Research article

Intervention in gene regulatory networks via greedy control policies based on long-run behavior

Xiaoning Qian^1,2 , Ivan Ivanov³ , Noushin Ghaffari¹ and Edward R Dougherty^1,4,5

¹Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, 77843, USA

²Department of Statistics, Texas A&M University, College Station, TX, 77843, USA

³Department of Veterinary Physiology & Pharmacology, Texas A&M University, College Station, TX, 77843, USA

⁴Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ, 85004, USA

⁵Department of Pathology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA

author email corresponding author email

BMC Systems Biology 2009, 3:61doi:10.1186/1752-0509-3-61

Published:	15 June 2009

Abstract

Background

A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks.

Results

In this paper, we propose three new greedy stationary control policies by directly investigating the effects on the network long-run behavior. Similar to the recently proposed mean-first-passage-time (MFPT) control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming. They can be used to design stationary control policies that avoid the need for a user-defined cost function because they are based directly on long-run network behavior; they can be used as an alternative to dynamic programming algorithms when the latter are computationally prohibitive; and they can be used to predict the best control gene with reduced computational complexity, even when one is employing dynamic programming to derive the final control policy. We compare the performance of these three greedy control policies and the MFPT policy using randomly generated probabilistic Boolean networks and give a preliminary example for intervening in a mammalian cell cycle network.

Conclusion

The newly proposed control policies have better performance in general than the MFPT policy and, as indicated by the results on the mammalian cell cycle network, they can potentially serve as future gene therapeutic intervention strategies.