Boolean Modeling of Biochemical Networks

Helikar, Tomas; Kochi, Naomi; Konvalina, John; A. Rogers, Jim

Boolean Modeling of Biochemical Networks

Tomas Helikar¹, Naomi Kochi², John Konvalina³, Jim A. Rogers^{*, 1, 3}

¹ Department of Pathology and Microbiology, University of Nebraska Medical Center, 983135 Nebraska Medical Center, Omaha, NE 68198, USA

² Department of Genetics, Cell Biology, and Anatomy, University of Nebraska Medical Center, 985805 Nebraska Medical Center, Omaha, NE 68198, USA

³ Department of Mathematics, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE 68182, USA

Article Information

Identifiers and Pagination:

Year: 2011
Volume: 5
First Page: 16
Last Page: 25
Publisher ID: TOBIOIJ-5-16
DOI: 10.2174/1875036201105010016

Article History:

Received Date: 12/10/2009
Revision Received Date: 02/07/2010
Acceptance Date: 04/08/2010
Electronic publication date: 02/02/2011
Collection year: 2011

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© 2011 Helikar et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

^* Address correspondence to this author at the Department of Mathematics, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE 68182, USA; Tel: 402 554-3110: Fax: 402 554-2975; E-mail: jrogers@unmc.edu

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.

Keywords: Systems biology, dynamical modeling, signal transduction, boolean networks.

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RESEARCH ARTICLE

Boolean Modeling of Biochemical Networks

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