The Community for Technology Leaders
RSS Icon
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
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.2011.107
REFERENCES
[1] A. Datta, A. Choudhary, M.L. Bittner, and E.R. Dougherty, “External Control in Markovian Genetic Regulatory Networks,” Machine Learning, vol. 52, nos. 1-2, pp. 169-181, 2003.
[2] A. Datta, R. Pal, A. Choudhary, and E.R. Dougherty, “Control Approaches for Probabilistic Gene Regulatory Networks,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp. 54-63, Jan. 2007.
[3] R. Pal, A. Datta, and E.R. Dougherty, “Optimal Infinite Horizon Control for Probabilistic Boolean Networks,” IEEE Trans. Signal Processing, vol. 54, no. 6, pp. 2375-2387, June 2006.
[4] R. Pal, A. Datta, M.L. Bittner, and E.R. Dougherty, “Intervention in Context-Sensitive Probabilistic Boolean Networks,” Bioinformatics, vol. 21, no. 7, pp. 1211-1218, 2005.
[5] T. Akutsu, M. Hayashida, W.-K. Ching, and M.K. Ng, “Control of Boolean Networks: Hardness Results and Algorithms for the Tree Structured Networks,” J. Theoretical Biology, vol. 244, no. 4, pp. 670-677, 2007.
[6] W. Ching, S. Zhang, Y. Jiao, T. Akutsu, N. Tsing, and A. Wong, “Optimal Control Policy for Probabilistic Boolean Networks with Hard Constraints,” IET Systems Biology, vol. 3, no. 2, pp. 90-99, Mar. 2009.
[7] Y. Cong, W. Ching, N. Tsing, and H. Leung, “On Finite-Horizon Control of Genetic Regulatory Networks with Multiple Hard-Constraints,” BMC Systems Biology vol. 4(Suppl 2), article S14, 2010.
[8] G. Vahedi, B. Faryabi, J.-F. Chamberland, A. Datta, and E.R. Dougherty, “Intervention in Gene Regulatory Networks via a Stationary Mean-First-Passage-Time Control Policy,” IEEE Trans. Biomedical Eng., vol. 55, no. 10, pp. 2319-2331, Oct. 2008.
[9] X. Qian, I. Ivanov, N. Ghaffari, and E.R. Dougherty, “Intervention in Gene Regulatory Networks via Greedy Control Policies Based on Long-Run Behavior,” BMC Systems Biology, vol. 3, article 61, 2009.
[10] D.P. Bertsekas, Dynamic Programming and Optimal Control. Athena Scientific, 2005.
[11] M.K. Ng, S.-Q. Zhang, W. Ching, and T. Akutsu, “A Control Model for Markovian Genetic Regulatory Networks,” Trans. Computational Systems Biology, vol. 4070, pp. 36-48, 2006.
[12] B. Faryabi, G. Vahedi, J.F. Chamberland, A. Datta, and E.R. Dougherty, “Optimal Constrained Stationary Intervention in Gene Regulatory Networks,” EURASIP J. Bioinformatics and Systems Biology, vol. 2008, article 620767, 2008.
[13] G. Vahedi, B. Faryabi, J.F. Chamberland, A. Datta, and E.R. Dougherty, “Optimal Intervention Strategies for Cyclic Therapeutic Methods,” IEEE Trans. Biomedical Eng., vol. 56, no. 2, pp. 281-291, Feb. 2009.
[14] X. Qian and E.R. Dougherty, “Effect of Function Perturbation on the Steady-State Distribution of Genetic Regulatory Networks: Optimal Structural Intervention,” IEEE Trans. Signal Processing, vol. 56, no. 10, pp. 4966-4976, Oct. 2008.
[15] A. Law and C. Wong, “Stanniocalcin-2 Is a Hif-1 Target gene that Promotes Cell Proliferation in Hypoxia,” Experimental Cell Research, vol. 316, no. 3, pp. 466-476, 2010.
[16] A. Law and C. Wong, “Stanniocalcin-2 Promotes Epithelialmesenchymal Transition and Invasiveness in Hypoxic Human Ovarian Cancer Cells,” Experimental Cell Research, vol. 316, no. 20, pp. 3425-3434, 2010.
