abstract: Malaria has been called the most devastating disease every experienced by mankind. Although today we have efficient drugs and even a first vaccine, it still kills around 400.000 people every year. Because the malaria parasite goes through so many different stages during its journey through the mosquito and human hosts, modeling efforts have to focus on specific aspects of high biological and medical relevance. Here we focus on the skin stage, when the parasite has the form of slender and crescent sporozoites. After release into the host skin during a mosquito blood meal, the malaria sporozoite quickly moves through the connective tissue in search of blood vessels. On a flat substrate, single cells follow circular trajectories with stick-and-slip motion. Using pillar arrays and agent-based computer simulations, we show how this circular motion is converted into different motion patterns that depend on the geometrical properties of the environment. We also study sporozoite movement in the context of large rotating collectives extracted from mosquito salivary glands. Quantitative image processing and agent-based modeling reveal that sporozoites are sorted in these vortices according to their curvatures and speeds, and that this phenomenon strongly depends on its mechanical flexibility. We conclude that this flexibility is an essential element for malaria sporozoites to move in mechanically challenging environments. YouTube and Slides. 
   https://www.sfb1129.de/ 
   https://www.pnas.org/content/118/37/e2103939118 
    
2. Stanca Ciupe, Virginia Tech. Title: The role of testing in COVID-19 control. Abstract: Vaccination is considered the best strategy for limiting and eliminating the COVID-19 pandemic. The success of this strategy relies on the rate of vaccine deployment and acceptance across the globe. As these efforts are being conducted, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is continuously mutating, which leads to the emergence of variants with increased transmissibility, virulence, and lower response to vaccines. One important question is whether surveillance testing is still needed in order to limit SARS-CoV-2 transmission in an increasingly vaccinated population. In this talk, I will present multi-scale mathematical models of SARS-CoV-2 transmission, and use them to determine the effects of vaccine uptake; surveillance testing with tests of different sensitivity, cost, testing frequency, and delay in test return; and testing strategies in limiting an outbreak with alpha and delta variants. YouTube and Slides.

October 28, 2021 Meeting

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Presentations

1. Russ Taylor, Johns Hopkins University. Title: Robotics for the ICU. We had technical difficulties and we will try to reschedule Dr. Taylor for a future meeting.
2. David Forgacs, University of Georgia. Title: What a longitudinal sero-surveillance study taught us about SARS-CoV-2. Abstract: As the fight against SARS-CoV-2 is far from over, serological surveillance is of utmost importance. In order to further elucidate the ways our immune system reacts to COVID-19, we established the SPARTA (SeroPrevalence and Respiratory Tract Assessment) program in order to follow a large number of people across multiple sites in the US. One year after its launch, we have amassed nearly 9,000 visits by close to 2,000 participants from several locations throughout Georgia and California. During monthly visits, saliva is obtained for virological testing and sequencing, serum is collected for the detection of SARS-CoV-2-specific antibodies, and PBMCs are banked for blood cell analysis. The focus of my talk is going to be what serological analyses have taught us about infection, vaccination, and a combination of the two. I will show antibody binding, as well as viral neutralization data to show how effective immunization with an mRNA vaccine is at eliciting a large antibody response, while infection is of much smaller magnitude but stays stable for a much longer period after infection. YouTube and Slides. 
   https://www.frontiersin.org/articles/10.3389/fimmu.2021.728021/full

October 21, 2021 Meeting

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Presentations

1. Jason Shoemaker, University of Pittsburgh. Title: Computational modeling to reveal the molecular mechanisms of immunopathology in flu and COVID-19. Abstract: Computational modeling and controls engineering-based analytics offer the opportunity to drive drug development and treatment optimization for severe respiratory infection. Respiratory infection is a top 10 cause of death in the US in a “normal” year, and COVID-19 has propelled respiratory infection to the 3rd leading cause of death in the US. In the past decade, mounting evidence has suggested that deadly respiratory virus infections, such infections with H5N1 virus, the 1918 Spanish Flu, the 2009 pandemic H1N1 virus and SARS-Cov-2, are associated with distinct immune system dynamics. Complementing this, immunomodulation studies have demonstrated that modifying the immune system can improve tissue pathology and disease outcome. Since June of 2020, immunomodulatory treatment via select corticosteroids have become the primary approach to treating severe COVID-19 infection. However, the immune response is a highly complicated, self-regulating system. Systems engineering approaches are well suited to modeling immune complexity and can provide in silico tools for drug target candidate prioritization and immunomodulatory treatment optimization. Here, I will discuss our work on computational modeling of immunodynamics during influenza infection and COVID-19. Ordinary differential equations (ODE) and agent-based models (ABMs) will be introduced, and we will discuss how these models have been employed to show that: -- For interferon production, paracrine signaling may be more significant than interferon induction mediated directly by the virus for causing aggressive immune responses. -- Some properties of immune signaling systems, such as cellular heterogeneity and stochastic responses, allow the immune system to minimize the number of responsive cells while still maintaining a robust immune response. -- And H1N1 and H5N1 viruses seem to differently induce interferon production. Time permitting, we will discuss network-based modeling approaches, including using global network controllability, for predicting host proteins (i.e. factors) that are essential for influenza virus replication. Using an siRNA screen to validate network predictions, we demonstrate that data-driven subnetwork construction is a successful approach for predicting novel drug target candidates. This approach has recently been applied to COVID-19 data, suggesting new possible molecules for therapeutic consideration. YouTube and Slides.

2. Tiam Heydari, PhD candidate at Zandstra lab, School of Biomedical Engineering, University of British Columbia, Title: IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNAseq data. Abstract: The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to a scRNA-seq dataset from early mouse T-cell development and show that it can infer a priori over 75% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of the known gene perturbations on the T-cell developmental trajectory. Finally, we implement a IQCELL gene selection pipeline that allows us to select, without prior knowledge, candidate genes and demonstrate that GRN simulations based on this set yield results similar to the original curated list. In summary the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems. YouTube and Slides.

October 14, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Balazs Madas, Centre for Energy Research, Budapest, Budapest, Hungary. Title: Deposition distribution of the new coronavirus (SARS-CoV-2) in the human airways upon exposure to cough-generated droplets and aerosol particles. Abstract: While huge efforts have been made to understand the spread of COVID-19 as well as the pathogenesis following cellular entry, much less attention is paid to how SARS-CoV-2 from the environment reach the receptors of the target cells. The aim of this study is to characterize the deposition distribution of SARS-CoV-2 in the airways upon exposure to cough-generated droplets and aerosol particles. For this purpose, the Stochastic Lung Deposition Model has been applied. Particle size distribution, breathing parameters supposing normal breathing through the nose, and viral loads were taken from the literature. We found that the probability of direct infection of the acinar airways due to inhalation of particles emitted by a bystander cough is very low. As the number of viruses deposited in the extrathoracic airways is about 7 times higher than in the acinar airways, we concluded that in most cases COVID-19 pneumonia must be preceded by SARS-CoV-2 infection of the upper airways. Our results suggest that without the enhancement of viral load in the upper airways, COVID-19 would be much less dangerous. The period between the onset of initial symptoms and the potential clinical deterioration could provide an opportunity for prevention of pneumonia by blocking or significantly reducing the transport of viruses towards the acinar airways. Therefore, even non-specific treatment forms like disinfection of the throat and nasal and oral mucosa may effectively keep the viral load of the upper airways low enough to avoid or prolong the progression of the disease. In addition, using a tissue or cloth in order to absorb droplets and aerosol particles emitted by own coughs of infected patients before re-inhalation is highly recommended even if they are alone in quarantine. YouTube and Slides.

2. Yinon Moise Bar-On, Weizmann Institute of Science. Title: Booster protection against COVID19 confirmed infections and severe disease. Abstract:  The early initiation of a nationwide vaccination campaign in Israel, led to a sharp decrease in the incidence of COVID-19 between mid-January 2021 and June 2021. Nevertheless, the emergence of the delta variant, and waning immunity has led to a recent resurgence in both confirmed infection and severe illness. 

   In an effort to curb this resurgence, Israeli authorities approved the administration of a booster dose, first to the 60+ population and later to the entire 16+ population who received a second dose >5 months earlier. 

   We analyze a nationwide database including data on about 5 million individuals to estimate the effect of the administration of the booster dose on the rates of confirmed infection and severe illness. YouTube and Slides.

October 7, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. David O’Connor, Professor of pathology and laboratory medicine, University of Wisconsin. Title: Implications of Israel's aggressive third dose vaccine program. Abstract: Israel began an aggressive COVID-19 immunization program before most other countries. Consequently, they were among the first to observe waning immunity. This prompted Israel to begin an ambitious real-world experiment to provide third doses of the Pfizer/BioNTech vaccine to those who were initially vaccinated at least five months previously. The early results show dramatic protection from both severe illness and mild cases among older individuals who were the first to receive the vaccine. In this presentation I will summarize the Israeli data and discuss why I think other countries including the US should implement similar programs as soon as possible. YouTube and Slides.
2. Shelby O’Connor, Professor of pathology and laboratory medicine, University of Wisconsin. Title: Air surveillance for respiratory pathogens in Dane County communities. Abstract: The COVID-19 pandemic has highlighted how under prepared our community is to detect circulating respiratory viruses. Predictive warning systems for these viruses are needed. For the past several months, we have been piloting Thermo AerosolSense air samplers in university, hospital, and other community settings. We have detected SARS-CoV-2 genetic material in air samples collected at many of these sites. In the future, we propose adapting this system for widespread use in K-12 schools to support ongoing mitigation strategies and provide additional virus detection methods for school communities. YouTube and Slides.

September 30, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Kaiming Ye, Binghamton University, Title: Cell Engineered Vaccine Against H5N1 Avian Influenza Infection and Cancers. YouTube and Slides.

