External Control in Markovian Genetic Regulatory Networks
Machine Learning
Genomic Signal Processing (Princeton Series in Applied Mathematics)
Genomic Signal Processing (Princeton Series in Applied Mathematics)
Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks
Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks
A control model for markovian genetic regulatory networks
Transactions on Computational Systems Biology V
Optimal infinite-horizon control for probabilistic Boolean networks
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing - Part I
A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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A salient purpose for studying gene regulatory networks is to derive intervention strategies to identify potential drug targets and design gene-based therapeutic intervention. Optimal and approximate intervention strategies based on the transition probability matrix of the underlying Markov chain have been studied extensively for probabilistic Boolean networks. While the key goal of control is to reduce the steady-state probability mass of undesirable network states, in practice it is important to limit collateral damage and this constraint should be taken into account when designing intervention strategies with network models. In this paper, we propose two new phenotypically constrained stationary control policies by directly investigating the effects on the network long-run behavior. They are derived to reduce the risk of visiting undesirable states in conjunction with constraints on the shift of undesirable steady-state mass so that only limited collateral damage can be introduced. We have studied the performance of the new constrained control policies together with the previous greedy control policies to randomly generated probabilistic Boolean networks. A preliminary example for intervening in a metastatic melanoma network is also given to show their potential application in designing genetic therapeutics to reduce the risk of entering both aberrant phenotypes and other ambiguous states corresponding to complications or collateral damage. Experiments on both random network ensembles and the melanoma network demonstrate that, in general, the new proposed control policies exhibit the desired performance. As shown by intervening in the melanoma network, these control policies can potentially serve as future practical gene therapeutic intervention strategies.