[17] S.A. Kauffman, “Homeostasis and Differentiation in Random Genetic Control Networks,” Nature, vol. 224, pp. 177-178, 1969.
[18] I. Shmulevich, E.R. Dougherty, S. Kim, and W. Zhang, “Probabilistic Boolean Networks: A Rule-Based Uncertainty Model for Gene Regulatory Networks,” Bioinformatics, vol. 18, pp. 261-274, 2002.
[19] I. Shmulevich and E.R. Dougherty, Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. SIAM Press, 2010.
[20] M. Brun, E.R. Dougherty, and I. Shmulevich, “Steady-State Probabilities for Attractors in Probabilistic Boolean Networks,” Signal Processing, vol. 85, no. 10, pp. 1993-2013, 2005.
[21] S.A. Kauffman, The Origins of Order: Self-Organization and Selection in Evolution. Oxford Univ. Press, 1993.
[22] F. Li, T. Long, Y. Lu, Q. Ouyang, and C. Tang, “The Yeast Cell-Cycle Network Is Robustly Designed,” Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 4781-4786, 2004.
[23] P.J. Schweitzer, “Perturbation Theory and Finite Markov Chains,” J. Applied Probability, vol. 5, pp. 401-413, 1968.
[24] J.J. Hunter, “Stationary Distributions and Mean First Passage Times of Perturbed Markov Chains,” Linear Algebra and Its Applications, vol. 410, pp. 217-243, 2005.
[25] S.A. Kauffman, C. Peterson, B. Samuelsson, and C. Troein, “Genetic Networks with Canalyzing Boolean Rules are Always Stable,” Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 17102-17107, 2004.
[26] I. Shmulevich and E.R. Dougherty, Genomic Signal Processing. Princeton Univ. Press, 2007.
[27] I. Shmulevich, H. Lahdesmaki, E.R. Dougherty, J. Astola, and W. Zhang, “The Role of Certain Post Classes in Boolean Network Models of Genetic Networks,” Proc. Nat'l Academy of Sciences USA, vol. 100, pp. 10734-10739, 2003.
[28] X. Qian and E.R. Dougherty, “On the Long-Run Sensitivity of Probabilistic Boolean Networks,” J. Theoretical Biology, vol. 257, no. 4, pp. 560-577, 2009.
[29] M. Bittner, P. Meltzer, Y. Chen, Y. Jiang, E. Seftor, M. Hendrix, M. Radmacher, R. Simon, Z. Yakhini, and A. Ben-Dor, “Molecular Classification of Cutaneous Malignant Melanoma by Gene Expression Profiling,” Nature, vol. 406, pp. 536-540, 2000.
[30] A.T. Weeraratna, Y. Jiang, G. Hostetter, K. Rosenblatt, P. Duray, M. Bittner, and J. Trent, “Wnt5a Signaling Directly Affects Cell Motility and Invasion of Metastatic Melanoma,” Cancer Cell, vol. 1, pp. 279-288, 2002.
[31] S.K. Dissanayake, P.B. Olkhanud, M.P. O'Connell, A. Carter, A.D. French, T.C. Camilli, C.D. Emeche, K.J. Hewitt, D.T. Rosenthal, P.D. Leotlela, M.S. Wade, S.W. Yang, L. Brant, B.J. Nickoloff, J.L. Messina, A. Biragyn, K.S. Hoek, D.D. Taub, D.L. Longo, V.K. Sondak, S.M. Hewitt, and A.T. Weeraratna, “Wnt5a Regulates Expression of Tumor-Associated Antigens in Melanoma via Changes in Signal Transducers and Activators of Transcription 3 Phosphorylation,” Cancer Research, vol. 68, no. 24, pp. 10205-10214, 2008.
[32] S. Kim, H. Li, E.R. Dougherty, N.W. Cao, Y.D. Chen, M. Bittner, and E.B. Suh, “Can Markov Chain Models Mimic Biological Regulation?,” J. Biology Systems, vol. 10, no. 4, pp. 337-357, 2002.
[33] I. Miyazaki, S. Simizu, H. Okumura, S. Takagi, and H. Osada, “A Small-Molecule Inhibitor Shows that Pirin Regulates Migration of Melanoma Cells,” Nature Chemical Biology, vol. 6, pp. 667-673, 2010.
42 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool