Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
2076894 | Biosystems | 2007 | 11 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Modelling and Simulation
Authors
Peter C.Y. Chen, Jeremy W. Chen,