GraTeLPy: graph-theoretic linear stability analysis

Background A biochemical mechanism with mass action kinetics can be represented as a directed bipartite graph (bipartite digraph), and modeled by a system of differential equations. If the differential equations (DE) model can give rise to some instability such as multistability or Turing instabilit...

Full description

Saved in:
Bibliographic Details
Published inBMC systems biology Vol. 8; no. 1; p. 22
Main Authors Walther, Georg R, Hartley, Matthew, Mincheva, Maya
Format Journal Article
LanguageEnglish
Published London BioMed Central 27.02.2014
BioMed Central Ltd
Subjects
Online AccessGet full text
ISSN1752-0509
1752-0509
DOI10.1186/1752-0509-8-22

Cover

More Information
Summary:Background A biochemical mechanism with mass action kinetics can be represented as a directed bipartite graph (bipartite digraph), and modeled by a system of differential equations. If the differential equations (DE) model can give rise to some instability such as multistability or Turing instability, then the bipartite digraph contains a structure referred to as a critical fragment. In some cases the existence of a critical fragment indicates that the DE model can display oscillations for some parameter values. We have implemented a graph-theoretic method that identifies the critical fragments of the bipartite digraph of a biochemical mechanism. Results GraTeLPy lists all critical fragments of the bipartite digraph of a given biochemical mechanism, thus enabling a preliminary analysis on the potential of a biochemical mechanism for some instability based on its topological structure. The correctness of the implementation is supported by multiple examples. The code is implemented in Python, relies on open software, and is available under the GNU General Public License. Conclusions GraTeLPy can be used by researchers to test large biochemical mechanisms with mass action kinetics for their capacity for multistability, oscillations and Turing instability.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1752-0509
1752-0509
DOI:10.1186/1752-0509-8-22