TaBooN Boolean Network Synthesis Based on Tabu Search

Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modeling. In this undertaking, the network provides a suitable framework for modeling the interactions between molecules. A biological network comprises nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modeled by the definition of a dynamical system. Among the different network categories, the Boolean network offers a reliable qualitative framework for modeling the biological systems. Automatically synthesizing a Boolean network from experimental data, therefore, remains a necessary but challenging issue. This study, presents taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model regarding biological knowledge and experiments must be found. In the taboon work-flow, the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that helps biologists synthesize a reliable model faster and assist in evaluating and optimizing the biological networks' dynamic behavior, further modeling and predictions.