Repairing Boolean regulatory networks using Answer Set Programming

Models of biological regulatory and signalling networks are increasingly used to formally describe and understand complex biological processes. Such models are often repaired whenever new observations become available, because the model cannot generate behaviours consistent with the new observations, or because the behaviours are contradictory. This process of model repair is often manual and therefore prone to errors. In this work, we describe biological regulatory and signalling networks using the Boolean formalism, where nodes are represented by Boolean variables denoting biological components and edges denote regulatory interactions between components. The evolution of each variable is defined by a Boolean function depending on the values of the regulators of the component. Here, we propose to repair the model by changing inconsistent functions, with four types of atomic repairs which can be further combined. The goal is to find the cardinality minimal set of repairs allowing the model to satisfy all available observations. The proposed method is implemented using Answer Set Programming (ASP) and is tested using data from Escherichia coli and Candida albicans organisms. Interestingly, the system finds adequate solutions to ensure consistency for all observations.