Abstract
In this paper, we study a control model for gene intervention in a genetic regulatory network. At each time step, a finite number of controls are allowed to drive to some target states (i.e, some specific genes are on, and some specific genes are off) of a genetic network. We are interested in determining a minimum amount of control cost on a genetic network over a certain period of time such that the probabilities of obtaining such target states are as large as possible. This problem can be formulated as a stochastic dynamic programming model. However, when the number of genes is n, the number of possible states is exponentially increasing with n, and the computational cost of solving such stochastic dynamic programming model would be very huge. The main objective of this paper is to approximate the above control problem and formulate as a minimization problem with integer variables and continuous variables using dynamics of states probability distribution of genes. Our experimental results show that our proposed formulation is efficient and quite effective for solving control gene intervention in a genetic network.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akutsu, T., Hayashida, M., Ching, W., Ng, M.: On the Complexity of Finding Control Strategies for Boolean Networks. In: The Fourth Asia Pacific Bioinformatics Conference, Taiwan (February 13-16, 2006)
Chui, C., Chen, G.: Linear Systems and Optimal Control. Springer, New York (1989)
Datta, A., Bittner, M.L., Dougherty, E.R.: External control in Markovian genetic regulatory networks. Machine Learning 52, 169–191 (2003)
Dougherty, E.R., Kim, S., Chen, Y.: Coefficient of determination in nonlinear signal processing. Signal Processing 80, 2219–2235 (2000)
Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. Theoretical Biology 22, 437–467 (1969)
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)
Kauffman, S.A., Levin, S.: Towards a general theory of adaptive walks on rugged landscapes. Theoretical Biology 128, 11–45 (1987)
Shmulevich, I., Dougherty, E.R., Kim, S., Zhang, W.: Probabilistic Boolean Networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 261–274 (2002)
Shmulevich, I., Dougherty, E.R., Zhang, W.: Gene perturbation and intervention in probabilistic boolean networks. Bioinformatics 18, 1319–1331 (2002)
Shmulevich, I., Dougherty, E.R., Zhang, W.: Control of stationary behavior in probabilistic boolean networks by means of structural intervention. Biological Systems 10, 431–446 (2002)
Sierksma, G.: Linear and Interger Programming: Theory and Practice, 2nd ed. (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ng, M.K., Zhang, SQ., Ching, WK., Akutsu, T. (2006). A Control Model for Markovian Genetic Regulatory Networks. In: Priami, C., Hu, X., Pan, Y., Lin, T.Y. (eds) Transactions on Computational Systems Biology V. Lecture Notes in Computer Science(), vol 4070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790105_4
Download citation
DOI: https://doi.org/10.1007/11790105_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36048-3
Online ISBN: 978-3-540-36049-0
eBook Packages: Computer ScienceComputer Science (R0)