Literature DB >> 17379691

The impact of function perturbations in Boolean networks.

Yufei Xiao1, Edward R Dougherty.   

Abstract

MOTIVATION: A network is said to be robust relative to a certain network characteristic if a small change in network structure does not significantly affect the characteristic. From the perspective of network stability, robustness is desirable; however, from the perspective of intervention to exert influence on network behavior, it is undesirable. For Boolean networks, there are two fundamental types of robustness. One type pertains to perturbing the state of the network and the other to perturbing the rule-based structure.
RESULTS: This article explores the impact of function perturbations in Boolean networks from two aspects: (1) analysis: predict the impact on network state transitions and attractors via analytical approaches or identify a perturbation by observing its consequences; (2) synthesis: preserve or modify the network characteristics, especially attractors, by introducing a judicious change to the functions. The results are applied to achieve intervention that structurally alters the network to achieve a more favorable steady-state distribution and to identify the function perturbation that has led to altered observed behavior. The intervention procedure is applied to a WNT5A network to reduce the risk of metastasis in melanoma, and the identification procedure is applied to a Drosophila melanogaster segmentation polarity gene network to identify regulatory function perturbation.

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Year:  2007        PMID: 17379691     DOI: 10.1093/bioinformatics/btm093

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  22 in total

1.  Inverse perturbation for optimal intervention in gene regulatory networks.

Authors:  Nidhal Bouaynaya; Roman Shterenberg; Dan Schonfeld
Journal:  Bioinformatics       Date:  2010-11-08       Impact factor: 6.937

2.  Optimal Perturbation Control of General Topology Molecular Networks.

Authors:  Nidhal Bouaynaya; Roman Shterenberg; Dan Schonfeld
Journal:  IEEE Trans Signal Process       Date:  2013-04       Impact factor: 4.931

3.  RMut: R package for a Boolean sensitivity analysis against various types of mutations.

Authors:  Hung-Cuong Trinh; Yung-Keun Kwon
Journal:  PLoS One       Date:  2019-03-19       Impact factor: 3.240

4.  Analysis on gene modular network reveals morphogen-directed development robustness in Drosophila.

Authors:  Shuo Zhang; Juan Zhao; Xiangdong Lv; Jialin Fan; Yi Lu; Tao Zeng; Hailong Wu; Luonan Chen; Yun Zhao
Journal:  Cell Discov       Date:  2020-06-30       Impact factor: 10.849

Review 5.  Boolean modelling as a logic-based dynamic approach in systems medicine.

Authors:  Ahmed Abdelmonem Hemedan; Anna Niarakis; Reinhard Schneider; Marek Ostaszewski
Journal:  Comput Struct Biotechnol J       Date:  2022-06-17       Impact factor: 6.155

6.  A tutorial on analysis and simulation of boolean gene regulatory network models.

Authors:  Yufei Xiao
Journal:  Curr Genomics       Date:  2009-11       Impact factor: 2.236

7.  On the long-run sensitivity of probabilistic Boolean networks.

Authors:  Xiaoning Qian; Edward R Dougherty
Journal:  J Theor Biol       Date:  2008-12-31       Impact factor: 2.691

8.  Algorithm to identify the optimal perturbation based on the net basin-of-state of perturbed states in Boolean network.

Authors:  Liangzhong Shen; Xiangzhen Zan; Wenbin Liu
Journal:  IET Syst Biol       Date:  2018-08       Impact factor: 1.615

9.  Modeling stochasticity and robustness in gene regulatory networks.

Authors:  Abhishek Garg; Kartik Mohanram; Alessandro Di Cara; Giovanni De Micheli; Ioannis Xenarios
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

10.  Efficient experimental design for uncertainty reduction in gene regulatory networks.

Authors:  Roozbeh Dehghannasiri; Byung-Jun Yoon; Edward R Dougherty
Journal:  BMC Bioinformatics       Date:  2015-09-25       Impact factor: 3.169

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