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This article is part of the supplement: Proceedings of the Eighth Annual MCBIOS Conference. Computational Biology and Bioinformatics for a New Decade

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A CoD-based stationary control policy for intervening in large gene regulatory networks

Noushin Ghaffari1*, Ivan Ivanov2, Xiaoning Qian3 and Edward R Dougherty14

Author Affiliations

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

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

3 Department of Computer Science and Engineering, University of South Florida, 4202 E Fowler Ave., ENB 118, Tampa, FL 33620, USA

4 Translational Genomics Research Institute (TGEN), 400 North Fifth Street, Suite 1600, Phoenix, AZ 85004 USA

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BMC Bioinformatics 2011, 12(Suppl 10):S10  doi:10.1186/1471-2105-12-S10-S10

Published: 18 October 2011



One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network.


In this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model.


The newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy.