Boolean modeling of biochemical networks

Tomáš Helikar, Naomi Kochi, John Konvalina, Jim A. Rogers

Research output: Contribution to journalReview article

24 Scopus citations

Abstract

The use of modeling to observe and analyze the mechanisms of complex biochemical network function is becoming an important methodological tool in the systems biology era. Number of different approaches to model these networks have been utilized--they range from analysis of static connection graphs to dynamical models based on kinetic interaction data. Dynamical models have a distinct appeal in that they make it possible to observe these networks in action, but they also pose a distinct challenge in that they require detailed information describing how the individual components of these networks interact in living cells. Because this level of detail is generally not known, dynamic modeling requires simplifying assumptions in order to make it practical. In this review Boolean modeling will be discussed, a modeling method that depends on the simplifying assumption that all elements of a network exist only in one of two states.

Original languageEnglish (US)
Pages (from-to)16-25
Number of pages10
JournalOpen Bioinformatics Journal
Volume5
DOIs
Publication statusPublished - Jul 11 2011

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Keywords

  • Boolean networks
  • Dynamical modeling
  • Signal transduction
  • Systems biology

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Biomedical Engineering
  • Health Informatics

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