Intervention in gene regulatory networks via a stationary mean-first-passage-time control policy.
A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention. To date, optimal stochastic intervention has been studied in the context of probabilistic Boolean networks, with the control policy based on the transition probability matrix of the associated Markov chain and dynamic programming used to find optimal control policies. Dynamical programming algorithms are problematic owing to their high computational complexity. Two additional computationally burdensome issues that arise are the potential for controlling the network and identifying the best gene for intervention. This paper proposes an algorithm based on mean first-passage time that assigns a stationary control policy for each gene candidate. It serves as an approximation to an optimal control policy and, owing to its reduced computational complexity, can be used to predict the best control gene. Once the best control gene is identified, one can derive an optimal policy or simply utilize the approximate policy for this gene when the network size precludes a direct application of dynamic programming algorithms. A salient point is that the proposed algorithm can be model-free. It can be directly designed from time-course data without having to infer the transition probability matrix of the network.
Department of Electrical and Computer Engineering, Texas A&M Univesity, College Station, TX 77843, USA. golnaz@tamu.edu
Golnaz
G
Vahedi
Babak
B
Faryabi
Jean-Francois
JF
Chamberland
Aniruddha
A
Datta
Edward R
ER
Dougherty
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003491:Cybernetics / Q000379:methods; D005786:Gene Expression Regulation / Q000502:physiology; D053263:Gene Regulatory Networks / Q000502:physiology; D005818:Genetic Engineering / Q000379:methods; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008545:Melanoma / Q000235:genetics / Q000378:metabolism; D008957:Models, Genetic; D016017:Odds Ratio; D012016:Reference Values; D012107:Research Design; D013997:Time Factors