@inproceedings{d78f82cc40b346e594face3941935733,

title = "A control model for Markovian genetic regulatory networks",

abstract = "In this paper, we study a control model for gene intervention in a genetic regulatory network. At each time step, a finite number of controls are allowed to drive to some target states (i.e, some specific genes are on, and some specific genes are off) of a genetic network. We are interested in determining a minimum amount of control cost on a genetic network over a certain period of time such that the probabilities of obtaining such target states are as large as possible. This problem can be formulated as a stochastic dynamic programming model. However, when the number of genes is n, the number of possible states is exponentially increasing with n, and the computational cost of solving such stochastic dynamic programming model would be very huge. The main objective of this paper is to approximate the above control problem and formulate as a minimization problem with integer variables and continuous variables using dynamics of states probability distribution of genes. Our experimental results show that our proposed formulation is efficient and quite effective for solving control gene intervention in a genetic network.",

author = "Ng, {Michael K.} and Zhang, {Shu Qin} and Ching, {Wai Ki} and Tatsuya Akutsu",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2005 IEEE International Conference on Granular Computing ; Conference date: 25-07-2005 Through 27-07-2005",

year = "2006",

doi = "10.1007/11790105_4",

language = "English",

isbn = "3540360484",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "36--48",

booktitle = "Transactions on Computational Systems Biology V",

address = "Germany",

}