Viability, invariance and reachability for controlled piecewise deterministic Markov processes associated to gene networks
ESAIM: Control, Optimisation and Calculus of Variations, Tome 18 (2012) no. 2, pp. 401-426.

We aim at characterizing viability, invariance and some reachability properties of controlled piecewise deterministic Markov processes (PDMPs). Using analytical methods from the theory of viscosity solutions, we establish criteria for viability and invariance in terms of the first order normal cone. We also investigate reachability of arbitrary open sets. The method is based on viscosity techniques and duality for some associated linearized problem. The theoretical results are applied to general On/Off systems, Cook's model for haploinsufficiency, and a stochastic model for bacteriophage λ.

DOI : 10.1051/cocv/2010103
Classification : 49L25, 60J25, 93E20, 92C42
Mots-clés : viscosity solutions, pdmp, gene networks
@article{COCV_2012__18_2_401_0,
     author = {Goreac, Dan},
     title = {Viability, invariance and reachability for controlled piecewise deterministic {Markov} processes associated to gene networks},
     journal = {ESAIM: Control, Optimisation and Calculus of Variations},
     pages = {401--426},
     publisher = {EDP-Sciences},
     volume = {18},
     number = {2},
     year = {2012},
     doi = {10.1051/cocv/2010103},
     mrnumber = {2954632},
     language = {en},
     url = {http://archive.numdam.org/articles/10.1051/cocv/2010103/}
}
TY  - JOUR
AU  - Goreac, Dan
TI  - Viability, invariance and reachability for controlled piecewise deterministic Markov processes associated to gene networks
JO  - ESAIM: Control, Optimisation and Calculus of Variations
PY  - 2012
SP  - 401
EP  - 426
VL  - 18
IS  - 2
PB  - EDP-Sciences
UR  - http://archive.numdam.org/articles/10.1051/cocv/2010103/
DO  - 10.1051/cocv/2010103
LA  - en
ID  - COCV_2012__18_2_401_0
ER  - 
%0 Journal Article
%A Goreac, Dan
%T Viability, invariance and reachability for controlled piecewise deterministic Markov processes associated to gene networks
%J ESAIM: Control, Optimisation and Calculus of Variations
%D 2012
%P 401-426
%V 18
%N 2
%I EDP-Sciences
%U http://archive.numdam.org/articles/10.1051/cocv/2010103/
%R 10.1051/cocv/2010103
%G en
%F COCV_2012__18_2_401_0
Goreac, Dan. Viability, invariance and reachability for controlled piecewise deterministic Markov processes associated to gene networks. ESAIM: Control, Optimisation and Calculus of Variations, Tome 18 (2012) no. 2, pp. 401-426. doi : 10.1051/cocv/2010103. http://archive.numdam.org/articles/10.1051/cocv/2010103/

[1] O. Alvarez and A. Tourin, Viscosity solutions of nonlinear integro-differential equations. Ann. Inst. Henri Poincaré, Anal. non linéaire 13 (1996) 293-317. | Numdam | MR | Zbl

[2] J.-P. Aubin, Viability Theory. Birkhäuser (1992). | MR | Zbl

[3] J.-P. Aubin and G. Da Prato, Stochastic viability and invariance. Ann. Sc. Norm. Pisa 27 (1990) 595-694. | Numdam | MR | Zbl

[4] J.-P. Aubin and H. Frankowska, Set Valued Analysis. Birkhäuser (1990). | MR | Zbl

[5] M. Bardi and I. Capuzzo-Dolcetta, Optimal control and viscosity solutions of Hamilton-Jacobi- Bellman equations. Systems and Control : Foundations and Applications, Birkhäuser (1997). | MR | Zbl

[6] M. Bardi and P. Goatin, Invariant sets for controlled degenerate diffusions : a viscosity solutions approach, in Stochastic analysis, control, optimization and applications, Systems Control Found. Appl., Birkhäuser, Boston, MA (1999) 191-208. | MR | Zbl

[7] M. Bardi and R. Jensen, A geometric characterization of viable sets for controlled degenerate diffusions. Set-Valued Anal. 10 (2002) 129-141. | MR | Zbl

[8] G. Barles and C. Imbert, Second-order elliptic integro-differential equations : Viscosity solutions theory revisited. Ann. Inst. Henri Poincaré, Anal. non linéaire 25 (2008) 567-585. | Numdam | MR | Zbl

[9] G. Barles and E.R. Jakobsen, On the convergence rate of approximation schemes for Hamilton-Jacobi-Bellman equations. ESAIM : M2AN 36 (2002) 33-54. | Numdam | MR | Zbl

[10] R. Buckdahn, S. Peng, M. Quincampoix and C. Rainer, Existence of stochastic control under state constraints. C. R. Acad. Sci. Paris Sér. I Math. 327 (1998) 17-22. | MR | Zbl

[11] R. Buckdahn, D. Goreac and M. Quincampoix, Stochastic optimal control and linear programming approach. Appl. Math. Opt. 63 (2011) 257-276. | MR | Zbl

[12] D.L. Cook, A.N. Gerber and S.J. Tapscott, Modelling stochastic gene expression : Implications for haploinsufficiency. Proc. Natl. Acad. Sci. USA 95 (1998) 15641-15646.

[13] A. Crudu, A. Debussche and O. Radulescu, Hybrid stochastic simplifications for multiscale gene networks. BMC Systems Biology 3 (2009).

[14] M.H.A. Davis, Markov Models and Optimization, Monographs on Statistics and Applied probability 49. Chapman & Hall (1993). | MR | Zbl

[15] M. Delbrück, Statistical fluctuations in autocatalytic reactions. J. Chem. Phys. 8 (1940) 120-124.

[16] S. Gautier and L. Thibault, Viability for constrained stochastic differential equations. Differential Integral Equations 6 (1993) 1395-1414. | MR | Zbl

[17] J. Hasty, J. Pradines, M. Dolnik and J.J. Collins, Noise-based switches and amplifiers for gene expression. PNAS 97 (2000) 2075-2080.

[18] H.M. Soner, Optimal control with state-space constraint. II. SIAM J. Control Optim. 24 (1986) 1110-1122. | MR | Zbl

[19] X. Zhu and S. Peng, The viability property of controlled jump diffusion processes. Acta Math. Sinica 24 (2008) 1351-1368. | MR | Zbl

Cité par Sources :