Multiscale Modeling and Viral Pandemics

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Viral Pandemics WG Seminar Series

November 14 at 3:00 PM Eastern time: 

The schedule for this week consists of one presentation:

1. Jan Rombouts, EMBL Heidelberg, will discuss: Beyond mechanisms: High-Level Descriptions As Components Of Mathematical Models In Biology.
   Biological systems are complex. Their behavior is generated by many interacting units, be it molecules, cells or whole organisms. To understand such systems, mathematical modeling is a crucial tool. Often, we do not know the details of the interaction rules, the regulatory networks or cell signaling dynamics that make up a system. However, we may have knowledge about the effective behavior of some components. For example, we may have a measured response curve that is the result of an unknown chemical reaction network, or we have measured an effective cell-cell interaction strength without knowing what leads to this interaction. In such cases, it is possible to use explicit parametrizations of these components as parts of a larger mathematical model. In this talk, I will discuss two distinct examples where we have used such a modeling approach.
       The first example is related to bistable switches in the cell cycle. Transitions in the cell cycle, such as the one from interphase into mitosis, are characterized by fast and irreversible switching and hysteresis. One way to look at the cell cycle is as a chain of bistable switches, corresponding to the transitions between the different phases. A full cell-cycle model that describes the molecular players leading to these different switches quickly becomes unwieldy with lots of unknown parameters. We have proposed an approach to model these bistable switches directly as 'modules' that can be included in a larger model, based on a modification of the classical Hill function. I will explain the method, how we applied it to the cell cycle, and discuss some possible applications in other fields.
       In the second part, I will discuss currently ongoing work on spatial pattern formation in embryonic development. Many multicellular pattern-forming systems are driven by cell rearrangements due to cell-cell interactions. If the mechanism of these interactions is unknown, nonlocal advection-diffusion equations can be used to effectively model such systems. These models are partial integro-differential equations, where cell-cell interactions by are described by an interaction kernel. We combine these models with experiments on randomized cell reaggregates from mouse embryos. These cells form spatial patterns starting from well-mixed initial conditions. In particular, I will discuss how we use our theoretical and experimental tools to investigate how domain geometry affects pattern formation, and how this matters for embryonic development.

Please join at https://iu.zoom.us/meeting/register/tZYqd-2srD8tGtCXDem4Cka08rBz5fDW0EQR

If you are interested in presenting a talk, please let us know.

Furthermore, if you have suggestions for potential speakers please fill out a Suggested Invitees Google Form. We will do our very best to schedule your potential seminar or to invite speakers according to your suggestions as soon as possible.

Upcoming Seminars:

November 28: 

1. US Holiday, no meeting.

December 12: 

1. Valeriu Damian.

January 9, 2025:

1. Martina Iulini, Uni Milan. Using NAMs to address PFAS immunotoxicity: an integrated in vitro and in silico testing strategist to study the effect on the antibody production

January 23, 2025:

1. TBD

February 6, 2025:

1. Adam Knapp, University of Florida.

For details of the past and future teleconferences, including YouTube videos and slides, 
please visit this page. 
You can access the entire set of Viral Pandemic WG seminars at our YouTube Playlist 
(click the "Subscribe" button if you would like to be notified when new videos are posted)

IMAG's COVID-19 modeling and data resources page is here.

Three VP Wiki pages: 
(1) Publications, (2) Random but Relevant Things and (3) Viral Pandemics WG Runnable Models Page

Recent Publications from group members (2024 only):

1. Böttcher L, Fonseca LL, Laubenbacher RC. Control of Medical Digital Twins with Artificial Neural Networks. bioRxiv [Preprint]. 2024 Mar 20:2024.03.18.585589. doi: 10.1101/2024.03.18.585589. PMID: 38562787; PMCID: PMC10983973. https://www.biorxiv.org/content/10.1101/2024.03.18.585589v1
2. OPINION: Medical ‘Digital Twins’ Will Lead the Way to Personalized Medicine. Reinhard C. Laubenbacher. Scientific American, May 14, 2024. https://www.scientificamerican.com/article/how-digital-twin-technology-harnesses-biology-and-computing-to-power/
3. Gary An, Chase Cockrell. A design specification for Critical Illness Digital Twins to cure sepsis: responding to the National Academies of Sciences, Engineering and Medicine Report: Foundational Research Gaps and Future Directions for Digital Twins. 8 May 2024. arXiv. https://doi.org/10.48550/arXiv.2405.05301.
4. Reinhard Laubenbacher,  Frederick Adler,  Gary An,  Filippo Castiglione,  Stephen Eubank,  Luis L. Fonseca, James Glazier,  Tomáš Helikar,  Marti Jett-Tilton,  Denise Kirschner,  Paul Macklin,  Borna Mehrad,  Bethany Moore,  Virginia Pasour,  Ilya Shmulevich,  Amber Smith,  Isabel Voigt,  Thomas Yankeelov,  Tjalf Ziemssen. Toward Mechanistic Digital Twins: Some Use Cases. Front. Digit. Health, Sec. Health Informatics, Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1349595 . (Provisionally accepted https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1349595/abstract)
5. Laubenbacher R, Adler F, An G, Castiglione F, Eubank S, Fonseca LL, Glazier J, Helikar T, Jett-Tilton M, Kirschner D, Macklin P, Mehrad B, Moore B, Pasour V, Shmulevich I, Smith A, Voigt I, Yankeelov TE, Ziemssen T. Forum on immune digital twins: a meeting report. NPJ Syst Biol Appl. 2024 Feb 16;10(1):19. doi: 10.1038/s41540-024-00345-5. PMID: 38365857; PMCID: PMC10873299. https://www.nature.com/articles/s41540-024-00345-5

