Steady State Analysis of Boolean Models: A Dimension Reduction Approach

Alan Veliz-Cuba, David Murrugarra

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Boolean models have been used to study biological systems where it is of interest to understand the qualitative behavior of the system or when the precise regulatory mechanisms are unknown. A feature of especial interest of Boolean models are the states where the system is invariant over time, because they correspond to stable patterns of the biological system. Thus, finding steady states or fixed points is an important problem in computational systems biology.Although Boolean networks provide a strong mathematical framework, the analysis by simulation is difficult for models of large size. Thus, it is necessary to develop tools to analyze large Boolean models other than by exhaustive simulation.Here we present an approach based on dimension reduction that allows us to study large Boolean models by systematically removing nodes without changing the number of steady states.

Original languageEnglish
Title of host publicationAlgebraic and Discrete Mathematical Methods for Modern Biology
Pages121-139
Number of pages19
ISBN (Electronic)9780128012710
DOIs
StatePublished - Mar 25 2015

Keywords

  • Boolean model
  • Fixed points
  • Model reduction
  • Polynomial algebra
  • Steady state

ASJC Scopus subject areas

  • Mathematics (all)

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