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TaBooN - Boolean Network Synthesis Based on Tabu Search

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.
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https://hal.archives-ouvertes.fr/hal-03167664
Contributor : Frédéric Davesne <>
Submitted on : Friday, March 12, 2021 - 11:52:19 AM
Last modification on : Thursday, July 1, 2021 - 11:03:16 AM

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Sara Sadat Aghamiri, Franck Delaplace. TaBooN - Boolean Network Synthesis Based on Tabu Search. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Institute of Electrical and Electronics Engineers, In press, pp.1-1. ⟨10.1109/TCBB.2021.3063817⟩. ⟨hal-03167664⟩

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