A Boolean network model of bacterial quorum-sensing systems. (2018)

• Record Type: Journal Article
• Authors: Ruz, Gonzalo A.; Zúñiga, Ana; Goles, Eric
• Is Part Of: International journal of data mining and bioinformatics. Volume 21:Number 2(2018)
• Page Start: 123
• Page End: 144
• Publication Date: 2018
• Subjects: gene regulatory networks -- quorum-sensing systems -- Boolean networks -- neural networks -- network inference; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.