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Optimal control policy for probabilistic Boolean networks with hard constraints

Optimal control policy for probabilistic Boolean networks with hard constraints

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It is well known that the control/intervention of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases like cancer. For this purpose, both optimal finite-horizon control and infinite-horizon control policies have been proposed. Boolean networks (BNs) and its extension probabilistic Boolean networks (PBNs) as useful and effective tools for modelling gene regulatory systems have received much attention in the biophysics community. The control problem for these models has been studied widely. The optimal control problem in a PBN can be formulated as a probabilistic dynamic programming problem. In the previous studies, the optimal control problems did not take into account the hard constraints, i.e. to include an upper bound for the number of controls that can be applied to the captured PBN. This is important as more treatments may bring more side effects and the patients may not bear too many treatments. A formulation for the optimal finite-horizon control problem with hard constraints introduced by the authors. This model is state independent and the objective function is only dependent on the distance between the desirable states and the terminal states. An approximation method is also given to reduce the computational cost in solving the problem. Experimental results are given to demonstrate the efficiency of our proposed formulations and methods.

References

    1. 1)
      • S. Zhang , M. Hayashida , T. Akutsu , W. Ching , M. Ng . Algorithms for finding small attractors in Boolean networks. EURASIP J. Bioinformatics Syst. Biol.
    2. 2)
      • A. Datta , A. Choudhary , M.L. Bittner , E.R. Dougherty . External control in Markovian genetic regulatory networks. Mach. Learn , 169 - 191
    3. 3)
      • P. Smolen , D. Baxter , J. Byrne . Mathematical modeling of gene network. Neuron , 567 - 580
    4. 4)
      • M. Ng , S. Zhang , W. Ching , T. Akutsu . A control model for Markovian genetic regulatory network. Trans. Comput. Syst. Biol. , 36 - 48
    5. 5)
      • W. Ching , S. Zhang , M. Ng , T. Akutsu . An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks. Bioinformatics , 1511 - 1518
    6. 6)
      • I. Shmulevich , E.R. Dougherty , W. Zhang . Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics , 1319 - 1331
    7. 7)
      • D. Thieffry , A.M. Huerta , E. Perez-Rueda , J. Collado-Vides . From specific gene regulation to genomic networks: A global analysis of transcriptional regulation in Escherichia coli. BioEssays , 5 , 433 - 440
    8. 8)
      • I. Shmulevich , E.R. Dougherty , W. Zhang . Control of stationary behavior in probabilistic Boolean networks by means of structural intervention. Biol. Syst. , 431 - 46
    9. 9)
      • L.J. Steggles , R. Banks , O. Shaw , A. Wipat . Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach. Bioinformatics , 336 - 343
    10. 10)
      • W. Ching , M. Ng . (2006) Markov Chains: models, algorithms and applications, International Series on Operations Research and Management Science.
    11. 11)
      • W. Ching , E. Fung , M. Ng , T. Akutsu . On construction of stochastic genetic networks based on gene expression sequences. Int. J. Neural Syst. , 297 - 310
    12. 12)
      • I. Shmulevich , E.R. Dougherty , W. Zhang . From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proc. IEEE , 1778 - 1792
    13. 13)
      • W. Ching , M. Ng , K. Wong , E. Altman . Customer lifetime value: stochastic optimization approach. J. Oper. Res. Soc. , 860 - 868
    14. 14)
      • S.A. Kauffman . Metabolic stability and epigenesis in randomly constructed genetic nets. Theor. Biol. , 437 - 467
    15. 15)
      • B. Faryabi , A. Datta , E.R. Dougherty . On approximate stochastic control in genetic regulatory networks. IET Syst. Biol. , 1 , 361 - 368
    16. 16)
      • S.A. Kauffman . (1993) The origins of order: Self-organization and selection in evolution.
    17. 17)
      • S. Huang . Gene expression profiling, genetic networks, and cellular states: An integrating concept for tumorigenesis and drug discovery. J. Mol. Med. , 469 - 480
    18. 18)
      • R. Pal , A. Datta , M.L. Bittner , E.R. Dougherty . Intervention in context-sensitive probabilistic Boolean networks. Bioinformatics , 7 , 1211 - 1218
    19. 19)
      • Ching, W., Zhang, S., Yue, J., Akutsu, T., Wong, A.: `Optimal finite-horizon control for probabilistic Boolean networks with hard constraints', Int. Symp. Optimization Syst. Biol. (OSB 2007), 2007, p. 21–28, (LCNS, 7).
    20. 20)
      • H.D. Jong . Modeling and simulation of genetic regulatory systems: A literature review. J. Comp. Biol. , 67 - 103
    21. 21)
      • E. Altman . (1999) Constrained Markov decision processes.
    22. 22)
      • R. Pal , A. Datta , E.R. Dougherty . Optimal infinite-horizon control for probabilistic Boolean networks. IEEE Tran. Signal Process. , 6 , 2375 - 2387
    23. 23)
      • I. Shmulevich , E.R. Dougherty , S. Kim , W. Zhang . Probabilistic Boolean Networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics , 261 - 274
    24. 24)
      • I. Shmulevich , I. Gluhovsky , R.F. Hashimoto , E.R. Dougherty , W. Zhang . Steady-state analysis of genetic regulatory networks modeled by probabilistic Boolean networks. Comp. Funct. Genomics , 601 - 608
    25. 25)
      • E.R. Dougherty , S. Kim , Y. Chen . Coefficient of determination in nonlinear signal processing. Signal Process. , 2219 - 2235
    26. 26)
      • Akutsu, T., Hayashida, M., Zhang, S., Ching, W., Ng, M.: `Finding incoming global states for Boolean networks', Proc. Fifth IEEE Int. Workshop Genomic Signal Process. Stat., 2007.
    27. 27)
      • M. Ptashne . (2004) A genetic switch.
    28. 28)
      • C.Y. Chen , W. Chen . Markovian approach to the control of genetic regulatory network. Biosystems , 535 - 545
    29. 29)
      • T. Akutsu , M. Hayasida , W. Ching , M. Ng . Control of Boolean networks: hardness results and algorithms for tree structured networks. J. Theor. Biol. , 670 - 679
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