CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.01 - January/February

Subscribe

Issue No.01 - January/February (2012 vol.9)

pp: 123-136

Xiaoning Qian , Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA

E. R. Dougherty , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.107

ABSTRACT

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.

INDEX TERMS

Steady-state, Markov processes, Context, Malignant tumors, Probabilistic logic, Bioinformatics, Computational biology,melanoma., Gene regulatory networks, probabilistic Boolean networks, network intervention, Markov chain, stationary control policy

CITATION

Xiaoning Qian, E. R. Dougherty, "Intervention in Gene Regulatory Networks via Phenotypically Constrained Control Policies Based on Long-Run Behavior",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.9, no. 1, pp. 123-136, January/February 2012, doi:10.1109/TCBB.2011.107REFERENCES