Some relevant links from John Rice and Ruchira Datta:

1. https://directorsblog.nih.gov/2021/09/30/new-microscope-technique-provides-real-time-3d-views/ 
   posted today seems this would be useful to Dr Ye's work. "New Microscope Technique Provides Real-Time 3D Views" NIH Directors Blog
2. CD47 as a potential biomarker for the early diagnosis of severe COVID-19 https://www.biorxiv.org/content/10.1101/2021.03.01.433404v1.full
3. A Potential Role of the CD47/SIRPalpha Axis in COVID-19 Pathogenesis https://www.mdpi.com/1467-3045/43/3/86

September 23, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Jorge Velasco-Hernandez, Instituto de Matemáticas, Unidad Juriquilla, oficina 20, Nodo Multidisciplinario de Matematicas Aplicadas and Adrián Acuña-Zegarra, Universidad de Sonora.  Title: Models and indicators for the evolution of the epidemic in Mexico.  Summary: I will present models and quantitative indicators that we have developed or applied to inform public health authorities on the mitigation and control of the epidemic in Mexico but, particularly, in Querétaro state. In Mexico there are limited testing and contact tracing of cases. We have, therefore, limited information for disease surveillance. Nevertheless, the need to have indicators of the state of the epidemic is a pressing need. We have developed some ideas to overcome these limitations that incorporate the traditional social behaviour of the Mexican people into our models. https://www.medrxiv.org/content/medrxiv/early/2021/04/20/2021.04.14.21255436.full.pdf YouTube and Slides.

September 16, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Daniel Becker, University of Oklahoma. Title: Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs. Abstract: Identifying and monitoring the wildlife reservoirs of novel zoonotic viruses remains logistically challenging and costly. Statistical models can be used to guide sampling prioritization, but predictions from any given model may be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of likely reservoir hosts. In the first quarter of 2020, we generated an ensemble of eight statistical models that predict host-virus associations and developed priority sampling recommendations for potential bat reservoirs. Over a year, we tracked the discovery of 40 new bat hosts of betacoronaviruses, validated initial predictions, and dynamically updated our analytic pipeline. We find that ecological trait-based models perform extremely well at predicting these novel hosts, whereas network methods consistently perform roughly as well or worse than expected at random. These findings illustrate the importance of ensembling as a buffer against variation in model quality and highlight the value of including host ecology in predictive models. Our revised models show improved performance and predict over 400 bat species globally that could be undetected hosts of betacoronaviruses. Although 20 species of rhinolophid bats are known to be the primary reservoir of SARS-like viruses, we find at least three-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected. Our study is the first to show via systematic validation that machine learning models can help optimize wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating. Lastly, we discuss next steps to systematically integrate within-host data streams into future modeling efforts. YouTube and Slides.

   From Dr. Ruchira Datta: 
   https://www.biorxiv.org/content/10.1101/2020.05.22.111344v4.external-links.html 
   https://www.sciencedirect.com/science/article/abs/pii/S0169534720302299 
   https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00995

2. John Burke, CEO, President, and Co-founder, Applied BioMath Title: Quantitative Modeling and Simulation to Drive Critical Decisions from Research through Clinical Trials. Abstract: 
   • Quantitative Systems Pharmacology (QSP) is a mathematical modeling and engineering approach that aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms. 
   • Several examples will be shown which highlight QSP efforts to accelerate the discovery and development of best-in-class therapeutics and impact critical decisions, in the continuum from preclinical exploration to clinical research. 
   YouTube and Slides.

September 9, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Virginia Pasour, ARO biomath program, Title: The Biomathematics Program at the US Army Research Office (ARO). Abstract:  I will give a brief introduction to research at ARO as well as the specific goals of the Biomathematics Program and will welcome questions about opportunities related to the Working Group members’ interests. YouTube and Slides.
2. Tomasz Lipniacki, Institute of Fundamental Technological Research, Poland. Title: Super-spreading events initiated the exponential growth phase of COVID-19 with Ro higher than initially estimated. Abstract: The basic reproduction number R0 of the coronavirus disease 2019 has been estimated to range between 2 and 4. Here, we used an SEIR model that properly accounts for the distribution of the latent period and, based on empirical estimates of the doubling time in the near-exponential phases of epidemic progression in China, Italy, Spain, France, UK, Germany, Switzerland and New York State, we estimated that R0 lies in the range 4.7–11.4. We explained this discrepancy by performing stochastic simulations of model dynamics in a population with a small proportion of super-spreaders. The simulations revealed two-phase dynamics, in which an initial phase of relatively slow epidemic progression diverts to a faster phase upon appearance of infectious super-spreaders. Early estimates obtained for this initial phase may suggest lower R0. YouTube and Slides.

September 2, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Harry Hochheiser, University of Pittsburgh School of Medicine, Principal Investigator of the MIDAS coordinating center. Presenting a talk about the efforts of the Coordinating Center around data provisioning, data models, FAIR data, and some of the possibilities that we envision for infectious disease modeling data. YouTube and Slides. 
   You can join MIDAS at https://midasnetwork.us/midas-membership/
2. Guido España, University of Notre Dame. Title: Modeling the impact of COVID-19 using agent-based models. Abstract: More than 122,000 COVID-19 associated deaths have been reported in Colombia and about 27,000 in the city of Bogotá by the first week of August, with vaccination coverage in the city at 30% of fully vaccinated people. As the incidence of cases currently decreases, questions remain about the potential impact of the delta variant already present in the city. We used an agent-based model calibrated to data on age-structured deaths and dominance of variants in Bogotá.  We modeled scenarios of early and delayed introduction of the delta variant in the city along with changes in mobility and social contact, and vaccine strategies over the next months.  Our model suggests that by mid-July, vaccination may have already prevented 17,800 (95% CrI: 16,000 - 19,000) deaths in Bogotá. The delta variant could become dominant and lead to a fourth wave later in the year, but its timing will depend on the date of introduction, social mixing patterns, and vaccination strategy. In all scenarios, higher social mixing is associated with a fourth wave of considerable magnitude. If an early delta introduction occurred (dominance by mid-July), a new wave may occur in August/September and in such case, age prioritization of vaccination is more important. However, if introduction occurred one or two months later (dominance by mid-August/September) the age-prioritization is less relevant in all scenarios we found that increasing the vaccination rate from the current average of 50,000/day to 100,000/day reduces the impact of a fourth wave due to the delta variant. In Bogotá, the delta variant could still lead to a fourth wave with a magnitude that depends on the level of social mixing and the timing of delta introduction. The impact of this wave can be reduced by maintaining moderate levels of social mixing and increasing vaccination rates to achieve high coverage. We found that, at this point, suspending the age prioritization to achieve higher coverage with first doses does not seem to have a major effect on deaths and ICU demand. YouTube and Slides. The impact of school reopening on COVID-19 dynamics in Bogotá, Colombia: https://osf.io/ebjx9/.

Some links relevant to today's talks (thanks to Dr Datta):

1. FRED (A Framework for Reconstructing Epidemic Dynamics) (2013): https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-940
2. Estimates of the severity of COVID-19 disease (2020): https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1
3. Age-dependent effects in the transmission and control of COVID-19 epidemics (2020): https://www.nature.com/articles/s41591-020-0962-9
4. SARS-CoV-2 variants: https://covariants.org/

August 26, 2021 Meeting

Slides are here and the YouTube recoding here.

This meeting was a futures planning discussion for the working group. The preliminary discussion document is here.

August 19, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Dean Bottino, Takeda. Title: Modeling-based evaluation of the potential of Takeda Oncology compounds for treatment of COVID-19. YouTube and Slides.
2. Juilee Thakar, University of Rochester Medical Center. Title: Discrete state modeling and knowledge acquisition to investigate human immune response. Abstract: Human immune response is complex interplay of molecular and cellular responses to signals. The wide range of signals such as viruses and vaccines elicit cascades of signaling events, which can be measured using highly advanced high-throughput assays. We have developed tools to construct mechanistic models of these signaling cascades using omics datasets. The mechanistic models are developed using parameter-free discrete-state methods and reveal multiple different states of cellular phenotype driven by molecular pathways. However, one caveat is in retrieving high-quality frequently updated topological maps of molecular and cellular information.  This talk will briefly introduce the mechanistic modeling methodology and will drive discussion on knowledge acquisition in context to molecular and cellular pathways.YouTube and Slides.

August 12, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Henrique de Assis, Laboratory for Systems Medicine, University of Florida.Title: Computational Modeling Reveals the Role of Macrophages in Respiratory A. fumigatus Infection in Immunocompromised Hosts. Abstract: Fungal infections of the respiratory system are a life-threatening complication for immunocompromised patients. Invasive pulmonary aspergillosis, caused by the airborne mold Aspergillus fumigatus, has a mortality rate of up to 50% in this patient population. The lack of neutrophils, a common immunodeficiency caused by, e.g., chemotherapy, disables a mechanism of sequestering iron from the pathogen, an important virulence factor. This paper shows that a key reason why macrophages are unable to control the infection in the absence of neutrophils is the onset of hemorrhaging, as the fungus punctures the alveolar wall. The result is that the fungus gains access to heme-bound iron. At the same time, the macrophage response to the fungus is impaired. We show that these two phenomena together enable the infection to be successful. A key technology used in this work is a novel dynamic computational model used as a virtual laboratory to guide the discovery process. YouTube and Slides. 
    
2. T.J. Sego, Indiana University. Title: Generating Multicellular Spatiotemporal Models of Population Dynamics from Ordinary Differential Equations. Abstract: The biophysics of an organism span multiple scales from subcellular to organismal, and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them, and highlights the need for simpler methods able to model the spatial features of biological systems. This work develops a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice-versa. The method is demonstrated by generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. These generated spatial models show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using the method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. The method may be useful for generating new ODE model terms from spatiotemporal, multicellular models, recasting popular ODE models on a cellular basis, and generating better models for critical applications where spatial and stochastic features affect outcomes. YouTube and Slides.

