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Author Ruz, G.A.; Goles, E.; Montalva, M.; Fogel, G.B. pdf  doi
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  Title Dynamical and topological robustness of the mammalian cell cycle network: A reverse engineering approach Type
  Year 2014 Publication Biosystems Abbreviated Journal Biosystems  
  Volume 115 Issue Pages 23-32  
  Keywords Gene regulatory networks; Boolean networks; Threshold networks; Update robustness; Topology robustness; Bees algorithm  
  Abstract A common gene regulatory network model is the threshold Boolean network, used for example to model the Arabidopsis thaliana floral morphogenesis network or the fission yeast cell cycle network. In this paper, we analyze a logical model of the mammalian cell cycle network and its threshold Boolean network equivalent. Firstly, the robustness of the network was explored with respect to update perturbations, in particular, what happened to the attractors for all the deterministic updating schemes. Results on the number of different limit cycles, limit cycle lengths, basin of attraction size, for all the deterministic updating schemes were obtained through mathematical and computational tools. Secondly, we analyzed the topology robustness of the network, by reconstructing synthetic networks that contained exactly the same attractors as the original model by means of a swarm intelligence approach. Our results indicate that networks may not be very robust given the great variety of limit cycles that a network can obtain depending on the updating scheme. In addition, we identified an omnipresent network with interactions that match with the original model as well as the discovery of new interactions. The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks. (C) 2013 Elsevier Ireland Ltd. All rights reserved.  
  Address [Ruz, Gonzalo A.; Goles, Eric; Montalva, Marco] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: gonzalo.ruz@uai.cl  
  Corporate Author Thesis  
  Publisher Elsevier Sci Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0303-2647 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000330500100004 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 350  
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Author Ruz, G.A.; Zuniga, A.; Goles, E. doi  openurl
  Title A Boolean network model of bacterial quorum-sensing systems Type
  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  
  Call Number UAI @ eduardo.moreno @ Serial 933  
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Author Timmermann, T.; Gonzalez, B.; Ruz, G.A. doi  openurl
  Title Reconstruction of a gene regulatory network of the induced systemic resistance defense response in Arabidopsis using boolean networks Type
  Year 2020 Publication Bmc Bioinformatics Abbreviated Journal BMC Bioinformatics  
  Volume 21 Issue 1 Pages 16 pp  
  Keywords Boolean networks; Differential evolution; Gene regulatory networks; Induced systemic resistance; Paraburkholderia phytofirmans; Pseudomonas syringae  
  Abstract Background An important process for plant survival is the immune system. The induced systemic resistance (ISR) triggered by beneficial microbes is an important cost-effective defense mechanism by which plants are primed to an eventual pathogen attack. Defense mechanisms such as ISR depend on an accurate and context-specific regulation of gene expression. Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRNs). Here, we explore the regulatory mechanism of the ISR defense response triggered by the beneficial bacterium Paraburkholderia phytofirmans PsJN in Arabidopsis thaliana plants infected with Pseudomonas syringae DC3000. To achieve this, a GRN underlying the ISR response was inferred using gene expression time-series data of certain defense-related genes, differential evolution, and threshold Boolean networks. Results One thousand threshold Boolean networks were inferred that met the restriction of the desired dynamics. From these networks, a consensus network was obtained that helped to find plausible interactions between the genes. A representative network was selected from the consensus network and biological restrictions were applied to it. The dynamics of the selected network showed that the largest attractor, a limit cycle of length 3, represents the final stage of the defense response (12, 18, and 24 h). Also, the structural robustness of the GRN was studied through the networks' attractors. Conclusions A computational intelligence approach was designed to reconstruct a GRN underlying the ISR defense response in plants using gene expression time-series data of A. thaliana colonized by P. phytofirmans PsJN and subsequently infected with P. syringae DC3000. Using differential evolution, 1000 GRNs from time-series data were successfully inferred. Through the study of the network dynamics of the selected GRN, it can be concluded that it is structurally robust since three mutations were necessary to completely disarm the Boolean trajectory that represents the biological data. The proposed method to reconstruct GRNs is general and can be used to infer other biologically relevant networks to formulate new biological hypotheses.  
  Address [Timmermann, Tania; Gonzalez, Bernardo; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Lab Bioingn, Santiago, Chile, Email: gonzalo.ruz@uai.cl  
  Corporate Author Thesis  
  Publisher Bmc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1471-2105 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000529043500003 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1143  
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