Multiscale Systems Biology Working Group
Multiscale Systems Biology Working Group
Goals and Objectives:
This working group is devoted to multiple-scale analysis and simulation of biological systems, with a special emphasis on cellular phenomena. Our first goal is to establish on this site a clearinghouse of up-to-date information on available tools, novel concepts, and major relevant review papers in the field. This resource will aim to capture the state of the art in significant research, for purposes of advancing research, education, and training. Secondary goals include identifying and articulating current challenges and opportunities in the field, as well as fostering scientific collaborations.
Participation in Working Group:
Participation in this working group (WG) is open to all who are interested; to join please contact one of the WG co-leads listed above. WG participants will be kept appraised of WG, Multiscale Modeling (MSM) Consortium, and IMAG discussions. Responsibilities of this working group include: (i) defining the WG title, goals and objectives, (ii) determining the current state of the art in multiscale systems biology, and identifying new challenges and opportunities, and (iii) suggesting and attending webinar presentations, contributing to WG white papers, workshops and publications. Participants will be expected to actively engage in WG activities, including managing content on the site, participating in online presentations, and other relevant activities (MSM participants are assigned IMAG wiki logins by IMAG staff). The activities of the WG should not reflect someone’s personal agenda, but should represent the consensus of the group.
Thursday March 21, 2013, 11:00am - noon EST
- Towards predictive quantitative modeling of tissue organization and tumor growth on histological scales by imaging, image analysis and modeling
- Dirk Drasdo, Stefan Hoehme, Jan G. Hengstler, Rolf Gebhardt, Ursula Klingmueller, Jens Timmer
- A major challenge is to understand how cells and molecules act coordinately together to form complex functional tissue architectures and which processes are perturbed in aberrant states. While in-vitro systems, frequently considered as model systems may help in identifying candidate mechanisms that may act in-vivo, the increased complexity of the in-vivo system of interest – usually a patient – ultultimately require in-vivo validation. We propose a mathematical modeling - guided experimental strategy to optimize and economize the choice of experiments that address tissue organization and growth processes. Our procedure is based on a recently established process chain composed of confocal laser scans, image processing and three-dimensional tissue reconstruction, as well as on quantitative mathematical modeling resolving tissue architecture (Hoehme et. al., PNAS, 107:10371-10376, 2010). We demonstrate how by iterative application of this procedure a final mathematical model could be constructed that unambiguously predicted a previously unrecognized order mechanism in liver regeneration. The model prediction has subsequently been experimentally validated. The model has been further used to calculate the spatial temporal pattern of ammonia detoxification during the regeneration process. We then use this model to simulate early genesis of primary tumors in liver in small animals. The simulations show that hepatocytes lacking cell cycle entrance control form an – experimenntally observed - tumor phenotype that reflects the order mechanism. If the order mechanism is compromised, another tumor phenotype forms which is very robust against changes of other parameters such as cell-cell adhesion, micro-motility etc.. The signature of the order mechanism gets lost once the tumor size overcomes the size of a single liver lobule. A fundamental problem is in how far findings in an animal model can be transferred to human as validation experiments in human are particularly difficult to perform. We finally illustrate that our mathematical model, firstly calibrated with static and dynamic mouse data, and in a second step re-calibrated with only static pig data, provides a valid prediction for liver regeneration after partial hepatectomy in pig. This may serve as a first proof-of-concept step to use models of tissue organization to extrapolate from an animal model to patients.
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 ET
- Multi-Scale Modeling of Sickle Cell Anemia
- George Karniadakis, PhD, Brown University
- Presentation 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 June 8, 2012 at 1:00pm ET
- Specification, Construction, and Exact Reduction of State Transition System Models of Biochemical Processes
- Scott M. Bugenhagen and Daniel A. Beard, PhD
- 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.
Friday October 28, 2011 1-2pm ET
- Kasia A. Rejniak, PhD, H. Lee Moffitt Cancer Center & Research Institute
- Title: Computational Bridging of Epithelial Morphogenesis and Tumor Mutations
- A major challenge in biology is the mapping of genotypic changes to phenotypic outcomes. I will present how a computational model of epithelial morphogenesis (IBCell) can address this problem by linking molecular alterations to epithelial morphology through cellular core traits. In particular, I will show an example in which IBCell interrogated with 3-dimensional experimental acinar morphologies of breast epithelial cells expressing a mutant HER2 receptor leads to identification of previously unknown core trait alterations, i.e., loss of negative feedback from autocrine secreted ECM. I will also briefly show other applications of the IBCell model.