August 5, 2021 Meeting

Presentations

1. James Sluka, Intelligent Systems Engineering and Biocomplexity Institute,Indiana University. Title: Discussion of proposed manuscript form the VPWG “A Practical Approach to Digital Twins in Medicine”. Abstract: The goal of the paper is to define the domain of medical digital twins as well as the major benefits, opportunities, and challenges. There are a range of understandings, definitions, and applications. In our VP WG seminars, there is no consensus of what a "Digital Twin" is. Is it a giant computing infrastructure that will cost billions to develop? Or can usable examples be developed on a much smaller financial scale? Is it AI based, or mechanism based? How does it relate to "Personalized Medicine"? What data is readily available and what data will require technological development to measure? How is data shared and communicated? Where is data stored? How is the data converted into actionable insights in patient care? Here we attempt to enumerate the domains and range for Medical Digital Twins, outline the challenges, opportunities and benefits and provide a definition of the components. YouTube and Slides.
2. Group Discussion: The IMAG/MSM Viral Pandemics Working Group: Discussion on our goals for year 2.

July 29, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Gary An, University of Vermont, College of Medicine. 
   Title: Comparative Biology Immune Agent-based Model. 
   Abstract: Given the importance and impact of pandemic viruses of bat origin, there is potential significant benefit in the comparative investigation into the differences in bat and human immune responses to viruses in terms of providing insight into what form future pandemics might manifest, and what types of potential therapies might be implemented. The practice of comparative biology can be potentially enhanced by the addition of mathematical and computational methods that can provide a means of dynamic knowledge representation to visualize and interrogate the putative differences between the two systems. 
   
   Towards this end, we present an agent-based model of components of the immune system that encompasses and bridges the differences between bat and human responses to viral infection; we term this model the Comparative Biology Immune Agent-based Model, or CBIABM. To our knowledge this is the first mechanism-based computational/mathematical model that seeks to directly compare bat and human immune mechanisms and the consequences of those mechanistic differences, namely inflammasome activity and differences in Type 1 Interferon dynamics, in terms of resistance to viral infection. Simulation experiments with the CBIABM demonstrate the efficacy of bat-related changes of impaired inflammasome activation and constitutive production of Type 1 Interferons in conferring viral resistance. Furthermore, simulation studies suggest a crucial role of endothelial inflammasome activity as a mechanism for systemic bat viral resistance and the clinical manifestations and severity of disease in human viral infections. Future work will involve the additional of adaptive immune features to this initial version of the CBIABM and the incorporation of features and properties of specific viruses. We hope that this initial study will help inspire additional comparative modeling projects that use computational dynamic knowledge representation to link, compare, and contrast immunological functions shared across different species, and in so doing, provide insight and preparation for responses to future viral pandemics of zoonotic origin. Article: https://www.mdpi.com/1999-4915/13/8/1620/pdf. YouTube and Slides.
2. Keisuke Ejima, Indiana University Bloomington. 
   Title: Estimation of Epidemiological Key Parameters Using Viral Dynamics Model. 
   Abstract: Viral dynamics models have extensively been used in mathematical biology. The models helped us understand quantitative and qualitative characteristics of temporal dynamics of viral load. Recently, the models are used in epidemiological and clinical studies. Since COVID-19 pandemic started, our group has been exploring the utility of the models in providing implication for public health practice. Particularly, the model was used to estimate the following two key epidemiological parameters: incubation period and false-negative rate of PCR tests. Additionally, we demonstrated the utility of the model in distinguishing imported cases and locally infected cases at early phase of the pandemic. The model was also used to compute the sample size for clinical trials of antiviral treatment and design guideline to determine when to end isolation of COVID-19 patients. I would like to argue the possibility of collecting longitudinal viral load data and further application of the models to epidemiological and clinical studies. YouTube and Slides.

July 22, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Angela Reynolds, Virginia Commonwealth University, 
   Title: Mathematical Modeling of the Inflammatory Response in Lung Infections and Injuries https://www.frontiersin.org/articles/10.3389/fams.2020.00036/full and Modeling Ventilator Induced Lung Injury https://pubmed-ncbi-nlm-nih-gov.proxyiub.uits.iu.edu/33930440/ . YouTube and Slides.
2. Reinhard Laubenbacher, Dean’s Professor of Systems Medicine, Director, Laboratory for Systems Medicine, Department of Medicine, University of Florida. 
   Title: A modular computational framework for medical digital twins 
   Abstract: This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams. They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions. Their large-scale development relies on both mechanistic and data driven techniques and requires the integration and ongoing update of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized, community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection. YouTube and Slides.

July 15, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Martha Mendoza, Associated Press. Mendoza is a two time Pulitzer Prize winner who has taught investigative reporting in the University of California Science Communications master’s program for more than a decade. 
   Title: Communicating Science with Journalists 
   Abstract: Scientists and journalists have different goals, but there’s overlap. Both want to engage, both want to inform. Journalist Martha Mendoza will share the behind-the-headlines work that reporters go through to find and produce news stories, and talk about some ways scientists can communicate with journalists about what they’re doing and why the public should know. YouTube and Slides.
2. Aleszu Bajak, Senior data reporter, USA Today. Aleszu Bajak is a science and data journalist, former Knight Science Journalism Fellow at M.I.T. and Science Friday radio producer who has been a freelance reporter in Latin America and once upon a time worked in the gene therapy department at Weill Cornell. His work has appeared in The New York Times, The Washington Post, The Boston Globe magazine, M.I.T. Technology Review, OjoPúblico, The Huffington Post, Esquire, Nature, Science, and Guernica. He founded and edited Storybench, a publication hosted by Northeastern’s School of Journalism that explores the future of digital storytelling and data journalism. He also served as innovation lead at the Co-Laboratory for Data Impact within Northeastern while teaching courses in journalism, coding and data visualization. 
   Abstract: 1) How do we better communicate models and with models, drawing on my work collaborating with researchers and building my own. 2) Tips for crafting strong, enticing op-eds. 3) Discussion of media’s successes and failings and how researchers can facilitate stories and steer coverage in new directions. YouTube and Slides.

July 8, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations

1. Evan Patterson, https://www.epatters.org/ and James Fairbanks, Computer and Information Science and Engineering Department Herbert Wertheim College of Engineering University of Florida. 
   Title: Compositional Modeling with AlgebraicJulia 
   YouTube and Slides. The Jupyter Notebook used in the presentation is here and it can be run from your browser.

July 1, 2021 Meeting

Slides: Are here.

Special 2 hour meeting on Bridge2AI & Digital Twins

Agenda: 
3:00pm – Brief recap of Bridge2AI – Grace Peng 
        Prior to meeting:  review presentations and videos on KiStorm 
<https://kistorm.com/QDB69EaAg9NlV0sZQwiJ/LYF9wKZUTPOrliNddEDn&gt; 
3:15 - IMAG roundtable:  Where does modeling fit? – IMAG agency reps 
           Prior to meeting: review Bruce Tromberg’s blog 
           <https://www.nibib.nih.gov/about-nibib/directors-corner/corner-posts/bri…; 
           and video (start at 10:14) <https://youtu.be/yZ-4tDvT6lY&gt;

3:30 – Open Q&A on Bridge2AI

4:00 – MSM strategy for Digital Twins – MSM Viral Pandemic WG (VPWG) 
            Prior to meeting: review VPWG recording on Digital Twins 
           <https://www.imagwiki.nibib.nih.gov/resources/presentations/multi-scale-…;

      1.  VPWG context 
      2.  NIAID interests 
      3.  context of potential Fall meeting

Grace Peng's notes document is at: 
https://docs.google.com/document/d/1Aj9aFzfwY1Ffls9elk1w6kmpw99w1rA5mPnZdx1QLYI/

The YouTube recording is here.

June 24, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Liesbet Geris, Virtual Physiological Human Institute (VPHI) EXECUTIVE DIRECTOR, university of Liège and KU Leuven in Belgium. Title: In silico medicine: bringing the community together and the field forward. Abstract: The use of computer modeling and simulation in medicine, also called in silico medicine, is taking big strides towards fulfilling its promise: contributing to a faster, safer and more personalised healthcare. In silico technologies are increasingly adopted in all aspects of healthcare, from prevention over diagnostics to treatment prediction and clinical follow-up. This progress is owed to a collaborative effort of a wide variety of stakeholders, from researchers to clinicians, from industry to regulators, and from policy makers to funders.  In recent years, FDA and ASME published guidelines and standards on reporting, verification and validation of computer models used in the context of medical devices. In Europe, regulators are working together with academia and industry to establish guidelines for the evaluation of in silico technologies (beyond the classical well-regulated pharmacometrics) in drug design and development. Organizations such as the Virtual Physiological Human institute (VPHi, the international scientific society for in silico medicine) and the Avicenna Alliance (the alliance of VPHi and industry) are driving initiatives that will further accelerate the adoption of in silico medicine. One such initiative is the work on ‘Good Simulation Practice’ which will be a quality standard defining how to assess and approve in silico tools before they can be used to produce regulatory evidence on the safety/efficacy of a new medical product. Another initiative is the Community Challenge for the adoption of consensus protocols for the characterization of biological tissue properties (C4bio), which will lead to a ‘certificates of birth’ for the generated data meeting regulatory requirements.   Key to all these initiatives is the involvement of multiple stakeholders, which ensures a wide basis for acceptance and uptake of the developed standards and guidelines and which is the only way to remove the remaining barriers for the full adoption of in silico medicine. YouTube and Slides.
2. Rufus Pollock, Datopian, President and Founder.  Title: Data Portals and Data Management for Accelerating Time to Insight Abstract: Data Portals and Data Management System (DMS) have become essential tools in unlocking the value of data for organizations and enterprises ranging from the US government to Fortune 500 pharma companies, from non-profits to startups. They provide a convenient point of truth for discovery and use of an organization’s data assets. 
   A Data Portal is a gateway to data. That gateway can be big or small, open or restricted. For example, data.gov (opens new window)is open to everyone, whilst an enterprise “intra” data portal is restricted to that enterprise (and perhaps even to certain people within it). 
   A Data Portal’s core purpose is to enable the rapid discovery and use of data. However, as a flexible, central point of truth on an organizations data assets, a Data Portal can become essential data infrastructure and be extended or integrated to provide many additional features. 
   The rise of Data Portals reflect the rapid growth in the volume and variety of data that organizations hold and use. With so much data available internally (and externally) it is hard for users to discover and access the data they need. And with so many potential users and use-cases it is hard to anticipate what data will be needed, when. 
   Concretely: how does Jane in the new data science team know that Geoff in accounting has the spreadsheet she needs for her analysis for the COO? Moreover, it is not enough just to have a dataset’s location: if users are easily to discover and access data it has to be suitably organized and presented. 
   Data portals answer this need: by making it easy to find and access data, a data portal helps solve these problems. As a result, data portals have become essential tools for organizations to bring order to the “data swamp” and unlock the value of data assets.  https://tech.datopian.com/data-portals/ YouTube and Slides.

