Title: A Boolean network model of bacterial quorum-sensing systems

Authors: Gonzalo A. Ruz; Ana Zúñiga; Eric Goles

Addresses: Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal Las Torres 2640, Peñalolén, Santiago, Chile; Center of Applied Ecology and Sustainability (CAPES-UC), Santiago, Chile ' Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal Las Torres 2640, Peñalolén, Santiago, Chile; Centre de Biochimie Structurale, INSERM U1054, CNRS UMR5048, University of Montpellier, Montpellier, France ' Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal Las Torres 2640, Peñalolén, Santiago, Chile

Abstract: There are several mathematical models to represent gene regulatory networks, one of the simplest is the Boolean network paradigm. In this paper, we reconstruct a regulatory network of bacterial quorum-sensing systems, in particular, we consider Paraburkholderia phytofirmans PsJN which is a plant growth promoting bacteria that produces positive effects in horticultural crops like tomato, potato and grape. To learn the regulatory network from temporal expression pattern of quorum-sensing genes at root plants, we present a methodology that considers the training of perceptrons for each gene and then the integration into one Boolean regulatory network. Using the proposed approach, we were able to infer a regulatory network model whose topology and dynamic exhibited was helpful to gain insight on the quorum-sensing systems regulation mechanism. We compared our results with REVEAL and Best-Fit extension algorithm, showing that the proposed neural network approach obtained a more biologically meaningful network and dynamics, demonstrating the effectiveness of the proposed method.

Keywords: gene regulatory networks; quorum-sensing systems; Boolean networks; neural networks; network inference.

DOI: 10.1504/IJDMB.2018.10017726

International Journal of Data Mining and Bioinformatics, 2018 Vol.21 No.2, pp.123 - 144

Accepted: 02 Oct 2018
Published online: 27 Nov 2018 *

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