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

Bioinformatics. 2004 Apr 12;20(6):924-30. doi: 10.1093/bioinformatics/bth008. Epub 2004 Jan 29.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Feedback
  • Gene Expression Profiling / methods*
  • Gene Expression Regulation / genetics*
  • Humans
  • Markov Chains
  • Melanoma / genetics*
  • Melanoma / secondary
  • Models, Genetic*
  • Models, Statistical*
  • Skin Neoplasms / genetics*
  • Skin Neoplasms / secondary