June 17, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Chantal Darquenne, University of California, San Diego. Title: The fate of inhaled aerosols in health and disease. YouTube and Slides.

2. Adi Stern, Tel Aviv University. This will be rescheduled. Title: Evidence for vaccine breakthrough by coronavirus variants of concern in particular time windows post vaccination. Abstract: The BNT162b2 mRNA vaccine is highly effective against SARS-CoV-2. However, apprehension exists that variants of concerns (VOCs) may surmount vaccine protection, following evidence showing reduced neutralization of VOCs B.1.1.7 and B.1.351, and others, in laboratory assays. We performed a matched cohort study to examine the distribution of VOCs in infections of vaccinees, and hypothesized that if vaccine effectiveness against a VOC is reduced, its proportion among breakthrough cases would be higher than in unvaccinated controls. We showed that vaccinees who tested positive at least a week after the second dose, were disproportionally infected with B.1.351, compared with controls. Those who tested positive between two weeks after the first dose and a week after the second dose, were disproportionally infected by B.1.1.7, suggesting reduced vaccine effectiveness against both VOCs at particular time windows. Our results emphasize the importance of rigorously tracking viral variants, and of increasing vaccination to prevent the spread of VOCs (https://www.nature.com/articles/s41467-020-19248-0). YouTube and Slides.

June 10, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Charles A. Taylor, co-founder, Chief Technology Officer (CTO), and member of the Board of Directors of HeartFlow Inc. Title: Convincing physicians to trust computational modeling. YouTube and Slides.

June 3, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Alun L. Lloyd, North Carolina State University, Title: Bidirectional Contact Tracing for COVID-19. Abstract: Contact tracing is critical in controlling COVID-19 outbreaks in the absence of a vaccine, but most protocols only “forward-trace” to notify people who were recently exposed. Using a stochastic branching-process model, we find that “bidirectional” tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in the effective reproduction number, R_{eff}, achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realized by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control. YouTube and Slides.

2. Amanda Randles, Alfred Winborne Mordecai and Victoria Stover Mordecai Assistant Professor, Duke University. Title: Developing scalable, efficient, and accurate personalized flow simulations. Abstract: Patient-specific simulations are a promising area for personalized medicine and often times require efficient use of large-scale computational resources. In this talk, I will discuss two use cases: personalized blood flow modeling and air flow models to support ventilator splitting.  In each case, I will discuss how we have developed scalable models and tuned them to represent individual patients.  I will cover the acquisition of the data, building of the model, validation methods, and steps to ensure scalable and reproducible results.  In terms of the blood flow models, it has been shown that hemodynamic forces can play a key role in the localization and development of disease. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system, these image-based models yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next generation drug delivery systems. Building a detailed, realistic model of human blood flow, however, is a formidable mathematical and computational challenge. The models must incorporate the motion of fluid, intricate geometry of the blood vessels, continual pulse-driven changes in flow and pressure, and the behavior of suspended bodies such as red blood cells. In this talk, I will discuss the development of HARVEY, a parallel fluid dynamics application designed to model hemodynamics in patient-specific geometries. I will cover the methods introduced to reduce the overall time-to-solution and enable near-linear strong scaling on some of the largest supercomputers in the world.  Finally, I will present the expansion of the scope of projects to address not only vascular diseases, but also treatment planning and the movement of circulating tumor cells in the bloodstream. For the ventilator splitting work, I will discuss how we established a new model, validated it, and were able to turn around a large parameter study using 800,000 compute hours over the course of one weekend.  In both cases, a problem-centric approach set the stage for building efficient models that can provide insight into patient-specific dynamics. YouTube and Slides.

May 27, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Philip Ball, is a freelance science writer. He worked previously at Nature for over 20 years, first as an editor for physical sciences (for which his brief extended from biochemistry to quantum physics and materials science) and then as a Consultant Editor. His writings on science for the popular press have covered topical issues ranging from cosmology to the future of molecular biology. (https://www.philipball.co.uk/bio) YouTube and Slides and Script. 
   Additional articles by Phillip Ball:
   1. Shot of hope: inside the race for a coronavirus vaccine https://www.prospectmagazine.co.uk/magazine/when-will-there-be-vaccine-coronavirus-progress
   2. What you need to know about the coronavirus vaccine https://www.prospectmagazine.co.uk/magazine/what-you-need-to-know-about-the-coronavirus-vaccine
   3. The epidemiology of misinformation https://www.prospectmagazine.co.uk/science-and-technology/epidemiology-misinformation-coronavirus-covid19-conspiracy-theory
   4. Ten lessons of the Covid-19 pandemic https://www.newstatesman.com/international/coronavirus/2020/10/ten-lessons-covid-19-pandemic
   5. The silence of the chief scientist ... https://www.newstatesman.com/politics/health/2020/05/silence-chief-scientists-worrying-and-deeply-political
   6. The day that Dominic Cummings finally came clean https://www.thearticle.com/the-day-that-dominic-cummings-finally-came-clean
   7. Mr. Ball also pointed out this interesting Opinion Piece in Science: "Science journalism grows up" Deborah Blum, Science  23 Apr 2021:Vol. 372, Issue 6540, pp. 323 DOI: 10.1126/science.abj0434
2. Miriam Rafailovich, Department of Materials Science, SUNY at Stony Brook. Title:  viral infection and initiation of thrombosis. Abstract: The conformation of fibrinogen on hydrophobic surfaces is shown to initiate fibrillogenesis and non-thrombogenic clots. Even though endothelial cells are not easily infected,  non-thrombogenic clots are shown to form after exposure of the cells to conditioned media from epithelial cells infected with H1N1 and OC43 encapsulated RNA viruses. Inflammatory factors secreted from infected epithelium is believed to be responsible for damage to the endothelium, initiating opening of the fibrinogen alpha-C domains initiating  fibrillogenesis. Blocking the terminus of the alpha-C domains with a 12 amino acid peptide, prevents fibrinogen fiber formation, which may suppress spontaneous clot formation associated with viral infection. YouTube and Slides.

Thanks to Dr. Datta for these links related to Dr. Rafailovich's talk.

1. A Physical and Regulatory Map of Host-Influenza Interactions Reveals Pathways in H1N1 Infection https://www.sciencedirect.com/science/article/pii/S0092867409015657
2. Aberrant coagulation causes a hyper-inflammatory response in severe influenza pneumonia https://pubmed.ncbi.nlm.nih.gov/27041635/
3. Coagulation Abnormalities and Thrombosis in Patients Infected With SARS-CoV-2 and Other Pandemic Viruses https://pubmed.ncbi.nlm.nih.gov/32657623/
4. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19 https://www.nejm.org/doi/full/10.1056/NEJMoa2015432
5. Effects on Coagulation and Fibrinolysis Induced by Influenza in Mice With a Reduced Capacity to Generate Activated Protein C and a Deficiency in Plasminogen Activator Inhibitor Type 1 https://www.ahajournals.org/doi/full/10.1161/01.RES.0000250834.29108.1a
6. Control of Anti-Thrombogenic Properties: Surface-Induced Self-Assembly of Fibrinogen Fibers https://pubs.acs.org/doi/10.1021/bm2015976
7. In situ conformational analysis of fibrinogen adsorbed on Si surfaces https://www.sciencedirect.com/science/article/abs/pii/S0927776505000986

May 20, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Rusty Irving, retired GE Digital Twin GEN Mgr & Chief Engineer now Computer Science faculty at Siena college. Title: Digital Twins: A Practitioner’s View

   ABSTRACT:  In recent year’s one of the latest buzzwords in the fields of computing, modeling, artificial intelligence and others is “Digital Twin”. Rusty Irving is one of the pioneer’s in the development of Digital Twins. Digital Twins are a capstone of his life’s work as he is also a pioneer in “The Internet of Things”. In 2005 he gave the first ever public lecture on that topic. It is still available on iTunes as a video. (Search on “Rusty Irving UMBC”.) He has given international keynote speeches on Digital Twin.

   In this talk Rusty will provide the context (history) from which Digital Twins have evolved. This context is important as it helps calibrate what Digital Twins can and can’t do today. With this context the talk will cover:

• Industrial Digital Twin Definition Explained
• Industrial Digital Twin Examples
• A Human Digital Twin Example
• How to Build Digital Twins: What You Need to Know

When this talk is complete the audience will understand that Digital Twins are a means to an end and not an end in themselves. They are expensive to build and must have a predetermined purpose so that their value is understood before endeavoring to build them. This understanding will be fostered through the audience participation nature of the presentation. YouTube and Slides.

