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Tension and Robustness in Multitasking Cellular Networks

Overview of attention for article published in PLoS Computational Biology, April 2012
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Title
Tension and Robustness in Multitasking Cellular Networks
Published in
PLoS Computational Biology, April 2012
DOI 10.1371/journal.pcbi.1002491
Pubmed ID
Authors

Jeffrey V. Wong, Bochong Li, Lingchong You

Abstract

Cellular networks multitask by exhibiting distinct, context-dependent dynamics. However, network states (parameters) that generate a particular dynamic are often sub-optimal for others, defining a source of "tension" between them. Though multitasking is pervasive, it is not clear where tension arises, what consequences it has, and how it is resolved. We developed a generic computational framework to examine the source and consequences of tension between pairs of dynamics exhibited by the well-studied RB-E2F switch regulating cell cycle entry. We found that tension arose from task-dependent shifts in parameters associated with network modules. Although parameter sets common to distinct dynamics did exist, tension reduced both their accessibility and resilience to perturbation, indicating a trade-off between "one-size-fits-all" solutions and robustness. With high tension, robustness can be preserved by dynamic shifting of modules, enabling the network to toggle between tasks, and by increasing network complexity, in this case by gene duplication. We propose that tension is a general constraint on the architecture and operation of multitasking biological networks. To this end, our work provides a framework to quantify the extent of tension between any network dynamics and how it affects network robustness. Such analysis would suggest new ways to interfere with network elements to elucidate the design principles of cellular networks.

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Geographical breakdown

Country Count As %
United Kingdom 2 4%
United States 2 4%
Unknown 47 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 35%
Researcher 13 25%
Student > Bachelor 5 10%
Professor 2 4%
Student > Postgraduate 2 4%
Other 6 12%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 31%
Engineering 7 14%
Computer Science 4 8%
Biochemistry, Genetics and Molecular Biology 4 8%
Physics and Astronomy 4 8%
Other 10 20%
Unknown 6 12%