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Biosystems. 2007 Sep-Oct;90(2):535-45. Epub 2006 Dec 20.

A Markovian approach to the control of genetic regulatory networks.

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1
Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore. mpechenp@nus.edu.sg

Abstract

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.

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