Abstract
Rule-based modelling allows very compact descriptions of protein-protein interaction networks. However, combinatorial complexity increases again when one attempts to describe formally the behaviour of the networks, which motivates the use of abstractions to make these models more coarse-grained.
Context-insensitive abstractions of the intrinsic flow of information among the sites of chemical complexes through the rules have been proposed to infer sound coarse-graining, providing an efficient way to find macro-variables and the corresponding reduced models. In this paper, we propose a framework to allow the tuning of the context-sensitivity of the information flow analyses and show how these finer analyses can be used to find fewer macro-variables and smaller reduced differential models.
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Camporesi, F., Feret, J., Hayman, J. (2013). Context-Sensitive Flow Analyses: A Hierarchy of Model Reductions. In: Gupta, A., Henzinger, T.A. (eds) Computational Methods in Systems Biology. CMSB 2013. Lecture Notes in Computer Science(), vol 8130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40708-6_17
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DOI: https://doi.org/10.1007/978-3-642-40708-6_17
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