Literature DB >> 18596076

Coherent coupling of feedback loops: a design principle of cell signaling networks.

Yung-Keun Kwon1, Kwang-Hyun Cho.   

Abstract

MOTIVATION: It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together.
RESULTS: We have investigated the role of such coherently coupled feedback loops through extensive Boolean network simulations and found that a high proportion of coherent couplings can enhance the robustness of a network against its state perturbations. Moreover, we found that the robustness achieved by coherently coupled feedback loops can be kept evolutionarily stable. All these results imply that the coherent coupling of feedback loops might be a design principle of cell signaling networks devised to achieve the robustness.

Mesh:

Substances:

Year:  2008        PMID: 18596076     DOI: 10.1093/bioinformatics/btn337

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


  22 in total

1.  Identification of feedback loops embedded in cellular circuits by investigating non-causal impulse response components.

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Review 2.  Insights into the organization of biochemical regulatory networks using graph theory analyses.

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Review 4.  Non-coding RNA transcripts: sensors of neuronal stress, modulators of synaptic plasticity, and agents of change in the onset of Alzheimer's disease.

Authors:  Georges St Laurent; Mohammad Ali Faghihi; Claes Wahlestedt
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5.  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

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Authors:  Dhara N Amin; Deepika Ahuja; Paul Yaswen; Mark M Moasser
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7.  Monotonicity, frustration, and ordered response: an analysis of the energy landscape of perturbed large-scale biological networks.

Authors:  Giovanni Iacono; Claudio Altafini
Journal:  BMC Syst Biol       Date:  2010-06-10

8.  PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks.

Authors:  Hung-Cuong Trinh; Duc-Hau Le; Yung-Keun Kwon
Journal:  PLoS One       Date:  2014-07-24       Impact factor: 3.240

9.  Computing paths and cycles in biological interaction graphs.

Authors:  Steffen Klamt; Axel von Kamp
Journal:  BMC Bioinformatics       Date:  2009-06-15       Impact factor: 3.169

10.  Identification of a topological characteristic responsible for the biological robustness of regulatory networks.

Authors:  Yangle Wu; Xiaomeng Zhang; Jianglei Yu; Qi Ouyang
Journal:  PLoS Comput Biol       Date:  2009-07-24       Impact factor: 4.475

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