Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations

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We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, 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 coarse-grained numerical ... continued below

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Nemenman, Ilya; Sinitsyn, Nikolai & Hengartner, Nick January 1, 2008.

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We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, 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 coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. 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 are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.

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  • Journal Name: Proceedings of the National Academy of Sciences

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  • Report No.: LA-UR-08-07864
  • Report No.: LA-UR-08-7864
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956631
  • Archival Resource Key: ark:/67531/metadc933941

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  • January 1, 2008

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  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 9, 2016, 10:57 p.m.

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Nemenman, Ilya; Sinitsyn, Nikolai & Hengartner, Nick. Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations, article, January 1, 2008; [New Mexico]. (digital.library.unt.edu/ark:/67531/metadc933941/: accessed October 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.