Stochastic modeling of the chemostat

The chemostat is classically represented, at large population scale, as a system of ordinary differential equations. Our goal is to establish a set of stochastic models that are valid at different scales: from the small population scale to the scale immediately preceding the one corresponding to the deterministic model. At a microscopic scale we present a pure jump stochastic model that gives rise, at the macroscopic scale, to the ordinary differential equation model. At an intermediate scale, an approximation diffusion allows us to propose a model in the form of a system of stochastic differential equations. We expound the mechanism to switch from one model to another, together with the associated simulation procedures. We also describe the domain of validity of the different models.

[1]  D. Gillespie The chemical Langevin equation , 2000 .

[2]  T. Kurtz Solutions of ordinary differential equations as limits of pure jump markov processes , 1970, Journal of Applied Probability.

[3]  Johan Grasman,et al.  Breakdown of a Chemostat Exposed to Stochastic Noise , 2005 .

[4]  Kenny S. Crump,et al.  Some stochastic features of bacterial constant growth apparatus , 1979 .

[5]  Tiejun Li,et al.  Analysis of Explicit Tau-Leaping Schemes for Simulating Chemically Reacting Systems , 2007, Multiscale Model. Simul..

[6]  F. Campillo,et al.  Stochastic models for the chemostat , 2010, 1011.5108.

[7]  Philip K. Pollett,et al.  Diffusion approximations for ecological models , 2001 .

[8]  L. Rogers Stochastic differential equations and diffusion processes: Nobuyuki Ikeda and Shinzo Watanabe North-Holland, Amsterdam, 1981, xiv + 464 pages, Dfl.175.00 , 1982 .

[9]  Paul Waltman,et al.  The Theory of the Chemostat , 1995 .

[10]  P K Pollett,et al.  On parameter estimation in population models II: multi-dimensional processes and transient dynamics. , 2009, Theoretical population biology.

[11]  李幼升,et al.  Ph , 1989 .

[12]  D. Herbert,et al.  The continuous culture of bacteria; a theoretical and experimental study. , 1956, Journal of general microbiology.

[13]  J V Ross,et al.  On parameter estimation in population models. , 2006, Theoretical population biology.

[14]  S. Ethier,et al.  Markov Processes: Characterization and Convergence , 2005 .

[15]  David F. Anderson,et al.  Error analysis of tau-leap simulation methods , 2009, 0909.4790.

[16]  Bernard Lapeyre,et al.  Introduction to Stochastic Calculus Applied to Finance , 2007 .

[17]  D. Gillespie,et al.  Avoiding negative populations in explicit Poisson tau-leaping. , 2005, The Journal of chemical physics.

[18]  Gregory Stephanopoulos,et al.  A stochastic analysis of the growth of competing microbial populations in a continuous biochemical reactor , 1979 .

[19]  Linda R. Petzold,et al.  Improved leap-size selection for accelerated stochastic simulation , 2003 .

[20]  T. Kurtz Limit theorems for sequences of jump Markov processes approximating ordinary differential processes , 1971, Journal of Applied Probability.

[21]  Darren J. Wilkinson Stochastic Modelling for Systems Biology , 2006 .

[22]  D. Gillespie Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .

[23]  A. Diop Sur la discrétisation et le comportement à petit bruit d'EDS unidimensionnelles dont les coefficients sont à dérivées singulières , 2003 .

[24]  T. Horiuchi [Continuous culture of bacteria]. , 1972, Tanpakushitsu kakusan koso. Protein, nucleic acid, enzyme.

[25]  Hugh P. Possingham,et al.  Do life history traits affect the accuracy of diffusion approximations for mean time to extinction , 2002 .

[26]  Sebastian Walcher,et al.  Exclusion and persistence in deterministic and stochastic chemostat models , 2005 .

[27]  Muruhan Rathinam,et al.  Consistency and Stability of Tau-Leaping Schemes for Chemical Reaction Systems , 2005, Multiscale Model. Simul..

[28]  A. Novick,et al.  Description of the chemostat. , 1950, Science.