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Author (up) Ruz, G.A.; Zuniga, A.; Goles, E. doi  openurl
  Title A Boolean network model of bacterial quorum-sensing systems Type Journal Article
  Year 2018 Publication International Journal Of Data Mining And Bioinformatics Abbreviated Journal Int. J. Data Min. Bioinform.  
  Volume 21 Issue 2 Pages 123-144  
  Keywords gene regulatory networks; quorum-sensing systems; Boolean networks; neural networks; network inference  
  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.  
  Address [Ruz, Gonzalo A.; Zuniga, Ana; Goles, Eric] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Av Diagonal Torres 2640, Santiago, Chile, Email: gonzalo.ruz@uai.cl;  
  Corporate Author Thesis  
  Publisher Inderscience Enterprises Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1748-5673 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000451832900003 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 933  
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