- Lance L. Munn, PhD, Massachusetts General Hospital & Harvard Medical School
- Title: Imaging vascular dynamics
- Although therapies targeting the vasculature have had growing popularity in the past decade, we still know surprisinlgy little about how vasculature is formed or remodeled in plastic tissues such as wound beds or tumors. Intravital microscopy in transparent windows has the potential to reveal how cells organize and cooperate to accomplish critical processes such as morphogenesis and anastomosis. Facilitated by the recent availability of in vivo reporters and time-lapse imaging which allow tracking of specific cell populations, intravital microscopy is a powerful tool for determining cellular mechanisms of vascularization and tumor growth.
Tuesday May 31, 2011 11am-12pm ET: The Cardiovascular System and Disease
Nic Smith, Kings College London. Translating multi-scale modelling to the Heart of the clinic: developing personalised cardiac models
Michael King, Cornell University. Multiscale model of platelet adhesion and thrombus formation: validation with the humanized mouse
BISEN (Biochemical Simulation Environment) http://www.virtualrat.org/BISEN/
CellSys, a modular software tool for simulation of growth and organization processes in multicellular systems in 2D and 3D implementing agent-based modeling http://msysbio.com/software/cellsys/
Chaste (Cancer, Heart and Soft Tissue Environment), a general purpose multi-scale simulation package http://web.comlab.ox.ac.uk/chaste/
Dymola (Dynamic Modeling Laboratory, a complete tool for modeling and simulation of integrated and complex systems based on the open Modelica modeling language) http://www.dymola.com/
DYNSTOC: a tool for simulating large-scale rule-based models http://public.tgen.org/dynstoc/
E-cell Project http://www.e-cell.org/ecell/
FLAME, Flexible Large-scale Agent Modelling Environment http://www.flame.ac.uk/
Gepasi (Biochemical Kinetics Simulator) http://www.gepasi.org/
JDesigner (Visual Network Design Tool) http://www.sys-bio.org
MASON (Multi-Agent Simulator Of Neighborhoods… or Networks; discrete-event multiagent simulation library core in Java) http://cs.gmu.edu/~eclab/projects/mason/
MCell and DReAMM (Center for Quantitative Biological Simulation Microphysiology Gateway) http://www.mcell.psc.edu/
Modelica (an object-oriented, equation based language to model complex physical systems) https://modelica.org/
NetLogo (Multi-Agent Modeling) http://ccl.northwestern.edu/netlogo/
NFsim (the network-free stochastic simulator, an open-source, modeling and simulation platform for biology) http://emonet.biology.yale.edu/nfsim/
RULEMONKEY: a tool for simulating large-scale rule-based models http://public.tgen.org/rulemonkey/
SemSim (multi-scale model modularity, semantic annotation) http://sbp.bhi.washington.edu/projects/semsim
Systems Biology Workbench http://www.sys-bio.org
TinkerCell (Visual Network Design Tool) http://www.tinkercell.com
Virtual Cell http://www.nrcam.uchc.edu/
Sensitivity and Uncertainty Analysis Tools
eFAST (Extended Fourier Amplitude Sensitivity Test) http://malthus.micro.med.umich.edu/lab/usadata/
LHS (Latin Hypercube Sampling) http://www.mathworks.com/matlabcentral/fileexchange/4352-latin-hypercube-sampling
LHS-PRCC (Latin Hypercube Sampling - Partial Rank Correlation Coefficient) http://malthus.micro.med.umich.edu/lab/usadata/
ME-PCM (Multi-element probabilistic collocation method) Foo J, Sindi S, Karniadakis GE. Multi-element probabilistic collocation for sensitivity analysis in cellular signalling networks. IET Syst Biol. 2009 3:239-254.
PLSDA (Partial Least Squares Discriminant Analysis) Westerhuis JA, van Velzen EJ, Hoefsloot HC, Smilde AK. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics. 2010 6:119-128.
