Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models.

TitleMass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models.
Publication TypeJournal Article
Year of Publication2010
AuthorsJamshidi N, Palsson BØ
JournalBiophysical journal
Volume98
Issue2
Pagination175-85
PubMed Date2010 Jan 20
ISSN1542-0086
KeywordsAlgorithms, Computer Simulation, Enzymes, Erythrocytes, Humans, Kinetics, Metabolism, Models, Biological, Signal Transduction
Abstract

The ability to characterize biological dynamics is important for understanding the integrated molecular processes that underlie normal and abnormal cellular states. The availability of metabolomic data, in addition to new developments in the formal description of dynamic states of networks, has enabled a new data integration approach for building large-scale kinetic networks. We show that dynamic network models can be constructed in a scalable manner using metabolomic data mapped onto stoichiometric models, resulting in mass action stoichiometric simulation (MASS) models. Enzymes and their various functional states are represented explicitly as compounds, or nodes in a stoichiometric network, within this formalism. Analyses and simulations of MASS models explicitly show that regulatory enzymes can control dynamic states of networks in part by binding numerous metabolites at multiple sites. Thus, network functional states are reflected in the fractional states of a regulatory enzyme, such as the fraction of the total enzyme concentration that is in a catalytically active versus inactive state. The feasible construction of MASS models represents a practical means to increase the size, scope, and predictive capabilities of dynamic network models in cell and molecular biology.

Alternate JournalBiophys. J.
PubMed ID20338839

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