TaBooN -- Boolean Network Synthesis Based on Tabu Search

TL;DR

This paper introduces TaBooN, a novel workflow that uses Tabu search to automatically synthesize Boolean networks from biological data, aiding in biological interpretation and modeling.

Contribution

The work presents an automated, data-driven method for Boolean network inference utilizing Tabu search to identify the most accurate models based on biological data.

Findings

Successfully infers Boolean networks from experimental data.

Uses Tabu search to optimize model selection.

Provides a platform for biological network analysis and prediction.

Abstract

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 modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of 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 modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present 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 with regards to 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 can also assist to evaluate and optimize the dynamic behaviour of the biological networks providing a reliable platform for further modelling and predictions.