NCBI (National Center for Biotechnology Information - genomic, proteomic, PubMed) http://www.ncbi.nlm.nih.gov/
Biositemaps, a mechanism for computational biologists and bioinformaticians to openly broadcast and retrieve meta-data about biomedical resources http://biositemaps.ncbcs.org/
The DrugBank database http://www.drugbank.ca/
HMDB (The Human Metabolome Database) http://www.hmdb.ca/
MiMI (Michigan Molecular Interactions) http://mimi.ncibi.org/MimiWeb/main-page.jsp
PathCase: Pathways Database System http://nashua.case.edu/PathwaysWeb/
PhysioNet (biomedical signals database) http://www.physionet.org/
ProteinLounge (signaling pathways; Pathway Builder software) http://www.proteinlounge.com/
SMPDB (The Small Molecule Pathway Database) http://www.smpdb.ca/
The UCSD-Nature Signaling Gateway http://www.signaling-gateway.org/
The Visible Human Project http://www.nlm.nih.gov/research/visible/
BioModels Database http://biomodels.net/, http://biomodels.org/
Cardiovascular Model Repository https://simtk.org/home/cv-gmodels/
CellML Models http://models.cellml.org/cellml
Neuromuscular Models Library https://simtk.org/home/nmblmodels/
Ricordo (Virtual Physiological Human project) http://www.vph-noe.eu/vph-projects/74-eu-fp7-vph-projects/390-ricordo
Cytoscape (An Open Source Platform for Complex-Network Analysis and Visualization) http://www.cytoscape.org/
MCV (Multiscale Spatiotemporal Visualisation, Development of an Open-Source Software Library for the Interactive Visualisation of Multiscale Biomedical Data) http://www.msv-project.eu/
NA-MIC (National Alliance for Medical Image Computing) http://www.na-mic.org/
NIfTI (Neuroimaging Informatics Technology Initiative) http://nifti.nimh.nih.gov/
3D Slicer (An open source software platform for visualization and medical image computing) http://www.slicer.org/
V3D (3D/4D/5D Image Visualization & Analysis System for Bioimages & Surface Objects) http://penglab.janelia.org/proj/v3d/V3D/
The Gene Ontology (GO) http://www.geneontology.org/
The Open Biological and Biomedical Ontologies http://www.obofoundry.org/
The Sequence Ontology http://www.sequenceontology.org/
Systems Biology Ontology (SBO) http://www.ebi.ac.uk/sbo/main/
Relevant Project Portals
The Physiome Project http://www.physiome.org.nz/, http://www.physiome.org/
The Virtual Physiological Human http://www.vph-noe.eu/
BioNetWiki/BioNetGen, a resource for rule-based modeling http://bionetgen.org/index.php/Main_Page
The Virtual Physiological Rat http://www.virtualrat.org
COMBINE, the COmputational Modeling in BIology NEtwork http://co.mbine.org
Disease- and Organ-Specific Resources
Human Body Simulator (Integrated Human Physiology):
NCI thesaurus http://ncit.nci.nih.gov/
The Cardiac Atlas Project http://www.cardiacatlas.org/
SimVascular Cardiovascular Modeling and Simulation Application https://simtk.org/home/simvascular/
The Virtual Gastrointestinal Tract http://www.vigorpp.eu/
The Quantitative Kidney Database http://physiome.ibisc.fr/qkdb/
The Virtual Liver http://www.virtual-liver.de/
The Virtual Physiological Rat Project YouTube Channel http://www.youtube.com/user/VirtualRatProject
Human Body Simulator (Integrated Human Physiology):
Challenges and Opportunities:
High-throughput genomic, proteomic and epigenomic data are rapidly accumulating. Current models of physiological systems rarely take advantage of the availability of such data, in part because they inform process that operate on different time and space scales. Thus, a challenge and opportunity for multiscale systems biology is utilize statistical modeling and analyses dealing with high-throughput data to inform mechanistic multiscale modeling.
Another challenge for the physics-based models is for computationally intensive simulations to reach to laboratory and experimental timescales to enable direct comparison between models and experimental data. There is a trade-off between efficient course-graining, and retaining connection to meaningful physical parameters that correspond to independently measurable quantities. Some of these issues will hopefully be addressed in the context of blood clot formation, in a special multiscale Biorheology session of the International Society on Thrombosis and Haemostasis this summer:
Annals of Biomedical Engineering 2012 in press (Special issue on Multiscale modeling)
IEEE Transactions on Biomedical Engineering, Special Issue on Multiscale Modeling and Analysis in Computational Biology and Medicine Volume: 58 , Issue: 10 , Part: 2 http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6082051&punumber=10
Multiscale Modeling of Particle Interactions: Applications in Biology and Nanotechnology. M.R. King and D.J. Gee, editors, Wiley, 2010 http://onlinelibrary.wiley.com/book/10.1002/9780470579831
Engineering in Medicine and Biology Magazine, IEEE Vol 28 , Issues:2- 3, 2009 http://ieeexplore.ieee.org/search/searchresult.jsp?punumber=51&searchWithin=multiscale
List of Participants:
Mark Alber, Univ. of Notre Dame
Gary An, Univ. of Chicago
Miguel Aon, Johns Hopkins Univ.
James Bassingthwaighte, Univ. of Washington
Mike Bindschadler, Univ. of Washington
Michael Blinov, Univ. of Connecticut
Daniela Calvetti, Case Western Univ.
Stuart Campbell, Univ. of California San Diego
Sonia Cortassa, Johns Hopkins Univ.
