There has been much progress in recent years towards building larger and larger computational models for biochemical networks, driven by advances both in high throughput data techniques, and in computational modelling and simulation. Such models are often given as unstructured lists of species and interactions between them, making it very difficult to understand the logicome of the network, i.e. the logical connections between its main actors. In this project we introduce a method to generate the logicome of a reaction based biochemical network. The starting point is an ODE model associated to the network, that is used to extract the logicome of the network in terms of a Boolean circuit over a given set of key nodes. The main technique used in generating the logicome involves the simulation of all knock-out mutant models over a set of key species and discretizing the concentrations of these species over all simulations. We apply this method to the EGFR signalling pathway and extract the logicome of a model with 103 species and 148 reactions as a Boolean circuit with 8 nodes. The main advantage of the logicome is that it allows the modeller to focus on a small set of key nodes, while abstracting away from the rest of the model, seen as biochemical implementation details of the model.
 Dennis YQWang, Luca Cardelli, Andrew Phillips, Nir Piterman, and Jasmin Fisher. Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics. BMC Systems Biology, 3(1): 1–18, 2009.
. Jorrit J Hornberg, Bernd Binder, Frank J Bruggeman, Birgit Schoeberl, Reinhart Heinrich, and Hans V Westerhoff. Control of MAPK signalling: from complexity to what really matters. Oncogene, 24(36): 5533–5542, 2005.