This webinar series was recorded and archived below

Wednesday, November 14, 2012 at 12:30pm ET

Modeling cardiac function and dysfunction

Natalia Trayanova, PhD, Johns Hopkins University

Simulating cardiac electrophysiological function is one of the most striking examples of a successful integrative multi-scale modeling approach applied to a living system directly relevant to human disease. This presentation showcases specific examples of the state-of-the-art in cardiac integrative modeling, including 1) improving ventricular ablation procedure by using MRI reconstructed heart geometry and structure to investigate the reentrant circuits formed in the presence of an infarct scar; 2) developing a new out-of-the box high-frequency defibrillation methodology; 3) understanding the contributions of non-myocytes to cardiac function and dysfunction, and others.

Archived Recording: https://webmeeting.nih.gov/p75536528/

Monday, September 17, 2012 at 1pm EDT

Multi-Scale Modeling of Sickle Cell Anemia

Dr. George Karniadakis

Talk slides

Sickle cells exhibit abnormal morphology and membrane mechanics in the deoxygenated state due to the polymerization of the interior sickle hemoglobin (HbS). We study the dynamics of self-assembly behavior of HbS in solution and corresponding induced cell morphologies by dissipative particle dynamics approach. A coarse-grained HbS model, which contains hydrophilic and hydrophobic particles, is constructed to match the structural properties and physical description (including crowding effects) of HbS. The hydrophobic interactions are shown to be necessary with chirality being the main driver for the formation of HbS fibers. In the absence of chain chirality, only the self-assembled small aggregates are observed whereas self-assembled elongated step-like bundle microstructures appear when we consider the chain chirality. Several typical cell morphologies (sickle, granular, elongated shapes), induced by the growth of HbS fibers, are revealed and their deviations from the biconcave shape are quantified by the asphericity and elliptical shape factors.We then use these sickle cells to study the rheological properties of sickle blood and the adhesive dynamics between red blood cells, white cells, and the arterial wall in small arterioles.

Archived Recording: https://webmeeting.nih.gov/p78189808/

Friday September 7, 2012 at 1:00pm EDT

Probabilistic Analysis in Biomechanics

Dr. P. Laz

Center for Orthopaedic Biomechanics, University of Denver

http://www.du.edu/biomechanics

Many sources of variability are inherently present in biomechanics. Intersubject variability includes differences in the geometry and mechanical properties of anatomical structures, as well as in loading and joint mechanics. When considering orthopaedic implants, alignment of the components also contains significant variability. Probabilistic analysis provides a framework to assess the impact of these sources of variability on performance measures. Specifically, these techniques quantify a distribution or bounds of performance and identify the most important parameters and or combinations of parameters that influence performance.

This webinar will provide an overview of probabilistic modeling techniques and highlight their use in a series of clinical and biomechanical applications: effects of implant alignment variability on joint mechanics, statistical shape modeling to characterize intersubject variability and perform population-based evaluations, and relationships between shape and function in the natural knee. An improved understanding of the role of variability can provide insight into normal and pathologic joint mechanics and aid in the design of implants that are robust to patient and alignment variability.

Archived Recording: https://webmeeting.nih.gov/p21856578/

Friday July 13, 2012 at 1:00pm EDT

Multi-scale Image-based models for CFD simulations of pulmonary air flow

Dr. Youbing Yin

Abstract

Computational fluid dynamics (CFD) has become an attractive tool in understanding the characteristic of air flow in the human lungs. Due to inter-subject variations, subject specific simulations are essential for understanding structure-function relationship, assessing lung function and improving drug delivery. However, currently the subject specific CFD analysis remains challenging due, in large part to, two issues: construction of realistic deforming airway geometry and imposition of physiological boundary conditions. This presentation will first describe a mass-preserving nonrigid registration algorithm for matching three-dimensional (3D) MDCT lung images. We further demonstrate the ability to develop realistic, subject-specific dynamic lung models by utilizing the proposed registration method in order to address these two issues above. The proposed lung model combines both the 3D and 1D airway trees, considers the regional ventilation from a local voxel to global sub-lung regions, and accounts for turbulent-transitional-laminar flows, thus accounting for the nature of the multiscale in pulmonary air flow. Additionally, we developed image processing pipelines to evaluate CT repeatability, link MDCT-MRI lung images, build micro-CT-based acinar models, and study lobar sliding and FEM-based lung mechanics.

Archived Recording: https://webmeeting.nih.gov/p69936260/

Friday June 8, 2012 at 1:00pm EDT

Specification, Construction, and Exact Reduction of State Transition System Models of Biochemical Processes, Scott M. Bugenhagen and Daniel A. Beard, PhD

Biochemical reaction systems may be viewed as discrete event processes characterized by a number of states and state transitions. These systems may be modeled as state transition systems with transitions representing individual reaction events. Since they often involve a large number of interactions, it can be difficult to construct such a model for a system, and since the resulting state-level model can involve a huge number of states, model analysis can be difficult or impossible. In this presentation, we introduce methods for the high-level specification of a system using hypergraphs, for the automated generation of a state-level model from a high-level model, and for the exact reduction of a state-level model using information (viz. symmetries and invariant manifolds) from the high-level model. We then give a tutorial demonstration of the practical application of the methods to the modeling of biochemical reaction systems using several examples constructed using Vernan, a MATLAB tool implementing the methods.

Archived Recording: https://webmeeting.nih.gov/p65832122/

Friday May 11, 2012 at 1:00pm EDT

Synergistic Use of Data-based and Hypothesis-based Modeling of Biomedical Dynamic Systems, Vasilis Z. Marmarelis, Ph.D.

The inductive (data-based) and the deductive (hypothesis-based) approaches have played a complementary and mutually beneficial role in the history of science, whereby observations have led to the postulation of hypotheses that are subsequently tested by properly designed experiments. This forms an evolutionary process of hypothesis formulation and testing, leading to scientific advancement. In life sciences and medicine, the importance of discovering and quantifying the physiological mechanisms under normal and pathological conditions has given rise to mechanism-based modeling methods (e.g. compartmental or structural modeling) which rely on the current state of understanding of the system under study. However, the intrinsic complexity of physiological systems and the need for validation of the structural models present formidable challenges in the mechanism-based approach and motivate the complementary use of data-based modeling approaches (typically input-output or stimulus-response formulations). The latter seek to capture the essential functional characteristics of the physiological system in a manner consistent with the available data. Subsequent analysis of the obtained data-based models suggest hypothesis-based model forms that encapsulate the relevant physiological mechanisms and can be tested through properly designed experiments. In this process, the data-based model serves as “ground truth” for the validity of an equivalent hypothesis-based or mechanism-based model. Our experience over the last 30 years shows that this “virtuous cycle” of model development is enabled by the synergistic use of data-based and hypothesis-based approaches.

The study of functional and structural complexity in living systems requires reliable and robust modeling tools in a hierarchical context of multiple scales of time and space. Although mechanism-based models remain the ultimate objective of multi-scale modeling, data-based models can be helpful in pursuing this goal because of their applicability to arbitrary levels of systemic organization from molecular to cellular to multi-cellular to organ to multi-organ etc. This broad applicability depends on appropriate methods of modeling/analysis within the constraints imposed by experimental limitations. This talk seeks to stimulate our thinking on the synergistic use of data-based and hypothesis-based modeling methods in a practical context. It will summarize our findings to date and will present illustrative examples from neural and metabolic systems where this synergistic approach has yielded useful insights.

Archived Recording: https://webmeeting.nih.gov/p79925002/

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