Finite Time Distributions of Stochastically Modeled Chemical Systems with Absolute Concentration Robustness

Recent research in both the experimental and mathematical communities has focused on biochemical interaction systems that satisfy an “absolute concentration robustness” (ACR) property. The ACR property was first discovered experimentally when, in a number of different systems, the concentrations of key system components at equilibrium were observed to be robust to the total concentration levels of the system. Follow-up mathematical work focused on deterministic models of biochemical systems and demonstrated how chemical reaction network theory can be utilized to explain this robustness. Later mathematical work focused on the behavior of this same class of reaction networks, though under the assumption that the dynamics were stochastic. Under the stochastic assumption, it was proven that the system will undergo an extinction event with a probability of one so long as the system is conservative, showing starkly different long-time behavior than in the deterministic setting. Here we consider a general class ...

[1]  S. Varadhan,et al.  On the Support of Diffusion Processes with Applications to the Strong Maximum Principle , 1972 .

[2]  Péter Érdi,et al.  Mathematical Models of Chemical Reactions: Theory and Applications of Deterministic and Stochastic Models , 1989 .

[3]  David F. Anderson,et al.  Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks , 2008, Bulletin of mathematical biology.

[4]  R. Jackson,et al.  General mass action kinetics , 1972 .

[5]  Thomas G. Kurtz,et al.  Equivalence of Stochastic Equations and Martingale Problems , 2011 .

[6]  T. Kurtz Strong approximation theorems for density dependent Markov chains , 1978 .

[7]  Thomas G. Kurtz,et al.  Averaging for martingale problems and stochastic approximation , 1992 .

[8]  Alicia Dickenstein,et al.  Complex-linear invariants of biochemical networks. , 2012, Journal of theoretical biology.

[9]  Germán A. Enciso,et al.  Stochastic analysis of biochemical reaction networks with absolute concentration robustness , 2013, Journal of The Royal Society Interface.

[10]  Lea Popovic,et al.  Scaling limits of spatial chemical reaction networks , 2013 .

[11]  Jeremy Gunawardena,et al.  Dimerization and Bifunctionality Confer Robustness to the Isocitrate Dehydrogenase Regulatory System in Escherichia coli* , 2012, The Journal of Biological Chemistry.

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

[13]  Hans J. Stetter,et al.  Numerical polynomial algebra , 2004 .

[14]  David F. Anderson,et al.  Continuous Time Markov Chain Models for Chemical Reaction Networks , 2011 .

[15]  Stein Shiromoto,et al.  Lyapunov functions , 2012 .

[16]  U. Alon,et al.  Robustness in bacterial chemotaxis , 2022 .

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

[18]  Heinz Koeppl,et al.  Dynamical properties of Discrete Reaction Networks , 2013, Journal of mathematical biology.

[19]  J. Gunawardena Models in Systems Biology: The Parameter Problem and the Meanings of Robustness , 2010 .

[20]  Carsten Wiuf,et al.  Product-Form Poisson-Like Distributions and Complex Balanced Reaction Systems , 2015, SIAM J. Appl. Math..

[21]  T. Kurtz,et al.  Submitted to the Annals of Applied Probability ASYMPTOTIC ANALYSIS OF MULTISCALE APPROXIMATIONS TO REACTION NETWORKS , 2022 .

[22]  Thomas G. Kurtz,et al.  Stochastic Analysis of Biochemical Systems , 2015 .

[23]  Uri Alon,et al.  Input–output robustness in simple bacterial signaling systems , 2007, Proceedings of the National Academy of Sciences.

[24]  Carsten Wiuf,et al.  Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks , 2015, Bulletin of mathematical biology.

[25]  Hye-Won Kang,et al.  Central limit theorems and diffusion approximations for multiscale Markov chain models , 2012, 1208.3783.

[26]  Uri Alon,et al.  Sensitivity and Robustness in Chemical Reaction Networks , 2009, SIAM J. Appl. Math..

[27]  M. Feinberg,et al.  Structural Sources of Robustness in Biochemical Reaction Networks , 2010, Science.

[28]  M. Feinberg,et al.  Chemical mechanism structure and the coincidence of the stoichiometric and kinetic subspaces , 1977 .

[29]  Franco Blanchini,et al.  Structurally robust biological networks , 2011, BMC Systems Biology.