May 13, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Fred Adler, University of Utah. Title: Viral Evolution Subgroup Update. YouTube and Slides.
2. John Rice, Dissemination and Outreach Subgroup. Topic: IMAG/MSM Virtual Conference. YouTube and Slides.

May 6, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Natacha Go, Novadiscovery SA, Lyon, France. Title: Application of a mechanistic model to design RTI prophylaxis trials. Abstract: There is a good chance to soon control the COVID-19 pandemic but other respiratory tract infections (RTIs) will continue to impact public health. Viral lower RTIs in children are associated with hospitalization, wheezing and asthma inception, while upper RTIs are less severe but have a high prevalence. Viral RTIs are also triggers for exacerbations of chronic pulmonary diseases. Vaccination against prevalent viruses like RSV and RV are currently neither available nor in the near future and therefore non-specific immunomodulation for RTI prophylaxis is promising to fill this gap.  For example the oral bacterial lysate Broncho-Vaxom (OM-85) has demonstrated efficacy efficacy in prevention of recurrent RTIs, specifically in at-risk pediatric population (in et al., 2018; DOI:10.1016/j.intimp.2017.10.032). For targeting other populations and RTI-indications, robust efficacy data need to be generated, but clinical trials are strongly impacted by the pandemic. Globally, all trials other than dedicated to COVID-19 are experiencing delays or even halts, e.g. due to patient recruitment issues. At the same time, RTI burden changes - through lockdown or social distancing - with an uncertain trajectory. If at all and how RTI prophylaxis trials are feasible to conduct in the near future is a completely open question. 
   For a better design of RTI prophylaxis trials in the currently moving frame, we have developed a dedicated multiscale in silico approach. A mechanistic pharmacokinetics  /pharmacodynamics and within-host viral infection disease model is interfaced with a population-scale (between-host) SIRS disease burden model - thereby accounting for seasonality and extrinsic factors through time-dependent transmission. On the back of this model and a Virtual Population we conduct in silico clinical trials with variations in observational periods, eligibility criteria (defining the included at-risk population) and follow-up giving us efficacy metrics and sample size estimates as outputs. We demonstrate how the model can be used to address and rationalize efficacy heterogeneity in clinical data through links between population, follow-up, regimen and clinical efficacy. The integrated SIRS was then used to mimic lockdown as COVID-19 containment in line with RCGP 2019-2020 data, allowing us to adapt the  instantaneous control group prevalence and efficacy dependent on this modulation. We also show how and why different containment scenarios vary in their impact of demonstrated efficacy, recruitment needs and difficulty through analyses of the predicted outcome distributions. On a longer term, we wish to highlight the capability of computational systems biology for RTI prophylaxis trial design under rapidly changing conditions and that such a modeling approach can supporting go-no/go decisions in clinical development for a wide range of RTI-prophylaxis oriented indications. YouTube and Slides.
2. Joshua T. Schiffer, Fred Hutchinson Cancer Research Center, University of Washington, Seattle. Title: Modeling SARS-CoV-2 shedding, therapy and transmission. Abstract: I will describe our group’s multi-scale modeling of SARS-CoV-2. Our models include an intra-host model which captures viral load trajectories and immune responses and can be used as a tool to simulate clinical trials, a model intended to capture the impact of viral load on transmission and super-spreader events as well as the impacts of masking and vaccination on transmission, and an epidemiologic model of SARS-CoV-2 transmission in King County Washington that captures the impact of vaccination, non-pharmaceutical interventions and novel SARS-CoV-2 variants of concern. The Fred Hutchinson Cancer Center web page on viral modeling in King County Washington is at https://covidmodeling.fredhutch.org/. YouTube and Slides.

April 29, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Penelope Morel, MD, University of Pittsburgh. Title: The immune response to SARS-CoV-2: Friend or Foe? Abstract: The novel SARS-CoV-2 coronavirus is responsible for worldwide pandemic that has infected over 145 million people resulting in over 3 million deaths. The immune response to SARS-CoV-2 involves both innate and adaptive responses and it appears that the timing and magnitude of these responses are important factors in determining the outcome of the infection.  For the vast majority of those infected by SARS-CovV-2 develop classic anti-viral immunity leading to a mild clinical course. The picture is very different for the 10% of infected individuals who develop serious disease, which can lead to respiratory failure, multi-organ failure and death. This is associated with a hyperinflammatory state, with high levels of circulating cytokines, and a failure of the adaptive immune response. The development and distribution of effective vaccines is beginning to have an impact on infections and disease rates. However, questions remain unanswered concerning the longevity of immunity, the impact of new viral variants and the emergence of long-hauler disease.  In this talk we will examine how the timing and magnitude of the immune response to SARS-CoV-2 impacts disease outcomes, and how modeling may provide new insights on disease and therapeutics. YouTube and Slides.
2. Katherine L. Morse, IEEE Fellow, Principal Professional Staff, JHU/APL. Title:  Standards and standards development. Abstract: 

• Existing relevant SISO standards and standards development activities: DSEEP, HLA, FEAT, Simulation Interoperability Readiness Levels (SIRL);
• How standards processes work, using SISO as an example;
• SISO standards and standards development activities that aren’t germane to biomed, but fit in the same niche in a standards portfolio where they have gaps as a way of illustrating how these gaps can and are filled. 
  YouTube and Slides.

April 22, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Debra Peters, USDA, Los Cruces. Title: Cross-scale interactions and catastrophic events: towards a predictive model of the spread of vector-borne diseases. Abstract: Predicting the processes and environmental drivers of incursion and expansion of vector-borne diseases is a major challenge that needs to be met for geographically-extensive diseases leading to catastrophic events. In many cases, spread of disease is mediated by spatial and temporal heterogeneity in climate and other environmental drivers where geospatial data are increasingly available. These data can be used as part of a predictive disease ecology paradigm provided the diverse data can be synthesized and harmonized with fine-scale, highly-resolved data on vector and host responses to their environment. A multi-scale big data-model integration approach using human-guided machine learning was developed to objectively evaluate the importance of a large suite of spatially-distributed environmental variables (several hundred) to the spread of vector-borne diseases. Vesicular stomatitis (VS) and West Nile provide examples of this approach where new insights about the underlying processes driving disease dynamics were elucidated. YouTube and Slides.

• Ruchira Datta posted links to two articles from Dr. Peters presentation:
  • https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3157
  • https://esajournals.onlinelibrary.wiley.com/doi/10.1002/fee.2194

1. C. Donald Combs, PhD, FSSH, Vice President and Dean, School of Health Professions, Eastern Virginia Medical School. Title: The Digital Patient Project  Abstract: Twenty-five years ago physiologists around the world began to work explicitly toward the creation of a virtual physiological human. Fifteen years ago, the European Union funded the Discipulus Project, which developed a roadmap toward the creation of a digital patient before the project ended in 2012. Since then, a number of research teams have continued to develop whole body simulation tools such as BioGears, HumMod and Muse.  Recently, a few researchers have begun to discuss the concept of digital twinning as it might apply to health and healthcare. The Digital Patient Project intends to build on the work of these predecessors to update and expand that research.  We will address questions related to the creation of a platform integrating data from the molecular to the neighborhood and community. Data used will incorporate individual patient data and as well as data gleaned from published clinical and population health research. Ultimately, the Digital Patient Platform will be deployed in the analysis, diagnosis and treatment of both individual patients and communities. YouTube and Slides.

April 15, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Winston Garira, University of Venda, South Africa. Title: A THEORY FOR MULTISCALE MODELLING OF DISEASE DYNAMICS. Abstract: Most scientific fields have been made over with a revolutionary theory at least once in recent centuries. Such paradigm shifts reorder old knowledge into a new framework. Revolutionary theories succeed when the new framework they introduce makes it possible to solve problems that challenged the previous intellectual regime. In this talk, I will discuss about a theory for multiscale dynamics of infectious diseases. This theory reorders old scientific knowledge of disease dynamics based on transmission mechanism theory into a new framework based on the multiscale dynamics of infectious disease called the replication-transmission relativity theory. The replication-transmission relativity theory states that at every level of organization of an infectious disease system, there is no privileged or absolute scale which would determine disease dynamics, only interactions between the microscale and macroscale.  It identifies an infectious disease system as a complex system which is organized into seven main hierarchical levels at which host-pathogen interactions can play out. Describing the multiscale dynamics of an infectious in its entirety as a complex system is a mammoth task. The replication-transmission relativity theory enables us to bring down the complexity of an infectious disease system to manageable levels by discretizing or decomposing the infectious disease system into hierarchical  levels of organization, each of which, can analyzed  independently using multiscale modelling methods. This theory ripped the entire fabric of classical transmission mechanism theory which has been in existence at least since Daniel Bernoulli developed a dynamic model of smallpox transmission and control in 1760,  which was later unified by  Kermack and McKendrick in their seminal work, into an idea  now more widely known as mathematical epidemiology.  It demolished the notion that transmission is the only main dynamic process in infectious disease dynamics.  I anticipate that the replication-transmission relativity theory will remain firmly established as the fundamental theory on which multiscale modelling of infectious disease dynamics is based on from the cell level to the macro ecosystem level. Therefore, with a theory in place, we expect that multiscale modelling of infectious disease systems will evolve and expand in scope. YouTube and Slides. 
    
