Model Revision of Boolean Regulatory Networks at Stable State

Abstract

Models of biological regulatory networks are essential to understand the cellular processes. However, the definition of such models is still mostly manually performed, and consequently prone to error. Moreover, as new experimental data is acquired, models need to be revised and updated. Here, we propose a model revision tool, capable of proposing the set of minimum repairs to render a model consistent with a set of experimental observations. We consider four possible repair operations, giving preference to function repairs over topological ones. Also, we consider observations at stable state, i.e., we do not consider the model dynamics. We evaluate our tool on five known logical models. We perform random changes considering several parameter configurations to assess the tool repairing capabilities. Whenever a model is repaired under the time limit, the tool successfully produces the optimal solutions to repair the model. Also, the number of repair operations required is less than or equal to the number of random changes applied to the original model.