Automated abstraction methodology for genetic regulatory networks

Hiroyuki Kuwahara*, Chris J. Myers, Michael S. Samoilov, Nathan A. Barker, Adam P. Arkin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

In order to efficiently analyze the complicated regulatory systems often encountered in biological settings, abstraction is essential. This paper presents an automated abstraction methodology that systematically reduces the small-scale complexity found in genetic regulatory network models, while broadly preserving the large-scale system behavior. Our method first reduces the number of reactions by using rapid equilibrium and quasi-steady-state approximations as well as a number of other stoichiometry-simplifying techniques, which together result in substantially shortened simulation time. To further reduce analysis time, our method can represent the molecular state of the system by a set of scaled Boolean (or n-ary) discrete levels. This results in a chemical master equation that is approximated by a Markov chain with a much smaller state space providing significant analysis time acceleration and computability gains. The genetic regulatory network for the phage λ lysis/lysogeny decision switch is used as an example throughout the paper to help illustrate the practical applications of our methodology.

Original languageEnglish (US)
Title of host publicationTransactions on Computational Systems Biology VI
Pages150-175
Number of pages26
Volume4220 LNBI
StatePublished - 2006
Externally publishedYes
Event4th International Conference on Computational Methods in Systems Biology - Edinburgh, United Kingdom
Duration: Apr 3 2005Apr 5 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4220 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Computational Methods in Systems Biology
CountryUnited Kingdom
CityEdinburgh
Period04/3/0504/5/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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