Reaction-contingency based bipartite Boolean modelling
| Autor: | Edda Klipp, Max Flöttmann, Falko Krause, Marcus Krantz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Theoretical computer science
Bipartite Boolean Computer science MAP Kinase Signaling System Systems biology Boolean modelling Saccharomyces cerevisiae Signal transduction rxncon Models Biological Rendering (computer graphics) 03 medical and health sciences 0302 clinical medicine Software Structural Biology Modelling and Simulation Intracellular signalling Molecular Biology 030304 developmental biology 0303 health sciences business.industry Systems Biology Applied Mathematics Reproducibility of Results Computer Science Applications Signalling Modeling and Simulation Bipartite graph Contingency business 030217 neurology & neurosurgery Research Article |
| Zdroj: | BMC Systems Biology; Vol 7 BMC Systems Biology |
| ISSN: | 1752-0509 |
| DOI: | 10.1186/1752-0509-7-58 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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