A control model for Markovian genetic regulatory networks

Kwok Po NG*, Shu Qin Zhang, Wai Ki Ching, Tatsuya Akutsu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

24 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationTransactions on Computational Systems Biology V
PublisherSpringer Verlag
Pages36-48
Number of pages13
ISBN (Print)3540360484, 9783540360483
DOIs
Publication statusPublished - 2006
Event2005 IEEE International Conference on Granular Computing - Beijing, China
Duration: 25 Jul 200527 Jul 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4070 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2005 IEEE International Conference on Granular Computing
Country/TerritoryChina
CityBeijing
Period25/07/0527/07/05

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'A control model for Markovian genetic regulatory networks'. Together they form a unique fingerprint.

Cite this