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A Network Characteristic That Correlates Environmental and Genetic Robustness

Overview of attention for article published in PLoS Computational Biology, February 2014
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Title
A Network Characteristic That Correlates Environmental and Genetic Robustness
Published in
PLoS Computational Biology, February 2014
DOI 10.1371/journal.pcbi.1003474
Pubmed ID
Authors

Zeina Shreif, Vipul Periwal

Abstract

As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

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Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
United Kingdom 1 2%
Unknown 46 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 37%
Student > Ph. D. Student 10 20%
Professor > Associate Professor 6 12%
Professor 4 8%
Student > Postgraduate 2 4%
Other 5 10%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 35%
Biochemistry, Genetics and Molecular Biology 8 16%
Computer Science 3 6%
Physics and Astronomy 3 6%
Medicine and Dentistry 3 6%
Other 10 20%
Unknown 5 10%