Statistical Genomics and Bioinformatics

Agent based modeling of biological processes

In order to better understand biological processes we need to develop mathematical models of the underlying molecular systems that are able to integrate many different kinds of experimental data and handle the complexity and scale of their operating mechanisms. Dynamical models have traditionally been described using ordinary differential equations, but the size of many biological pathways and the combinatorial complexity of the interactions between members makes their use intractable. Recently a family of ‘rule-based’ agent languages have been developed that are sufficiently flexible to allow many different types of experimental data to be combined in a unified framework and make the modelling of large pathways possible.

In collaboration with researchers at the James Hutton Institute we have used one such agent based language, ‘Kappa’, to begin building generic models of plant pathogen interactions (PPIs). During infection, pathogen associated molecular patterns (PAMPs) are recognised by the host plant and lead to activation of the MAP kinase signaling pathway and expression of downstream immune resistance genes in the nucleus. By building models of PPIs we hope to identify potential targets for intervention to improve the ability of host plants to resist and respond to pathogens.

interaction pathway sub-sectionSub-section of the conserved plant pathogen interaction pathway derived from KEGG illustrating the host response to bacterial pathogens mediated by the Microtubule associated protein kinase pathway (MEKK, MKK, MPK).

Further details from: Ian Simpson

Article date 2013


Statistical Genomics and Bioinformatics

Process and Systems Modelling

Statistical Methodology

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