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The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002865
Pubmed ID
Authors

Mario Pujato, Thomas MacCarthy, Andras Fiser, Aviv Bergman

Abstract

Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a) at the sequence level in individual binding sites (modulating binding specificity), and (b) at the network level (creating and destroying binding sites). We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters), a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters), robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 5%
France 2 2%
Portugal 2 2%
United Kingdom 2 2%
Brazil 2 2%
Chile 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Germany 1 <1%
Other 6 5%
Unknown 108 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 32%
Student > Ph. D. Student 34 26%
Professor 9 7%
Student > Postgraduate 9 7%
Professor > Associate Professor 9 7%
Other 22 17%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 56%
Biochemistry, Genetics and Molecular Biology 22 17%
Engineering 7 5%
Computer Science 6 5%
Physics and Astronomy 4 3%
Other 9 7%
Unknown 11 8%