Reachability analysis for switched affine systems and its application to controlled stochastic biochemical reaction networks

Under suitable assumptions, the moments of a controlled stochastic biochemical reaction network can be computed as the solution of a switched affine system. Motivated by this application, we propose a new method to approximate projections of the reachable set of a switched affine system onto a plane of interest. Our method does not require the computation of the full reachable set, thus allowing us to efficiently analyze the moments of a species of interest in arbitrarily large biochemical networks. To illustrate the benefits of the proposed method we consider a controlled gene expression model involving two species: the mRNA and the corresponding protein. The proposed approach can be used to estimate the reachable set of the protein mean and variance, under less stringent assumptions than those adopted in the literature. Specifically, we address the cases of multiple controlled reactions and heterogeneous population.

[1]  Jeffrey J. Tabor,et al.  Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals , 2014, Nature Methods.

[2]  Franco Blanchini,et al.  Switched Positive Linear Systems , 2015, Found. Trends Syst. Control..

[3]  Francesca Parise,et al.  On the use of hyperplane methods to compute the reachable set of controlled stochastic biochemical reaction networks , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[4]  Alberto Bemporad,et al.  Optimal control of continuous-time switched affine systems , 2006, IEEE Transactions on Automatic Control.

[5]  F. Fages,et al.  Long-term model predictive control of gene expression at the population and single-cell levels , 2012, Proceedings of the National Academy of Sciences.

[6]  Mario di Bernardo,et al.  Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks , 2011, Autom..

[7]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[8]  Corentin Briat,et al.  Computer control of gene expression: Robust setpoint tracking of protein mean and variance using integral feedback , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[9]  Claudio Altafini,et al.  The reachable set of a linear endogenous switching system , 2002, Syst. Control. Lett..

[10]  Hai Lin,et al.  Switched Linear Systems: Control and Design , 2006, IEEE Transactions on Automatic Control.

[11]  G. Vinnicombe,et al.  Fundamental limits on the suppression of molecular fluctuations , 2010, Nature.

[12]  Chang Hyeong Lee,et al.  A moment closure method for stochastic reaction networks. , 2009, The Journal of chemical physics.

[13]  Francesca Parise,et al.  On the reachable set of the controlled gene expression system , 2014, 53rd IEEE Conference on Decision and Control.

[14]  D. Gillespie A rigorous derivation of the chemical master equation , 1992 .

[15]  D. Pincus,et al.  In silico feedback for in vivo regulation of a gene expression circuit , 2011, Nature Biotechnology.

[16]  John Lygeros,et al.  Designing experiments to understand the variability in biochemical reaction networks , 2013, Journal of The Royal Society Interface.

[17]  John Lygeros,et al.  Iterative experiment design guides the characterization of a light-inducible gene expression circuit , 2015, Proceedings of the National Academy of Sciences.

[18]  B. Krogh,et al.  Hyperplane method for reachable state estimation for linear time-invariant systems , 1991 .