Literature DB >> 15637632

Automatic generation of cellular reaction networks with Moleculizer 1.0.

Larry Lok1, Roger Brent.   

Abstract

Accurate simulation of intracellular biochemical networks is essential to furthering our understanding of biological system behavior. The number of protein complexes and of chemical interactions among them has traditionally posed significant problems for simulation algorithms. Here we describe an approach to the exact stochastic simulation of biochemical networks that emphasizes the contribution of protein complexes to these systems. This simulation approach starts from a description of monomeric proteins and specifications for binding, unbinding and other reactions. This manageable specification is reasonably intuitive for biologists. Rather than requiring the inclusion of all possible complexes and reactions from the outset, our approach incorporates new complexes and reactions only when needed as the simulation proceeds. As a result, the simulation generates much smaller reaction networks, which can be exported to other simulators for further analysis. We apply this approach to the automatic generation of reaction systems for the study of signal transduction networks.

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Year:  2005        PMID: 15637632     DOI: 10.1038/nbt1054

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  64 in total

1.  Leveraging modeling approaches: reaction networks and rules.

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4.  Timescale analysis of rule-based biochemical reaction networks.

Authors:  David J Klinke; Stacey D Finley
Journal:  Biotechnol Prog       Date:  2011-09-26

5.  Rule-based modelling and simulation of biochemical systems with molecular finite automata.

Authors:  J Yang; X Meng; W S Hlavacek
Journal:  IET Syst Biol       Date:  2010-11       Impact factor: 1.615

6.  Simulation study of the contribution of oligomer/oligomer binding to capsid assembly kinetics.

Authors:  Tiequan Zhang; Russell Schwartz
Journal:  Biophys J       Date:  2005-10-07       Impact factor: 4.033

7.  Signaling through receptors and scaffolds: independent interactions reduce combinatorial complexity.

Authors:  Nikolay M Borisov; Nick I Markevich; Jan B Hoek; Boris N Kholodenko
Journal:  Biophys J       Date:  2005-05-27       Impact factor: 4.033

8.  Structure-based kinetic models of modular signaling protein function: focus on Shp2.

Authors:  Dipak Barua; James R Faeder; Jason M Haugh
Journal:  Biophys J       Date:  2007-01-05       Impact factor: 4.033

9.  Hierarchical graphs for rule-based modeling of biochemical systems.

Authors:  Nathan W Lemons; Bin Hu; William S Hlavacek
Journal:  BMC Bioinformatics       Date:  2011-02-02       Impact factor: 3.169

10.  Kinetic Monte Carlo method for rule-based modeling of biochemical networks.

Authors:  Jin Yang; Michael I Monine; James R Faeder; William S Hlavacek
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2008-09-10
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