Multiscale Modeling Webinars

From IMAG_Wiki

Link to WG Presentation Schedule

Upcoming Presentations

Past Presentations

• Thurs Jan 24 at 3:00pm ET:

A status update on COMBINE standardization activities, with a focus on SBML

Mike Hucka, Member of the Professional Staff, Computing + Mathematical Sciences, California Institute of Technology

Abstract:

A vast number of modeling and simulation software tools are available today for research in computational systems biology. This wealth of resources is a boon to researchers, but it also presents interoperability problems. Different software tools for systems biology are implemented in different programming languages, run on different operating systems, express models using different mathematical frameworks, provide different analysis methods, present different user interfaces, and support different data formats. Despite working with different tools, researchers want to disseminate their work widely, as well as reuse and extend the models of other researchers. They do not want to hardcode their models as software programs, nor assume everyone uses the same computing environment; they need common exchange formats for representing their models in such a way that a variety of software systems can read and write them.

There exist a number of standardization efforts today with the goal of developing and evolving exchange formats for computational systems biology; they differ along dimensions such as domain of specialization and medium of communication. Many of these efforts are engaged in COMBINE (the COmputational Modeling in BIology NEtwork; http://co.mbine.org), an organization whose main goal is to help coordinate community standardization activities. In this presentation, I will summarize the goals of the core standards represented in COMBINE, and provide details about recent developments in certain ones with probable relevance to multiscale modeling, particularly SBML (Systems Biology Markup Language), as well as SED-ML (Simulation Experiment Description Markup Language) and SBGN (the Systems Biology Graphical Notation).

Hosted by the Model and Data Sharing Working Group

Archived Presentation: https://webmeeting.nih.gov/p35121409/

Presentation Slides: Link

• Wednesday November 28, 1-2pm ET

New Awardee: Xiaobo Zhou, Ph.D.

Center for Translational Bioinformatics and Systems Biology, The Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University

Modeling the Growth of the MIC Niche at the System Level

ABSTRACT: Our long-term goal is to develop coherent experimental protocols and mathematical models for understanding the biomechanical interaction between myeloma-initiating cells (MICs, also known as myeloma stem cells) and bone marrow stromal cells (BMSCs, also known as bone marrow derived mesenchymal stem cells) regulating the MIC evolution We have recently shown that 1) BMSCs from myeloma patients (myeloma BMSCs) have higher stiffness and contractibility than normal BMSCs, 2) MICs form more colonies, adhere more tightly and become more resistant to drugs when they are co-cultured with myeloma BMSCs or on stiffer hydrogels, 3) MICs express much higher stromal cell-derivative factor-1 (SDF1) than the mature myeloma cells and 4) treatment of CXCR4 inhibitor, AMD3100, leads to decreased adherence of MICs to myeloma BMSCs and decreased colony formation of MICs. The goals of this study are to more fully characterize how the mechanical properties of myeloma BMSCs are influenced by the SDF1/CXCR4 signaling pathway, and to model the impact of such changes on MIC fate by novel mathematic models. A 3D multi-scale agent-based model (ABM) is proposed to investigate the role of cancer – stroma cell-to-cell interactions in multi-myeloma tumorigenesis. It includes: (a) Intracellular level: The intracellular signaling pathway features of myeloma initiating cells (MICs) and MM associated BMSCs may dominate biomechanically induced Multiple myeloma (MM) cancer cell phenotypes at intercellular level, cancer development and disease prognosis at the tissue level. (b) Intercellular level: Cell-to-cell interactions are the pivot chain linking intracellular level features of MIC and BMSC to intracellular biomechanical phenotype switch of MIC, BMSC, and progenitor cells (PCs) and MM. And (c) Tissue level: The cytokines secreted from MIC, PC and MM in the tissue level will regulate the proliferation and differentiation of MICs. By seamlessly incorporating multi-scale events, we answered how the biomechanical remodeling of cancer stem cell niches via intercellular communications between tumor and stromal cells affects myeloma drug responses and prognosis, provided insights into myeloma development mechanisms, and cast new light on novel drug candidates and treatment strategies targeting the MIC-BMSC interactions. Drug synergism analysis also suggested interrupting the communications between cancer cells and their niches dramatically enhanced the drug efficacy against myeloma initiating cells, re-sensitized multiple myeloma to chemotherapies, and reduced risks of cancer relapse.

Archived Presentation (Edited): https://webmeeting.nih.gov/p58441950/

• Monday November 19, 1-2pm ET

Awardee: Linda Petzold, Chandra Krintz

Stochastic Simulation Service: Towards an Integrated Development Environment for Modeling and Simulation of Stochastic Biochemical Systems

ABSTRACT:In recent years it has become increasingly clear that stochasticity plays an important role in many biological processes. Examples include bistable genetic switches, noise enhanced robustness of oscillations, and fluctuation enhanced sensitivity or “stochastic focusing". In many cellular systems, local low species populations can create stochastic effects even if total cellular levels are high. Numerous cellular systems, including development, polarization and chemotaxis, rely on spatial stochastic noise for robust performance. In this talk we report on our progress in developing next-generation algorithms and software for modeling and simulation of stochastic biochemical systems, and in building an integrated development environment that will enable researchers to build a such a model and scale it up to increasing levels of complexity.

Archived Presentation (Edited): https://webmeeting.nih.gov/p27542195/

• 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/

• Wednesday October 3, 1-2pm ET

Awardees: David Kaplan, Markus Buehler, Joyce Wong

Models to Predict Protein Biomaterial Performance

ABSTRACT: There is a critical need to understand how tissue culture stimulation affects tissue construct development and function, with the ultimate goal of eliminating resource-intensive trial-and-error screening. Our goal is to develop predictive assessments of the in vivo performance of biomaterials so that a more rational approach based on a bottom-up modeling toolkit is used to guide the preparation of the required biomaterials. This new predictive approach would save time, animals, costs and accelerate the translation of such repair and regenerative systems. An important feature of our proposed approach is the direct integration of modeling and experimentation at multiple length scales, and the use of hierarchical material architectures across length scales, to reach enhanced material function. Our hypothesis is that predictions of biomaterials performance can be attained by the combined use of suitable experimental models to cover polymer features (chemistry, molecular weight), processing (fiber mechanical properties, hierarchical structure, degradation rate) and modeling at different length scales of materials structural hierarchy (from chemical to macroscopic). We have selected load bearing applications as the focus due to the generic needs in this field, such as for the anterior cruciate ligament, rotator cuff, bladder slings, hernia meshes, blood vessels, nerve guides and other tissues. Two well studied degradable polymer systems, silks and collagen, will be used for the experimental studies and model building, as they are directly amendable to highly controlled preparations and processing and cover a range of mechanical properties and degradation rates. In all cases, we build upon our extensive prior studies with these protein-based biomaterials, as well as developing hierarchical models of protein structure and function.

Archived Presentation (Edited): powerpoint slides

• 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/

For future recorded webinars:

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