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Adiabatic coarse-graining and simulations of stochastic biochemical networks.

ABSTRACT: We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical networks, which rests on elimination of fast chemical species without a loss of information about mesoscopic, non-Poissonian fluctuations of the slow ones. Our approach is similar to the Born-Oppenheimer approximation in quantum mechanics and follows from the stochastic path integral representation of the cumulant generating function of reaction events. In applications with a small number of chemical reactions, it produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, interpretable representation and can be used for high-accuracy, low-complexity coarse-grained numerical simulations. As an example, we derive the coarse-grained description for a chain of biochemical reactions and show that the coarse-grained and the microscopic simulations agree, but the former is 3 orders of magnitude faster.

SUBMITTER: Sinitsyn NA

PROVIDER: S-EPMC2705573 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Adiabatic coarse-graining and simulations of stochastic biochemical networks.

Sinitsyn N A NA Hengartner Nicolas N Nemenman Ilya I

Proceedings of the National Academy of Sciences of the United States of America 20090612 26

We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical networks, which rests on elimination of fast chemical species without a loss of information about mesoscopic, non-Poissonian fluctuations of the slow ones. Our approach is similar to the Born-Oppenheimer approximation in quantum mechanics and follows from the stochastic path integral representation of the cumulant generating function of reaction events. In applications with a small number of chemica ...[more]

PMID: 19525397

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Dataset Information

Adiabatic coarse-graining and simulations of stochastic biochemical networks.

Publications

Adiabatic coarse-graining and simulations of stochastic biochemical networks.

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