Reaction-contingency based bipartite Boolean modelling

BMC Systems Biology

Marcus Krantz¹  Edda Klipp¹  Falko Krause¹  Max Flöttmann¹ 

[1] Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, Berlin 10115, Germany

关键词: Bipartite Boolean;    rxncon;    Boolean modelling;    Systems biology;    Signal transduction;   

Others  :  1142627
DOI  :  10.1186/1752-0509-7-58

 received in 2012-11-05, accepted in 2013-06-14,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Flöttmann et al.; licensee BioMed Central Ltd.

【 图 表 】

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