Scalable mechanistic modelling of cellular signal transduction

Orateur : Markus KRANTZ (Humboldt University, Berlin, Germany)

Large-scale knowledge bases and models are becoming increasingly important to systematise and interpret empirical knowledge on cellular systems. In signalling networks, as opposed to metabolic networks, distinct modifications of and bonds between components combine into very large numbers of possible configurations, or microstates. These are essentially never measured in vivo, making explicit modelling strategies both impractical and problematic. I will present rxncon 2.0, the second generation rxncon language, as a tool to define signal transduction networks at the level of empirical data. By expressing both reactions and contingencies (contextual constraints on reactions) in terms of elemental states, both the combinatorial complexity and the discrepancy to empirical data can be minimised. It works as a higher-level language natural to biologists, which can be compiled into a range of graphical formats or executable models. The compilation targets include a parameter-free simulation logic that allows both network validation and qualitative simulation, and the regulatory graph for complete visualisation of even large signalling networks. To illustrate the method, I will present a mechanistic model of the molecular network that controls and executes the cell division cycle in baker’s yeast, which encompasses 229 distinct proteins and over 1200 unique reaction events. Taken together, the rxncon language combines mechanistic precision with scalability in a composable and compilable language, which is designed for building executable knowledge bases on the molecular biology of signalling systems.

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