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Michael L. Blinov

Researcher at University of Connecticut

Publications -  75
Citations -  4422

Michael L. Blinov is an academic researcher from University of Connecticut. The author has contributed to research in topics: Rule-based modeling & SBML. The author has an hindex of 27, co-authored 66 publications receiving 3962 citations. Previous affiliations of Michael L. Blinov include University of Toronto & Weizmann Institute of Science.

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Journal ArticleDOI

The BioPAX community standard for pathway data sharing

Emek Demir, +94 more
- 01 Sep 2010 - 
TL;DR: Thousands of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases, and this large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Book ChapterDOI

Rule-based modeling of biochemical systems with BioNetGen.

TL;DR: This work focuses on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the Bio netGen software tool.
Journal ArticleDOI

BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains

TL;DR: BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules.
Journal ArticleDOI

Rules for Modeling Signal-Transduction Systems

TL;DR: Approaches to creation of mathematical models of signaling systems with strategies that keep the models from being unwieldy but still allow them to accurately reflect biological systems are reviewed.
Journal ArticleDOI

Virtual cell modelling and simulation software environment

TL;DR: The Virtual Cell is now open source, with its native model encoding language (VCML) being a public specification, which stands as the basis for a new generation of more customised, experiment-centric modelling tools using a new plug-in based platform.