The Status of the QSSA Approximation in Stochastic Simulations of Reaction Networks

Stochastic models of chemical reactions are needed in many contexts in which the copy numbers of species are low, but only the simplest models can be treated analytically. However, direct simulation of computational models for systems with many components can be very time-consuming, and approximate methods are frequently used. One method that has been used in systems with multiple time scales is to approximate the fast dynamics, and in this note we study one such approach, in which the deterministic QSSH is used for the fast variables and the result used in the rate equations for the slow variables. We examine the classical Michaelis-Menten kinetics using this approach to determine when it is applicable.

[1]  C. Rao,et al.  Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm , 2003 .

[2]  Yang Cao,et al.  Stochastic simulation of enzyme-catalyzed reactions with disparate timescales. , 2008, Biophysical journal.

[3]  Linda E Reichl,et al.  Instabilities, Bifurcations, and Fluctuations in Chemical Systems , 1982 .

[4]  Jae Kyoung Kim,et al.  The relationship between stochastic and deterministic quasi-steady state approximations , 2015, BMC Systems Biology.

[5]  L. A. Segel,et al.  The Quasi-Steady-State Assumption: A Case Study in Perturbation , 1989, SIAM Rev..

[6]  Andreas Hellander,et al.  Accuracy of the Michaelis–Menten approximation when analysing effects of molecular noise , 2015, Journal of The Royal Society Interface.

[7]  H. Othmer Analysis of Complex Reaction Networks in Signal Transduction , Gene Control and Metabolism , 2006 .

[8]  Animesh Agarwal,et al.  On the precision of quasi steady state assumptions in stochastic dynamics. , 2012, The Journal of chemical physics.

[9]  T. Kurtz,et al.  Separation of time-scales and model reduction for stochastic reaction networks. , 2010, 1011.1672.

[10]  Kevin R. Sanft,et al.  Legitimacy of the stochastic Michaelis-Menten approximation. , 2011, IET systems biology.

[11]  Philipp Thomas,et al.  Communication: limitations of the stochastic quasi-steady-state approximation in open biochemical reaction networks. , 2011, The Journal of chemical physics.

[12]  Philip Ball,et al.  The Self-Made Tapestry: Pattern Formation in Nature , 1999 .

[13]  A. Oudenaarden,et al.  Cellular Decision Making and Biological Noise: From Microbes to Mammals , 2011, Cell.

[14]  L. Tsimring Noise in biology , 2014, Reports on progress in physics. Physical Society.

[15]  Hans G Othmer,et al.  A multi-time-scale analysis of chemical reaction networks: II. Stochastic systems , 2015, Journal of Mathematical Biology.

[16]  Philipp Thomas,et al.  The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions , 2012, BMC Systems Biology.

[17]  Krešimir Josić,et al.  The validity of quasi-steady-state approximations in discrete stochastic simulations. , 2014, Biophysical journal.

[18]  Hans G Othmer,et al.  A multi-time-scale analysis of chemical reaction networks: I. Deterministic systems , 2010, Journal of mathematical biology.

[19]  J. Goutsias Quasiequilibrium approximation of fast reaction kinetics in stochastic biochemical systems. , 2005, The Journal of chemical physics.

[20]  Jeffrey W. Smith,et al.  Stochastic Gene Expression in a Single Cell , .

[21]  T. Kurtz The Relationship between Stochastic and Deterministic Models for Chemical Reactions , 1972 .

[22]  Philipp Thomas,et al.  Rigorous elimination of fast stochastic variables from the linear noise approximation using projection operators. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.