CANTATA-prediction of missing links in Boolean networks using genetic programming

Motivation: Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks. Results: Initial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies.

Automated summary

This article introduces a method called CANTATA, which supports the integration of information into regulatory networks and retrieves potential underlying regulations by optimizing the static and dynamic properties of these networks. The results show that the algorithm can predict missing interactions and the resulting models can be used to hypothesize the biological impact.