Modeling mechanistic biological networks: An advanced Boolean approach

Motivation: The understanding of the molecular sources for diseases like cancer can be signicantly improved by computational models. Recently, Boolean networks have become very popular for modeling signaling and regulatory networks. However, such models rely on a set of Boolean functions which are in general not known. Unfortuna-tely, while detailed information on the molecular interactions becomes available in large scale through electronic databases, the information on the Boolean functions does not become available simultaneously and has to be included manually into the models, if at all known. Results: We propose a new Boolean approach which can directly utilize the mechanistic network information available through modern databases. The Boolean function is implicitly dened by the reaction mechanisms. Special care has been taken for the treatment of kinetic features like inhibition. The method has been applied to a signaling model combining the Wnt and MAPK pathway. Availability: A sample C++ implementation of the proposed method is available for Linux and compatible systems throug