External control in Markovian genetic regulatory networks: the imperfect information case. Academic Article

abstract

• Probabilistic Boolean Networks, which form a subclass of Markovian Genetic Regulatory Networks, have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. In an earlier paper, we introduced external control into Markovian Genetic Regulatory networks. More precisely, given a Markovian genetic regulatory network whose state transition probabilities depend on an external (control) variable, a Dynamic Programming-based procedure was developed by which one could choose the sequence of control actions that minimized a given performance index over a finite number of steps. The control algorithm of that paper, however, could be implemented only when one had perfect knowledge of the states of the Markov Chain. This paper presents a control strategy that can be implemented in the imperfect information case, and makes use of the available measurements which are assumed to be probabilistically related to the states of the underlying Markov Chain.

authors

• Datta, Aniruddha
• Dougherty, Edward

published proceedings

• Bioinformatics

altmetric score

• 3

author list (cited authors)

• Datta, A., Choudhary, A., Bittner, M. L., & Dougherty, E. R.

citation count

• 125

complete list of authors

• Datta, Aniruddha||Choudhary, Ashish||Bittner, Michael L||Dougherty, Edward R

publication date

• April 2004

publisher

• Oxford University Press (OUP)  Publisher

published in

• Bioinformatics  Journal

keywords

• Algorithms
• Feedback
• Gene Expression Profiling
• Gene Expression Regulation
• Humans
• Markov Chains
• Melanoma
• Models, Genetic
• Models, Statistical
• Skin Neoplasms

PubMed Central ID

• 14751971

Digital Object Identifier (DOI)

• 10.1093/bioinformatics/bth008

start page

• 924

end page

• 930

volume

• 20

issue

• 6

URL

• http://dx.doi.org/10.1093/bioinformatics/bth008