2. Vivek Shenoy: University of Pennsylvania. Title: Dynamic fibroblast contractions attract remote macrophages in fibrillar collagen matrix. Abstract: Macrophage (Mϕ)-fibroblast interactions coordinate tissue repair after injury whereas miscommunications can result in pathological healing and fibrosis. We show that contracting fibroblasts generate deformation fields in fibrillar collagen matrix that provide far-reaching physical cues for Mϕ. Within collagen deformation fields created by fibroblasts or actuated microneedles, Mϕ migrate towards the force source from several hundreds of micrometers away. The presence of a dynamic force source in the matrix is critical to initiate and direct Mϕ migration. In contrast, collagen condensation and fiber alignment resulting from fibroblast remodeling activities or chemotactic signals are neither required nor sufficient to guide Mϕ migration. Binding of α2β1 integrin and stretch-activated channels mediate Mϕ migration and mechanosensing in fibrillar collagen ECM. We propose that Mϕ mechanosense the velocity of local displacements of their substrate, allowing contractile fibroblasts to attract Mϕ over distances that exceed the range of chemotactic gradients. YouTube and Slides.

Video from the post-presentation discussion is here.

April 8, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. James Moore, Jr.: Department of Bioengineering, Imperial College London. Title:  Biotransport Mechanisms for Adaptive Immunity. Abstract: Lymph nodes are immune information collection and transfer junctions, where immune cells are housed in sufficient numbers and varieties to develop effective immune responses.  It is therefore the job of the lymphatic system to gather information, including free antigen, antigen presenting cells, cytokines and other immune cells/signals, from the periphery and deliver them to the node for further processing.  This requires an exquisitely complex combination of active pumping, cell/antigen transport, and biological signaling between multiple cell types.  We have performed a series of experiments and computational simulations of flow patterns in afferent lymphatic vessels and lymph nodes.  Agent-based models have been developed to explore the role of lymph node swelling in T cell population dynamics.  Pumping dynamics of afferent vessels, and therefore the delivery of immune information to lymph nodes, are strongly affected by variations in local pressure, including the external pressure and vasoactive effects of vaccinations.  A better understanding of these transport phenomena could lead to more relevant criteria for designing methods to modulate the immune system effectively for health benefit, including vaccines. YouTube and Slides. 
    
2. Denise Kirschner: University of Michigan.Title: A systems biology approach to understanding the immunobiology of tuberculosis infection and treatment. Abstract: Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb), the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. The structure of granulomas, lesions forming in lungs in response to Mtb infection, create heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb. We can use a systems biology approach pairing experimental data from non-human primates with computational modeling to represent and predict how factors impact antibiotic regimen efficacy and granuloma bacterial sterilization. We utilize an agent-based, computational model that simulates granuloma formation, function and treatment, called GranSim. A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. With the number of potential combinations of antibiotics and dosages, it is prohibitively expensive to exhaustively search all combinations to achieve these goals. We present a framework to search for optimal regimens using GranSim. Overall, we present a computational tool to evaluate antibiotic regimen efficacy while accounting for the complicating factors in TB treatment to strengthen our ability to predict new regimens that can improve clinical treatment of TB. YouTube and Slides.

Todays presentations generated a large number of links to papers.

Thank you to Dr. Ruchira Datta for posting to the chat. Below is the list of links along with the titles.

1. Lymphatic System Flows https://www.annualreviews.org/doi/abs/10.1146/annurev-fluid-122316-045259
2. Modeling Lymph Flow and Fluid Exchange with Blood Vessels in Lymph Nodes https://pubmed.ncbi.nlm.nih.gov/26683026/
3. Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model https://pubmed.ncbi.nlm.nih.gov/15501468/
4. Synergy between Individual TNF-Dependent Functions Determines Granuloma Performance for Controlling Mycobacterium tuberculosis Infection https://www.jimmunol.org/content/182/6/3706
5. A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740747/
6. The association between sterilizing activity and drug distribution into tuberculosis lesions https://pubmed.ncbi.nlm.nih.gov/26343800/
7. In silico evaluation and exploration of antibiotic tuberculosis treatment regimens https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4650854/
8. Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas https://www.frontiersin.org/articles/10.3389/fphar.2020.00333/full
9. Applying optimization algorithms to tuberculosis antibiotic treatment regimens https://pubmed.ncbi.nlm.nih.gov/29276546/
10. CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models https://pubmed.ncbi.nlm.nih.gov/33643465/
11. Evaluation of a Mouse Model of Necrotic Granuloma Formation Using C3HeB/FeJ Mice for Testing of Drugs against Mycobacterium tuberculosis https://aac.asm.org/content/56/6/3181
12. Quantifying the limits of CAR T-cell delivery in mice and men https://pubmed.ncbi.nlm.nih.gov/33653113/
13. Lymph node metastases can invade local blood vessels, exit the node and colonize distant organs in mice https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6002772/

April 1, 2021 Meeting

Slides: Are here for the 11AM and here for the 3PM (these are the WG's slides, the presentation's slides are linked with the speakers below).

The post presentations discussion is available on YouTube.

Presentations:

SPECIAL TIME 11 AM (Eastern)

1. Rita Colwell, University of Maryland Institute for Advanced Computer Studies. Title: CLIMATE, OCEANS, AND HUMAN HEALTH: What Cholera can teach us about COVID-19 Abstract: Models developed for understanding and predicting outbreaks of cholera, based on work done in the Chesapeake Bay and the Bay of Bengal, today assist UNICEF and aid agencies in predicting risk of cholera in Yemen and other countries of the African continent.  With onset of COVID-19, the models were modified and used to predict risk of COVID-19, the current pandemic of coronavirus.  Thus, molecular microbial ecology coupled with computational science and remote sensing can provide a critical indicator and prediction of human health and wellness. YouTube and Slides.

Regular Time 3 PM (Eastern)

1. John Yin, UW-Madison Title: Kinetics of virus growth in cells. Abstract: This mini-talk will provide a brief history of modeling virus growth in cells. The talk will also aim to highlight challenges and opportunities for experimentalists and modelers to productively collaborate in this exciting and rapidly growing field. YouTube and Slides.
2. Ron Milo, Weizmann Institute of Science. Title: The total number and mass of SARS-CoV-2 virions in an infected person.  Abstract: Quantitatively describing the time course of the SARS-CoV-2 infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the typical viral load of SARS-CoV-2 in bodily fluids and host tissues to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109-1011 virions during peak infection, with a total mass in the range of 1 μg-0.1 mg, which curiously implies that all SARS-CoV-2 virions currently circulating within human hosts have a collective mass of only 0.1-10 kg. We combine our estimates with the available literature on host immune response and viral mutation rates to demonstrate how antibodies outnumber the spike proteins and the genetic diversity of virions in an infected host covers all possible single nucleotide substitutions. YouTube and Slides.

March 25, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Jacob Barhak, Multiscale Modeling and Viral Pandemics Working Group, Title: Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility 
   Abstract:  A white paper draft by the Model Reproducibility, Credibility and Standardization subgroup and the Integration subgroup. Link to white paper draft: 
   https://docs.google.com/document/d/1IMEgmdNkx-EsnOjGuegpenSIMmKIkK00Lc8Gred3QxM/edit?usp=sharing  YouTube 
    
2. David Odde, Dept. of Biomedical Eng., U. Minnesota. Title: Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets. 
   Abstract: Effective therapies for COVID-19 are urgently needed. Presently there are thousands of COVID-19 clinical trials globally, many with drug combinations, resulting in an empirical process with an enormous number of possible combinations. To identify the most promising potential therapies, we developed a biophysical model for the SARS-CoV-2 viral cycle and performed a sensitivity analysis for individual model parameters and all possible pairwise parameter changes (162 = 256 possibilities). We found that model-predicted virion production is fairly insensitive to changes in most viral entry, assembly, and release parameters, but highly sensitive to some viral transcription and translation parameters. Furthermore, we found a cooperative benefit to pairwise targeting of transcription and translation, predicting that combined targeting of these processes will be especially effective in inhibiting viral production. I will discuss how the model has fared in light of clinical trial results, and current applications. YouTube and Slides.

March 18, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Yannis Kevrekidis, Johns Hopkins University. Title: No equations, no variables: data driven (and physics informed) dynamic models 
   Abstract: I will discuss some current developments on obtaining data driven models of agent-based systems. The focus will be on (a) finding good latent spaces (good "observables") from complicated, disorganized measurements and (b) learning dynamical equations for the evolution of these observables (through different ML tools). In particular, I will discuss the idea of creating useful embedding spaces for problems that involve dynamics on (possibly evolving) networks. YouTube and Slides.
2. Ashlee Ford Versypt, Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York. Title: Multiscale Simulation of Lung Fibrosis Induced by SARS-CoV-2 Infection and Acute Respiratory Distress Syndrome. 
   Abstract: The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge about immune system-virus-tissue interactions and how these can result in low-level infections in some cases and acute respiratory distress syndrome (ARDS) and other tissue damage in others is limited. We are developing an open-source, multi-scale tissue simulator that can be used to investigate mechanisms of intracellular viral replication, infection of epithelial cells, host immune response, and tissue damage. Our model can simulate fibroblast-mediated collagen deposition to account for the fibrosis at the damaged site in response to immune-response-induced tissue injury. The severity of infection and collagen deposition depends on the anti-inflammatory cytokine secretion rate, multiplicity of infection, and contact time for a CD8+ T cell to kill an infected cell. Additionally, the change in the ACE2 receptor concentration from the multiscale model has been used in a separate model of renin-angiotensin system to predict the change in ANGII, which is a biomarker for hypertension, pro-inflammation, and pro-fibrosis.  YouTube and Slides.

