The goal of this project is to create powerful rule-based modeling capabilities embedded into the popular Virtual Cell (VCell) software. This project will benefit the large community of VCell users, who will be able to build models and simulations of larger and more complex systems by combining traditional approaches with the expressive power of rules and using network-free simulation engines. The project will also benefit the smaller, but rapidly growing community of modelers that use rule-based approaches, who will be able to create models with an intuitive, easy to use, biologist-oriented graphical interface, and access many powerful features of the established VCell modeling and simulation framework.. The proposed approach uses existing BioNetGen software and model description language, which will be extended and integrated into VCell. Significant de novo development will be required for both user interface elements (graphical model editing and visualization) and computational algorithms (translating and linking between VCell and BNG languages and simulation engines). Specifically, we propose to: (a) integrate VCell and BioNetGen data models and provide storage, search, retrieval, and reuse of rule-based models and model elements in the VCell database (b) extend VCell model building and visualization capabilities to include rule- based specifications of molecular species and interactions and, and (c) Integrate the simulation capabilities of VCell and BioNetGen, allowing BioNetGen-generated models to be simulated using existing VCell solvers, and including new simulation options in VCell based on BioNetGen on-the-fly and network-free solvers. The approach is designed to be flexible and will be co- ordinated with the continuing development of BioNetGen and VCell core capabilities, which is facilitated by the collaborations of the investigators with the PIs of the grants devoted to development of core capabilities of BioNetGen and VCell respectively (letters of support included). Additionally, the proposed model specification and visualization schemas will be compatible with extensions to the SBML and SBGN standards currently under development. This will enhance the ability of users to exchange models created in the proposed integrated BioNetGen-VCell framework with other software tools and databases, and the scientific community at large.

Public Health Relevance

Mathematical models and computer simulations of the complex molecular mechanisms that underlay cellular processes are increasingly important tools for elucidating the causes of human disease and predicting viable treatment strategies. This work will facilitate the construction of such models, building an integrated framework based upon several state-of-the-art software tools and modeling strategies.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM095485-03
Application #
8464157
Study Section
Special Emphasis Panel (ZRG1-BST-H (50))
Program Officer
Lyster, Peter
Project Start
2011-05-01
Project End
2014-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
3
Fiscal Year
2013
Total Cost
$282,359
Indirect Cost
$99,009
Name
University of Connecticut
Department
Biochemistry
Type
Schools of Medicine
DUNS #
022254226
City
Farmington
State
CT
Country
United States
Zip Code
06030
Blinov, Michael L; Schaff, James C; Vasilescu, Dan et al. (2017) Compartmental and Spatial Rule-Based Modeling with Virtual Cell. Biophys J 113:1365-1372
Falkenberg, Cibele Vieira; Carson, John H; Blinov, Michael L (2017) Multivalent Molecules as Modulators of RNA Granule Size and Composition. Biophys J 113:235-245
Schaff, James C; Vasilescu, Dan; Moraru, Ion I et al. (2016) Rule-based modeling with Virtual Cell. Bioinformatics 32:2880-2
Blinov, Michael L; Schaff, James C; Ruebenacker, Oliver et al. (2014) Pathway Commons at virtual cell: use of pathway data for mathematical modeling. Bioinformatics 30:292-4
Falkenberg, Cibele V; Blinov, Michael L; Loew, Leslie M (2013) Pleomorphic ensembles: formation of large clusters composed of weakly interacting multivalent molecules. Biophys J 105:2451-60
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Blinov, Michael L; Moraru, Ion I (2012) Leveraging modeling approaches: reaction networks and rules. Adv Exp Med Biol 736:517-30