Literature DB >> 21568538

Binomial moment equations for stochastic reaction systems.

Baruch Barzel1, Ofer Biham.   

Abstract

A highly efficient formulation of moment equations for stochastic reaction networks is introduced. It is based on a set of binomial moments that capture the combinatorics of the reaction processes. The resulting set of equations can be easily truncated to include moments up to any desired order. The number of equations is dramatically reduced compared to the master equation. This formulation enables the simulation of complex reaction networks, involving a large number of reactive species much beyond the feasibility limit of any existing method. It provides an equation-based paradigm to the analysis of stochastic networks, complementing the commonly used Monte Carlo simulations.

Mesh:

Year:  2011        PMID: 21568538     DOI: 10.1103/PhysRevLett.106.150602

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  6 in total

1.  A moment-convergence method for stochastic analysis of biochemical reaction networks.

Authors:  Jiajun Zhang; Qing Nie; Tianshou Zhou
Journal:  J Chem Phys       Date:  2016-05-21       Impact factor: 3.488

2.  Queuing Models of Gene Expression: Analytical Distributions and Beyond.

Authors:  Changhong Shi; Yiguo Jiang; Tianshou Zhou
Journal:  Biophys J       Date:  2020-09-09       Impact factor: 4.033

3.  Gene expression in self-repressing system with multiple gene copies.

Authors:  Jacek Miekisz; Paulina Szymańska
Journal:  Bull Math Biol       Date:  2013-01-25       Impact factor: 1.758

4.  The kinetic Monte Carlo method as a way to solve the master equation for interstellar grain chemistry.

Authors:  H M Cuppen; L J Karssemeijer; T Lamberts
Journal:  Chem Rev       Date:  2013-11-04       Impact factor: 60.622

5.  Dynamic patterns of information flow in complex networks.

Authors:  Uzi Harush; Baruch Barzel
Journal:  Nat Commun       Date:  2017-12-19       Impact factor: 14.919

6.  Identifying early-warning signals of critical transitions with strong noise by dynamical network markers.

Authors:  Rui Liu; Pei Chen; Kazuyuki Aihara; Luonan Chen
Journal:  Sci Rep       Date:  2015-12-09       Impact factor: 4.379

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.