Richard D. Berlin
Center for Cell Analysis & Modeling

CCAM Faculty

Michael Blinov

Assistant Professor of Genetics and Develpmental Biology

Contact Information

Dr. Michael Blinov
University of Connecticut Health Center
Center for Cell Analysis & Modeling
263 Farmington Avenue
Farmington, CT 06030-6406
Phone: 860-679-6081
Fax: 860-679-1039
blinov@uchc.edu
Blinov Lab Web Page

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.