Edmund Crampin, Univ. of Auckland
Ranjan Dash, Medical College of Wisconsin
Walter Deback, Technical Univ. Dresden
Forbes Dewey, MIT
Scott Diamond, Univ. of Pennsylvania
Julie Dickerson, Iowa State Univ.
Maciej Dobrzynski, Systems Biology Ireland
Amina Eladdadi, College of St. Rose
Stephan Eubank, Virginia Tech
Ronan Fleming, University of Iceland
Jonathan Freund, Univ. of Illinois
Bingmei Fu, City Univ. of New York
Qian Gao, Brunel Univ.
Donald Gaver, Tulane Univ.
Soma Ghosh, Indian Institute of Science
James Glazier, Indiana Univ.
Susan Gregurick, DOE
Julius Guccione, UCSF School of Medicine
Dan Hammer, Univ. of Pennsylvania
Jason Haugh, North Carolina State Univ.
Randy Heiland, Indiana Univ.
Robert Hester, Univ. of Mississippi
Bill Hlavacek, Los Alamos National Lab
Zuyi Huang, BHSAI
Jay Humphrey, Yale Univ.
Anthony Hunt, UCSF
Peter Hunter, Univ. of Auckland, NZ
Kevin Janes, Univ. of Virginia
Yi Jiang, Los Alamos National Lab
Roger Kamm, MIT
Ghassan Kassab, IUPUI
Mahendra Kavdia, Wayne State Univ.
Damir Khismatullin, Tulane Univ.
Boris Kholodenko, Univ. College Dublin
Denise Kirschner, Univ. of Michigan
Peter Kohl, Imperial College
Konstantinos Konstantopoulos, JHU (PMMA Team)
Ellen Kuhl, Stanford
Martin Kushmerick, Univ. of Washington
Nicola Lai, Case Western Reserve Univ.
Reinhard Laubenbacher, Virginia Bioinformatics Institute
Jonathan Lederer, Univ. of Maryland
Klaus Ley, LIAI (PMMA Team)
Jennifer Linderman, Univ. of Michigan
Leslie Loew, Univ. of Connecticut
Joanne Luciano, Rensselaer Polytech. Inst.
Feilim Mac Gabhann, Johns Hopkins Univ.
Madhav Marathe, Virginia Tech
Simeone Marino, Univ. of Michigan
Andrew McCulloch, UCSD
Hongyu Miao, Univ. of Rochester
Alexander Mitrophanov, BHSAI
Mohammad Mofrad, UC Berkeley
Michael Monine, Bioanalysis Group
Ion Moraru, Univ. of Connecticut
Maxwell Neal, Univ. of Washington
Mette Olufsen, NC State Univ.
Peter Ortoleva, Indiana Univ.
Jason Papin, Univ. of Virginia
Grace Peng, NIH/NHLBI
John Pepper, NIH/NCI
Linda Petzold, Univ. of California Santa Barbara
Maria Pospieszalska, LIAI (PMMA Team)
Amina Qutub, Rice Univ.
Ravi Radhakrishnan, Univ. Pennsylvania
Katarzyna Rejniak, Moffitt Cancer Center
Haluk Resat, Pacific Northwest National Laboratory
Tuhin Roy, Mayo Clinic
Gerald Saidel, Case Western Reserve Univ.
Jeffrey Saucerman, Univ. of Virginia
Michael Saunders, Stanford
Herbert Sauro, Univ. of Washington
James Schwaber, Thomas Jefferson Univ.
Timothy Secomb, Univ. of Arizona
Scott Simon, Univ. of California, Davis
Rod Smallwood, Univ. of Sheffield
Nicolas Smith, King's College London
Erkki Somersalo, Case Western Reserve Univ.
Marianne Stefanini, BHSAI
Maciej Swat, Indiana University
Ines Thiele, University of Iceland
Natalia Trayanova, Johns Hopkins Univ.
Nikolaos Tsoukias, Florida International Univ.
George Truskey, Duke Univ.
Rajanikanth Vadigepalli, Thomas Jefferson Univ.
Jeff Varner, Cornell Univ.
Yoram Vodovotz, University of Pittsburgh
Bridget Wilson, University of New Mexico
Raimond Winslow, Johns Hopkins Univ.
Charles Wolgemuth, Univ. of Arizona
Yi Wu, Univ. of Connecticut Health Center
Tadeusz A Wysocki, University of Nebraska - Lincoln
Lingchong You, Duke Univ.
Wang Yu-Ping, Tulane Univ.
LuFang Zhou, University of Alabama
Xiaobo Zhou, Wake Forest Univ.