P_UNSAT approach of attractor calculation for Boolean gene regulatory networks

被引：5
|
作者：
He, Qinbin [1 ]
Xia, Zhile [1 ]
Lin, Bin [1 ]
机构：
[1] Taizhou Univ, Dept Math, Linhai 317000, Zhejiang, Peoples R China
来源：
JOURNAL OF THEORETICAL BIOLOGY | 2018年 / 447卷
关键词：
Boolean networks; Genetic regulatory networks; Attractor; CANCER; REDUCTION; STABILITY; DYNAMICS; PATHWAY; MODELS;
D O I：
10.1016/j.jtbi.2018.03.037
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which combined the predecessor approach and the logic unsatisfiability approach to accelerate at tractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks even the networks with a relatively large average degree. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：171 / 177
页数：7
相关论文
共 50 条
  • [41] Relations between gene regulatory networks and cell dynamics in Boolean models
    Didier, Gilles
    Remy, Elisabeth
    DISCRETE APPLIED MATHEMATICS, 2012, 160 (15) : 2147 - 2157
  • [42] Using Boolean networks to model post-transcriptional regulation in gene regulatory networks
    Politano, Gianfranco
    Savino, Alessandro
    Benso, Alfredo
    Di Carlo, Stefano
    Rehman, Hafeez Ur
    Vasciaveo, Alessandro
    JOURNAL OF COMPUTATIONAL SCIENCE, 2014, 5 (03) : 332 - 344
  • [43] Probabilistic Control of Boolean Networks with Multiple Dynamics: Towards Control of Gene Regulatory Networks
    Kobayashi, Koichi
    Hiraishi, Kunihiko
    2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, : 4371 - 4376
  • [44] A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks
    Kerkhofs, Johan
    Geris, Liesbet
    PLOS ONE, 2015, 10 (06):
  • [45] Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
    Shmulevich, I
    Dougherty, ER
    Kim, S
    Zhang, W
    BIOINFORMATICS, 2002, 18 (02) : 261 - 274
  • [46] Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks
    Villegas, Pablo
    Ruiz-Franco, Jose
    Hidalgo, Jorge
    Munoz, Miguel A.
    SCIENTIFIC REPORTS, 2016, 6
  • [47] Majority Rules with Random Tie-Breaking in Boolean Gene Regulatory Networks
    Chaouiya, Claudine
    Ourrad, Ouerdia
    Lima, Ricardo
    PLOS ONE, 2013, 8 (07):
  • [48] Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks
    Pablo Villegas
    José Ruiz-Franco
    Jorge Hidalgo
    Miguel A. Muñoz
    Scientific Reports, 6
  • [49] Augusta: From RNA-Seq to gene regulatory networks and Boolean models
    Musilova, Jana
    Vafek, Zdenek
    Puniya, Bhanwar Lal
    Zimmer, Ralf
    Helikar, Tomas
    Sedlar, Karel
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2024, 23 : 783 - 790
  • [50] Global Attractor of Reaction–Diffusion Gene Regulatory Networks with S-Type Delay
    Xiao Liang
    Xianglai Zhuo
    Ruili Wang
    Neural Processing Letters, 2020, 51 : 1557 - 1577
12345