ArticleBMC systems biology2013
Reaction-contingency based bipartite Boolean modelling.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models.BMC bioinformatics · 2023Article
- Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation.Scientific reports · 2022Article
- A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models.NPJ systems biology and applications · 2020Article
- Automated visualization of rule-based models.PLoS computational biology · 2017Article
- Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks.BMC systems biology · 2015Article
- Computing smallest intervention strategies for multiple metabolic networks in a boolean model.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Network reconstruction and validation of the Snf1/AMPK pathway in baker's yeast based on a comprehensive literature review.NPJ systems biology and applications · 2015Article
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4 authors.
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Abstract
backgroundIntracellular 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.
resultsHere, 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.
conclusionsTaken 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.
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