Format

Send to:

Choose Destination
See comment in PubMed Commons below
BMC Syst Biol. 2013 Jul 8;7:58. doi: 10.1186/1752-0509-7-58.

Reaction-contingency based bipartite Boolean modelling.

Author information

  • 1Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr, 42, Berlin 10115, Germany. max.floettmann@biologie.hu-berlin.de

Abstract

BACKGROUND:

Intracellular signalling systems are highly complex, rendering mathematical modelling of large signalling networks infeasible or impractical. Boolean modelling provides one feasible approach to whole-network modelling, but at the cost of dequantification and decontextualisation of activation. That is, these models cannot distinguish between different downstream roles played by the same component activated in different contexts.

RESULTS:

Here, we address this with a bipartite Boolean modelling approach. Briefly, we use a state oriented approach with separate update rules based on reactions and contingencies. This approach retains contextual activation information and distinguishes distinct signals passing through a single component. Furthermore, we integrate this approach in the rxncon framework to support automatic model generation and iterative model definition and validation. We benchmark this method with the previously mapped MAP kinase network in yeast, showing that minor adjustments suffice to produce a functional network description.

CONCLUSIONS:

Taken together, we (i) present a bipartite Boolean modelling approach that retains contextual activation information, (ii) provide software support for automatic model generation, visualisation and simulation, and (iii) demonstrate its use for iterative model generation and validation.

PMID:
23835289
[PubMed - indexed for MEDLINE]
PMCID:
PMC3710479
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk