A Markovian approach to the control of genetic regulatory networks

This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives (without state feedback) the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the algorithm for a class of genetic networks. © 2007 Elsevier Ireland Ltd. All rights reserved.

• Type: Journal
• Year: 2007
• Where: BIOSYSTEMS
• Authors: Peter C. Y. Chen, Jeremy W. Chen
• Keywords: BIOSYSTEMS 2007, Gene Network, Markov Chain Model, State Transitions