title: Simulation of stochastic network dynamics via entropic matching.
abstract: The simulation of complex stochastic network dynamics arising, for instance, from models of coupled biomolecular processes remains computationally challenging. Often, the necessity to scan a model's dynamics over a large parameter space renders full-fledged stochastic simulations impractical, motivating approximation schemes. Here we propose an approximation scheme which improves upon the standard linear noise approximation while retaining similar computational complexity. The underlying idea is to minimize, at each time step, the Kullback-Leibler divergence between the true time evolved probability distribution and a Gaussian approximation (entropic matching). This condition leads to ordinary differential equations for the mean and the covariance matrix of the Gaussian. For cases of weak nonlinearity, the method is more accurate than the linear method when both are compared to stochastic simulations.
authors.affiliation: Arnold Sommerfeld Center for Theoretical Physics (ASC) and Center for Nanoscience (CeNS), LMU München, Theresienstrasse 37, 80333 München, Germany. tiago.ramalho@physik.uni-muenchen.de
authors.forename: Tiago
authors.initials: T
authors.lastname: Ramalho
authors.forename: Marco
authors.initials: M
authors.lastname: Selig
authors.forename: Ulrich
authors.initials: U
authors.lastname: Gerland
authors.forename: Torsten A
authors.initials: TA
authors.lastname: Ensslin
mesh_terms: D000818:Animals; D003198:Computer Simulation; D019277:Entropy; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism*; D013269:Stochastic Processes
chemical_list: D020543:Proteome