March 11, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Robert Stratford, Indiana University School of Medicine.  Title: The role of modeling in the process of drug discovery and development.  
   Abstract: Drug development is a complex, multi-dimensional, process. It is an expensive process, typically requiring investment of > $1 billion dollars, and requiring several years from target identification and molecular scaffold (lead) selection, to eventual clinical development, the latter itself also a multi-year process. Owing to its complexity, drug development is a risky undertaking. In the past two decades, application of modeling and simulation approaches along the continuum of lead selection -> lead optimization -> preclinical candidate development -> clinical development -> regulatory approval has reduced risks and improved efficiency of this multi-phasic and -dimensional process. This brief presentation will present an overview of key modeling and simulation platforms that drive drug discovery and development. Attention will be given to 1) how models improve translational fidelity from pre-clinical experimental models, and 2) how they inform clinical trial design. YouTube and Slides.
2. Veronika Zarnitzyna, Emory University. Title: Innate and Adaptive Immune Response Subgroup Plan. Abstract: We will consider mathematical models of immune response to viral infections.  Some viruses (e.g. influenza) cause acute infection, some (e.g. HIV, herpesviruses such as CMV and EBV) results in persistent infection and some (hepatitis C and hepatitis B) may generate both outcomes in immune-competent humans. Considerable evidence has shown that differential immune response is a major factor in disease outcome in many viral infections. What features of the virus and which factors of the immune system (both innate and adaptive) affect the outcome? What contributes to the heterogeneity in the outcome? Is it possible that SARS-CoV-2 establishes a persistent infection in a fraction of otherwise healthy (at the moment of virus introduction) people? Are long-haulers dealing with organ damage after virus clearance or with low-grade reactivation of the virus? The inherent complexity of the immune system requires modeling approaches in order to reveal the molecular and cellular mechanisms that promote unhealthy or non-optimal immune responses. Moreover, the models will be valuable for expediting the discovery and optimization of immunomodulatory treatments of virus infection. YouTube and Slides.

March 4, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Linig Xu and Yi Jiang, Georgia State University. Title: Modeling Mucociliary Mixing and Transport From Cell to Tissue Scales 
   Abstract: As we breathe, small particles ranging from aerosolized droplets to pollutants enter our airways and are trapped by the thin layer of mucus that coat the airway surface. Mucociliary clearance is the first line of defense of our respiratory system against these potential pathogens and allergens. Periodic beating of cilia from the ciliated cells in the airway epithelium drive the mucus flow and eventually transport these particles out of the airway. We investigate the mixing and transport of these particles using direct 3D numerical simulations coupling the cilia beating and mucus flow. Extending from previous models of single cilium and small clusters of cilia, we model clusters of ciliated cells at a length scale comparable to tissue experiments. Our simulations discover an optimal cilia cluster spacing for efficient transport, and that asynchronous cilia beating (metachrony) tends to inhibit transport and increase mixing. Most relevant to infectious disease is when the pathogens kill the ciliated cells, the decreased local cilia density could significantly reduce the directed particle transport, and effectively increase local pathogen density. The model also shows the spatiotemporal inhomogeneity in particle diffusion: short distance diffusion and long distance clustering, as well as short-time simple diffusion and longer-timescale sub-diffusion and super-diffusion.  These results can inform the spatial models of virus replication and viral-host interaction. YouTube and Slides.
2. Ellen Kuhl, Stanford, Title: Data-driven modeling of COVID-19: Lessons learned.  
   Abstract: Understanding the outbreak dynamics of COVID-19 through the lens of data-driven modeling is an elusive but significant goal. Within the past year, the COVID-19 pandemic has resulted in more than 100 million reported cases and more than 2.5 million deaths worldwide. Unlike any other disease in history, COVID-19 has generated a massive amount of data, well documented, continuously updated, and broadly available. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from one year of COVID-19 modeling. We highlight the early success of classical infectious disease models and show why these models fail to predict the current dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from reported case data to make informed predictions and guide political decision making. We anticipate that this presentation will stimulate discussion within the IMAG community and help provide guidelines for robust mathematical models to understand and manage the COVID-19 pandemic.YouTube and Slides.

February 25, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Rita de Almeida, Universidade Federal do Rio Grande do Sul. Title: Transcriptogram analyses for Sars-Cov1 and 2 Immune Response. YouTube and Slides.

2. James Faeder, University of Pittsburgh. Title: Modeling dynamics of coronavirus and alpha virus infection. 

   Abstract: Most intrahost models of viral infections track virus are built on ordinary differential equations that track viral and cell population but that simplify processes at the intracellular level. While these models have yielded key insights into the factors that affect viral load kinetics and have identified how factor such as timing and mechanism can determine treatment efficacy, there are several questions that require more detailed modeling of interactions at the molecular level. In particular, viral replication products and host signaling pathways interact in numerous ways that determine both the quantitative and qualitative outcomes of infection. Here, I will describe our initial attempts to model viral replication and to embed more detailed kinetic models inside a larger multiscale framework called PhysiCell, that models viral dynamics in a multicellular context. These efforts have been carried in collaboration with a large group of scientists led by Paul Macklin at the University of Indiana. While that effort has been largely concerned with modeling the complex array of cell types that are recruited to the site of infection over the course of several days, we are also interested in modeling the cell type specific induction of interferon responses at the intracellular level and its potential effects for both the local and systemic control of viral infection. This work is in collaboration with Caroline Larkin, William Klimstra, Penelope Morel, Ali Sinan Saglam, Jason Shoemaker, and other investigators in the SARS-CoV-2 Tissue Simulation Coalition. YouTube and Slides.

February 18, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Tomas Helikar, University of Nebraska - Lincoln. Title: Cell Collective: Enabling accessible and collaborative construction and analysis of comprehensive and annotated models Abstract: Cell Collective is a highly accessible online computational modeling platform for the collaborative construction, simulation, and analysis of large-scale dynamic models of biological and biochemical processes. Teams can collaboratively build/simulate models/dynamics within the cloud-based platform. Cell Collective contains public, peer-reviewed mechanistic network models of various biological and biochemical processes in organisms ranging from bacteria and viruses to yeast, flies, plants, and humans. The platform currently supports logical and constraint-based modeling approaches. We have curated nearly 90 previously published logical models for the community to use and build upon. In this presentation, I will also highlight a logical host-pathogen model of influenza-epithelial cell interactions, as well as a cellular-level model of the immune system constructed by our group. Our recent development efforts to facilitate the modeling and analyses of constraint-based models now provide analysis capabilities of genome-scale metabolic models from the BiGG Models and BioModels databases. YouTube and Slides.
2. Marie Ferguson, Project Director at PHICOR. Title: The Value of Reducing the Duration of SARS-CoV-2 Infectious Period Abstract: Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration. Marie Ferguson, Project Director with the Public Health Informatics, Computational, and Operations Research (PHICOR) team headquartered at the City University of New York (CUNY) Graduate School of Public Health and Health Policy (CUNY SPH) will present their study published in PLoS Computational Biology. Since 2007, PHICOR has been researching and developing systems approaches, models, and tools to help decision makers better understand complex issues in health. This has included helping with national response to infectious disease threats ranging from the 2009 H1N1 flu pandemic to the Zika outbreak to the COVID-19 pandemic. YouTube and Slides.

February 11, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Ruchira Datta, Datta Enterprise Enterprises LLC, Approximate Bayesian Computation for Inference with Complex Stochastic Simulations. YouTube and Slides.
2. Lucas Böttcher, UCLA, Title: Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities (arXiv:2101.03467). Abstract: Factors such as non-uniform definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US is 13% higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find negligible or negative excess deaths for part and all of 2020 for Denmark, Germany, and Norway. YouTube and Slides.

February 4, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Morgan Craig, Université de Montréal, Title: Leveraging in silico trials to identify pathological immunological mechanisms leading to severe COVID-19 Abstract: Delineating the pathophysiological processes that contribute to the development of severe COVID-19 is imperative for improving our understanding of the disease, and for developing improved treatment modalities. Because the identification of causal mechanisms can be experimentally and clinically difficult, we developed a mechanistic, within-host mathematical model and virtual patient cohort to understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19. Our findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. YouTube and Slides.
2. Greg Forest, University of North Carolina at Chapel Hill, Title: Physiologically faithful, mechanistic modeling to explain clinical observations from inhaled SARS-CoV-2 exposures. Abstract: We build a model that incorporates the detailed physiology of the upper and lower human respiratory tract (RT), including the physical dimensions of each generation of the airway branch geometry and the alveolar space, the thicknesses of the airway surface liquids (ASLs) and their advection velocity from ciliary propulsion in the lower RT.  We further incorporate the diffusivity of SARS-CoV-2 virions in ASLs, tracking their passage through the ASL to encounter the epithelial tissue, the percent surface coverage of infectable epithelial cells and their probability of infection per encounter with infectious virions, the cell latency post infection followed by replication rate and duration of infectious virions shed back into the ASL. From this detailed model and available bounds on infectivity parameters, assuming the individual has no immune response to this novel virus, we focus on understanding of two clinical observations for this presentation: 1. how a high-titer nasal tract infection develops rapidly (~ 2 days) from inhaled exposure to SARS-CoV-2; and 2. how a nasal infection can, and cannot, propagate to alveolar pneumonia in less than a week.  These results raise critical questions that remain open, and that are under intense investigation by us and additional collaborators. This work is based on collaborations by the following team, with papers in preparation: Alex Chen, Cal State Dominguez Hills, former postdoc with Forest and Lai, working on antibody-virus-mucus interactions. Tim Wessler, U. Michigan, former PhD student of Forest and Lai, working on antibody-virus-mucus interactions. Kate Daftari, current PhD student of Forest and Freeman. Kameryn Hinton, current PhD student of Freeman and Forest. Ronit Freeman, UNC Department of Applied Physical Sciences. Sam Lai, UNC Eshelman School of Pharmacy. Ric Boucher, Director, UNC Marsico Lung Institute. Ray Pickles, UNC Marsico Lung Institute and the speaker, Greg Forest, UNC Mathematics, Applied Physical Sciences, and Biomedical Engineering. YouTube and Slides.

January 28, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

YouTube: 

Presentations:

1. Marek OSTASZEWSKI, University of Luxembourg, and Anna Niarakis, Université Paris-Saclay & INRIA Saclay - Île-de-France, Title: COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms Abstract: We hereby describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied to the contents of the COVID-19 Disease Map for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases. YouTube and Slides. 
   
