Research Interests

Mechanistic computational modeling of signal transduction

I'm interested in detailed mechanistic modeling of signal transduction network in a single cell, describing activities and interactions among domains of biomolecules (e.g. phosphorylation of specific tyrosine residues, interactions between SH2 domain and phosphotyrosine). I mostly deal with Epidermal Growth Factor Receptor signaling, but I was also involved in modeling of signaling by high-affinity immunoreceptors, and looked into various signaling systems, e.g. Notch pathway, Insulin Growth Factor Receptor signaling, Interleukin-1 (IL-1) and Toll-like Receptor signaling.

Algorithms and software for rule-based modeling

Signal transduction networks often exhibit combinatorial complexity: the number of protein complexes and modification states that potentially can be generated during the response to a signal is large, because signaling proteins contain multiple sites of modification and interact with multiple binding partners. The conventional approach of manually specifying these networks is error-prone. If the number of species is large enough, manual specification becomes impossible. Reduction in the number of species and reactions is usually based on hidden assumptions that are often unjustified. An alternative to the conventional approach is a rule-based description, where all potential chemical species and reactions in the model are generated automatically by a computer algorithm from a set of rules. I am one of the developers of the rule-based modeling method and software BioNetGen. BioNetGen specification language can describe a broad range of biological effects. However, no spatial effects are included into it. Currently, I'm working in close collaboration with Virtual Cell team. I plan to develop rule-based approach for specification of spatial models, considering the subcellular localization of the molecules that take part in the cell, with aiming to investigste how the spatial organization of molecules in cells is utilized to control cell function.

Biological data storage and visualization tools

A large mechanistic model (accounting for many species and activities and interactions among domains of biomolecules) is very difficult to store, visualize, or modify. The standard way of storage is in electronic exchange formats (e.g. SBML). SBML file specifies each of individual species and interactions, but carries no information about domains of proteins and composition of multi-protein species. Simulation and visualization tools (such as CellDesigner) display each species and interactions, making representation very cluttered. An alternative is specifying (storage, visualization) of key features of the system, sufficient to restore the complete model. One way is Molecular Interaction Maps (MIM). Rules provide another very convenient way of protein-protein interaction data representation. Even more important, rules can serve as templates, allowing large models to be composed of simpler models describing known interactions. Integration of rules, MIMs and SBML is one of my projects.

Honors

2004, "BioNetGen" software nominated by Los Alamos National Lab for R&D 100

2003, Distinguished Performance Award, Los Alamos National Lab

1999, NSF travel award to attend AMS school in Irvine, USA

1995-2002, Feinberg Graduate Fellowship at the Weizmann Institute of Science

1994-95, International Science Foundation Student Grant for Distinguished Success in Studies

Selected Publications

Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W. (2006) Rules for modeling signal-transduction systems. Science STKE 334

Blinov ML, Yang J, Faeder JR, Hlavacek WS. (in press) Graph Theory for Rule-based Modeling of Biochemical Networks, Transactions on Computational Systems Biology in the series Lect. Notes Comput. Sci.

Blinov ML, Yang J, Faeder JR, Hlavacek WS. (2006) Depicting signaling cascades. Nat. Biotech. 24:137-38.

Blinov ML, Faeder JR, Goldstein B, and Hlavacek WS. (2006) A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. BioSystems 83:136-51.

Blinov ML, Faeder JR, Yang J, Goldstein B, Hlavacek WS. (2005) 'On-the-fly' or 'generate-first' modeling? Nat. Biotech. 23:1344-45

Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:3289-91.

Faeder JR, Hlavacek WS, Reischl I, Blinov ML, Metzger H, Redondo A, Wofsy C, Goldstein B. (2003) Investigation of early events in FceRI-mediated signaling using a detailed mathematical model. J. Immunol. 170:3769-81.

Patents

Blinov M, Faeder J, Hlavacek W. Software and procedures for creating mathematical/computational models of cellular signaling. US Patent Application (2005).

Faeder J, Blinov M, Hlavacek W. Graphical procedures for creating mathematical/computational models of cellular signaling. US Patent Application (2006).