Media about group members or the group's activities:

1. James Glazier's NOKIA interview podcast "Building the Star Trek Med-Bay" is available here!
2. Son's health issues fuel UNL researcher's interest in building virtual immune system, Omaha World-Herald, Feb 27, 2022. Newspaper article about Tomáš Helikar's work on digital twins of the immune system, with additional commentary from James Glazier and Reinhard Laubenbacher.
3. Medical Digital Twins: a New Frontier, ACM News Feb 24, 2022. ACM news article about Reinhard Laubenbacher's recent paper.

Recent Publications from group members (2022-2023 only):

Co-Leads:

James A. Glazier, PhD, Indiana University, JAGlazier@gmail.com

Tomas Helikar, PhD, University of Nebraska-Lincoln, thelikar2@unl.edu

Rationale: The ongoing COVID-19 pandemic has provided a striking example of the real-world importance of mathematical and computational modeling. Epidemiological simulations have become key technologies for optimizing responses by clinicians and policy makers around the world. Because epidemiological models had already been developed and validated for other infectious diseases before the appearance of SARS-CoV-2, these models were available for rapid repurposing when the COVID-19 crisis started. As a result, sophisticated epidemiological models of COVID-19 are informing healthcare professionals and public leaders as they decide on social restrictions or resource allocation.

However, once a patient is infected with SARS-CoV-2 (or another virus), current modeling technology is not sufficiently advanced to be of much help in assessing risk or guiding treatment. The spread of infection within the body and the immune response to respiratory and other viral pathogens is still poorly understood. The factors determining the beneficial (viral clearance) and harmful (cytokine storm) effects of immune response to COVID-19 are poorly understood. COVID-19 is also a very complex disease, with pathologies developing in organs beyond the sites of primary infection, and thus requiring understanding of the responses of multiple organ systems (especially the vasculature and blood) and their interactions. As the disease progresses over the course of weeks and months, coinfection between respiratory viruses is likely and its significance poorly understood. Therapies under consideration are also complex, with possible combination therapies combining phased dosages of novel and existing antivirals, pro-and anti-inflammatory drugs and antibodies. Such therapies will need to be personalized, and their combinatorial complexity makes evaluation with clinical studies which do not employ modeling for prioritization prohibitively time consuming and costly. The complex pattern of comorbidities to COVID-19 is suggestive but their significance is still unclear. Minority populations are at much greater risk both of being infected with SARS-CoV-2 and of dying from COVID-19, once infected. It is an urgent matter of health equity to understand and remediate these vulnerabilities. While the former (susceptibility) are primarily the subject of epidemiological models, the latter (death rates) are appropriate topics for the models the Working Group will consider. The COVID-19 epidemic has also brought modeling of infections into popular consciousness, education at all levels and policy making to an unprecedented degree. The development of models of human infection and response provide great opportunities for education and dissemination.

We currently lack platform tools for the evaluation of viral infections, their responses, and treatment opportunities like the epidemiology models that were available to customize for COVID-19. With partial exceptions for influenza and HIV/AIDS, few computational models attempt to collectively understand and harness the key drivers of infection progression for prognosis and optimal design of interventions. Even the most sophisticated current models do not usually include the details of immune response and inter-organ interactions that seem critical to COVID-19 and will be essential if the models are to serve as a platform for response to future viruses. Another obstacle to using modeling as a guide to treatment is the relative lack of ability to personalize such models using readily available data, such as lung CT scans, immune profiles from repeated blood draws, or comorbidities and other information from patient health records. This personalization of predefined and calibrated models is key, as COVID-19 has shown with its unpredictability of patient response. Added challenges are a better understanding of host-pathogen interactions, and the connection between the population and patient-level scales. These and other challenges make clear that the determination of an effective response to viral pandemics is a multiscale many-faceted problem whose solution has to rely on multiscale mathematical and computational models. The IMAG community is ideally positioned to lead an initiative to develop and help execute a strategy for developing and applying this technology.

Focus and structure of the working group: The community of modelers developing epidemiological and population-scale models is already extensive and well-integrated, in part due to the NIGMS MIDAS program. Within-host modeling of viral pathogens is much more limited. Therefore, the working group will initially focus on within-host scales, in particular the complex interactions between viral infection, host physiology, and the immune system. A main long-term deliverable of the working group will be an overall strategy for a coordinated multi-scale modeling effort which becomes a customizable translational technological platform for rapidly creating improved personalized prognoses and therapies in response to emerging viral pandemics. It will also include a plan on how to mobilize and coordinate the modeling community to support this effort.

The working group will be organized as follows:

A steering group, consisting of approximately 20 scientists, including modelers, data scientists, experimental and clinical domain experts, such as immunologists and virologists and representatives of affected communities and potential tool users, with special emphasis on the effects of the disease on disproportionately affected populations.

The steering group and subgroups will contain scientists from academia, the private sector, and government, also relying heavily on the members of the IMAG working groups. The working group will be widely advertised, and membership is open to all scientists. In addition, the co-leads will proactively invite scientists to join the steering group.

Member List is available here.