   Helpful links provided by Dr. Niarakis:

   1. https://disease-maps.org/
   2. https://covid.pages.uni.lu/
   3. https://git-r3lab.uni.lu/covid/models
   4. https://sbgn.github.io/

January 21, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Kevin A. Janes, Department of Biomedical Engineering,University of Virginia 
   Complete kinetic models are pervasive in chemistry but lacking in biological systems.  We encoded the complete kinetics of infection for coxsackievirus B3 (CVB3), a compact and fast-acting RNA virus.  The kinetics are built from detailed modules for viral binding–delivery, translation–replication, and encapsidation.  Specific module activities are dampened by the type I interferon response to viral double-stranded RNAs (dsRNAs), which is itself disrupted by viral proteinases.  The validated kinetics uncovered that cleavability of the dsRNA transducer mitochondrial antiviral signaling protein (MAVS) becomes a stronger determinant of viral outcomes when cells receive supplemental interferon after infection.  Cleavability is naturally altered in humans by a common MAVS polymorphism, which removes a proteinase-targeted site but paradoxically elevates CVB3 infectivity.  These observations are reconciled with a simple nonlinear model of MAVS regulation.  Modeling complete kinetics is an attainable goal for small, rapidly infecting viruses and perhaps viral pathogens more broadly. YouTube and Slides.
2. Rahuman Sheriff, European Bioinformatics Institute, Title: Reproducibility in Systems Biology Modelling, Abstract: From https://www.biorxiv.org/content/10.1101/2020.08.07.239855v1: The reproducibility crisis has emerged as an important concern across many fields of science including life science, since many published results failed to reproduce. Systems biology modelling, which involves mathematical representation of biological processes to study complex system behaviour, was expected to be least affected by this crisis. While lack of reproducibility of experimental results and computational analysis could be a repercussion of several compounded factors, it was not fully understood why systems biology models with well-defined mathematical expressions fail to reproduce and how prevalent it is. Hence, we systematically attempted to reproduce 455 kinetic models of biological processes published in peer-reviewed research articles from 152 journals; which is collectively a work of about 1400 scientists from 49 countries. Our investigation revealed that about half (49%) of the models could not be reproduced using the information provided in the published manuscripts. With further effort, an additional 12% of the models could be reproduced either by empirical correction or support from authors. The other 37% remained non-reproducible models due to missing parameter values, missing initial concentration, inconsistent model structure, or a combination of these factors. Among the corresponding authors of the non-reproducible model we contacted, less than 30% responded. Our analysis revealed that models published in journals across several fields of life science failed to reproduce, revealing a common problem in the peer-review process. Hence, we propose an 8-point reproducibility scorecard that can be used by authors, reviewers and journal editors to assess each model and address the reproducibility crisis. YouTube and Slides.

January 14, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

YouTube:

Presentations:

1. Grace Peng, NIH. Title: "The Structure and Function of IMAG and the Multiscale Modeling Consortium". YouTube and Slides.
2. Guang Lin, Purdue University, and Ehsan Kharazmi, Brown University. Title: "Predicting the COVID-19 pandemic with uncertainties using data-driven models" 
   We have developed an integer-order COVID-19 epidemic model and a fractional-order COVID-19 epidemic model to reconstruct and forecast the transmission dynamics of COVID-19 in New York City. To quantify the uncertainties in the proposed data-driven epidemic model, we have investigated model sensitivity analysis, structural and practical identifiability analysis, model calibration, and uncertainty quantification. We have employed Bayesian model calibration and physics-informed machine learning algorithms to calibrate the model parameters. In the early stage of the outbreak in New York City, the reproduction number was around 4.3, which indicates this outbreak has high transmissibility. We observed that multi-pronged interventions, such as the stay-at-home order and social distancing, had positive effects on controlling the outbreak and slowing the virus's spread. In addition, we employed the proposed data-driven models to evaluate the effects of various strategies to deploy the COVID-19 vaccine to control the pandemic. We have also applied the formulation to infer the dynamics of COVID-19 in other cities/states, where the spread dynamic is different from New York City. YouTube and Slides.

January 7, 2021 Meeting

Slides: Are here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Paul Macklin, Indiana University, "Rapid community-driven development of a SARS-CoV-2 tissue simulator". YouTube and Slides.
2. Bard Ermentrout, University of Pittsburgh, "A model for the the inflammatory response to SARS-CoV-2 in the upper- and lower-respiratory tracts". YouTube and Slides.

December 31, 2020 Meeting

No meeting (New Year's Eve)

December 24, 2020 Meeting

No meeting (Christmas Eve)

December 17, 2020 Meeting

Slides: Are Here (these are the WG's slides, the presentation's slides are linked with the speakers below).

Presentations:

1. Reed Shabman and Lilian Brown, NIH-NIAID, Title: "Data Dissemination and Modeling of Infectious Diseases: A NIAID Perspective".  YouTube and Slides.
   • Present an overview of data generation/data deposition efforts from programs both in our Office and across NIAID
   • Highlight other modeling efforts for infectious diseases in our program and elsewhere
   • Enter into a discussion about data deposition, modeling efforts, and future collaborations with others on the call
2. Filippo Castiglione, National Research Council of Italy, Title: "SARS-CoV-2 infection: a cohort study performed in-silico". YouTube and Slides.
3. Updates from the current subgroups, brief (1~3 minutes) comment from each group's lead(s).

December 10, 2020 Meeting

Slides: Are Here (these are the WG's slides, the presentation's slides are linked with the speakers below).

YouTube: 

Presentations:

1. Amber Smith, University of Tennessee Health Science Center, "Modeling Viral-Bacterial Coinfections" YouTube and slides.

Updates from the WG organizers.

December 3, 2020 Meeting

Slides: https://docs.google.com/presentation/d/1ryugKe4lYvtRfrBaeGWkqEHuVxZLSTaMvsCskWUO4K0/

YouTube: 

Presentations:

1. Jacob Barhak, "The Reference Model Accumulates Knowledge With Human Interpretation" YouTube and slides
2. Gary An, Department of Surgery, University of Vermont, "Biological Heterogeneity and Parameter Space: Using agent-based models to unify knowledge regarding zoonitic transfer, vaccine development and in silico trials of multi-modal therapeutic strategies" YouTube and slides and slides with animations for windows.
   • All too often biological heterogeneity is viewed as a challenge to be overcome. Rather, I propose that phenotypic heterogeneity represents an opportunity for using multi-scale models, and agent-based models (ABMs) in particular, as more generalizable, unifying knowledge representations. Specifically, focusing on the concept of the parameter space (as opposed to parameterization) of an ABM as a means to encompass heterogeneous data allows the ABM to serve as an instantiation of what is “similar” and conserved across different biological systems. I assert that this approach can bridge the gap between species and individuals and provide a useful approach to examining both fundamental aspects of zoonotic transfer of potential pandemic-generating viruses and as an in silico platform for testing biological countermeasures to novel agents. Integrating of machine learning, artificial intelligence and agent-based modeling can serve a critical role in a biothreat response strategy by discovering and evaluating multi-modal therapeutic regimens and vaccine strategies to accelerate and make more efficient their clinical implementation.

November 26, 2020 Meeting

No meeting

November 19, 2020 Meeting

Slides: https://docs.google.com/presentation/d/1g1k3u9CLMVQjL9Ap10pN70-ioCJpGVC79aIjFNslp-Q/edit?usp=sharing

YouTube: 

Presentations:

1. Alan Perelson, Los Alamos National Laboratory, "Overview of what they are doing with respect to SARS-CoV-2 modeling". YouTube
2. Fred Adler, University of Utah, "Rhinovirus, Undergraduate Education, and Multiscale Modeling of Viral Pandemics".  I will briefly discuss our use of rhinovirus (the other common cold) to bridge scales from biochemistry to evolution, and then the structure of a course I am leading that uses the COVID pandemic to motivate mathematical modeling stretching from physics to economics.  I will use these to present my vision of a unified structure linking the full suite of approaches of our group. YouTube and Slides.

General Discussion Highlights:

1. Request everyone to sign up for subgroups at https://forms.gle/DQ38A4H8Aa2VcG3r5 
2. Several people were impressed with Fred Adler's "COVID Landscape" slide. It was suggested that perhaps this could be the basis for a paper with each of the groups listed on the slide contributing details.

November 12, 2020 Meeting

Slides: https://docs.google.com/presentation/d/1ek6XwOjgseu36z5yyC60ijBzO4SPO9jRGKT1yY5wQdo/

YouTube: https://youtu.be/8Z4Z6bZWoic

Presentations:

1. TJ Sego, PhD, Indiana University, "Simulation framework for modeling viral infection and immune response with a special emphasis on extensibility" Abstract: Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository. See also https://pubmed.ncbi.nlm.nih.gov/33347439/ and this model can be run on nanoHUB as described here. Slides are here. YouTube Video.

General Discussion Highlights:

1. Request everyone to sign up for subgroups at https://forms.gle/Vf6RtapTeXfXLBaq6
2. John Rice suggested ~4 pressing questions, one paragraph each, from everyone
   1. Share the document (Jacob Barhak suggested),
   2. Past experience with similar sized group led to discovery of connections through this very simple exercise of expressing 2 or 3 research questions. Thx. Joshua
   3. If everyone reads through them, some of the questions may already be answered, others may suggest critical areas to pursue.
   4. Analysis of the postings using text analysis tools
3. John Rice suggested scheduling a followup zoom for each speaker where interested people can continue the discussion.

November 5, 2020 Meeting

Slides: Are here.

YouTube: Is here.

Presentations:

1. Yaling Liu, PhD, Lehigh University. "Binding of SARS-CoV-2 to cell membranes: Molecular to whole virus simulation and search for potential inhibitors" YouTube Video

October 22, 2020 Meeting

Viral Pandemic Inaugural teleconference. Video is here.