Neutral graph of regulatory Boolean networks using evolutionary computation

被引:0
|
作者
Ruz, Gonzalo A. [1 ]
Goles, Eric [1 ]
机构
[1] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile
关键词
ROBUSTNESS; MODEL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An evolution strategy is proposed to construct neutral graphs. The proposed method is applied to the construction of the neutral graph of Boolean regulatory networks that share the same state sequences of the cell cycle of the fission yeast. The regulatory networks in the neutral graph are analyzed, identifying characteristics of the networks which belong to the connected component of the fission yeast cell cycle network and the regulatory networks that are not in the connected component. Results show not only topological differences, but also differences in the state space between networks in the connected component and the rest of the networks in the neutral graph. It was found that regulatory networks in the fission yeast cell cycle network connected component can be mutated (change in their interaction matrices) no more than three times, if more mutations occur, then the networks leave the connected component. Comparisons with a standard genetic algorithm shows the effectiveness of the proposed evolution strategy.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Spatial Information and Boolean Genetic Regulatory Networks
    Manceny, Matthieu
    Aiguier, Marc
    Le Gall, Pascale
    Herisson, Joan
    Junier, Ivan
    Kepes, Francois
    BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, PROCEEDINGS, 2009, 5462 : 270 - +
  • [32] Gene regulatory networks modelling using a dynamic evolutionary hybrid
    Maraziotis, Ioannis A.
    Dragomir, Andrei
    Thanos, Dimitris
    BMC BIOINFORMATICS, 2010, 11
  • [33] Gene regulatory networks modelling using a dynamic evolutionary hybrid
    Ioannis A Maraziotis
    Andrei Dragomir
    Dimitris Thanos
    BMC Bioinformatics, 11
  • [34] An Evolutionary System using Development and Artificial Genetic Regulatory Networks
    Zhan, Song
    Miller, Julian F.
    Tyrrell, Andy M.
    2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 815 - 822
  • [35] Attractor Stabilizability of Boolean Networks With Application to Biomolecular Regulatory Networks
    Rafimanzelat, Mohammad Reza
    Bahrami, Fariba
    IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2019, 6 (01): : 72 - 81
  • [36] Modeling pathways of differentiation in genetic regulatory networks with Boolean networks
    Dealy, S
    Kauffman, S
    Socolar, J
    COMPLEXITY, 2005, 11 (01) : 52 - 60
  • [37] Fuzzy Logical on Boolean Networks as Model of Gene Regulatory Networks
    Xu, Honglin
    Wang, Shitong
    FIRST IITA INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2009, : 501 - 505
  • [38] Investigating the use of Boolean networks for the control of gene regulatory networks
    Taou, Nadia S.
    Corne, David W.
    Lones, Michael A.
    JOURNAL OF COMPUTATIONAL SCIENCE, 2018, 26 : 147 - 156
  • [39] Rapid computation and interpretation of Boolean attractors in biological networks
    Vasaikar, Suhas V.
    Jayaram, Bhyravabhotla
    Gomes, James
    Jayaram, Bhyravabhotla
    JOURNAL OF COMPLEX NETWORKS, 2015, 3 (01) : 147 - 157
  • [40] Optimal Computation of Symmetric Boolean Functions in Collocated Networks
    Kowshik, Hemant
    Kumar, P. R.
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2013, 31 (04) : 639 - 654