Validating a threshold-based boolean model of regulatory networks on a biological organism

Authors:
Christian Darabos;Ferdinando Di Cunto;Marco Tomassini;Jason H. Moore;Paolo Provero;Mario Giacobini
Affiliations:
Computational Genetics Laboratory, Dartmouth College, Hanover, NH;Computational Biology Unit, Molecular Biotechnology Center, University of Torino, Italy and Department of Genetics, Biology and Biochemistry, University of Torino, Italy;Information Systems Department, Faculty of Business and Economics, University of Lausanne, Switzerland;Computational Genetics Laboratory, Dartmouth College, Hanover, NH;Computational Biology Unit, Molecular Biotechnology Center, University of Torino, Italy;Computational Biology Unit, Molecular Biotechnology Center, University of Torino, Italy and Department of Animal Production Epidemiology and Ecology, University of Torino, Italy
Venue:
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2011

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Abstract

Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean update functions attached to each element, to validate a previously proposed threshold-based additive update function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean function to match this regime. Results show that there is a higher degree of overlap between the original function and the additive function than with random update function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.