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Evolutionary Tradeoffs between Economy and Effectiveness in Biological Homeostasis Systems

Overview of attention for article published in PLoS Computational Biology, August 2013
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Title
Evolutionary Tradeoffs between Economy and Effectiveness in Biological Homeostasis Systems
Published in
PLoS Computational Biology, August 2013
DOI 10.1371/journal.pcbi.1003163
Pubmed ID
Authors

Pablo Szekely, Hila Sheftel, Avi Mayo, Uri Alon

Abstract

Biological regulatory systems face a fundamental tradeoff: they must be effective but at the same time also economical. For example, regulatory systems that are designed to repair damage must be effective in reducing damage, but economical in not making too many repair proteins because making excessive proteins carries a fitness cost to the cell, called protein burden. In order to see how biological systems compromise between the two tasks of effectiveness and economy, we applied an approach from economics and engineering called Pareto optimality. This approach allows calculating the best-compromise systems that optimally combine the two tasks. We used a simple and general model for regulation, known as integral feedback, and showed that best-compromise systems have particular combinations of biochemical parameters that control the response rate and basal level. We find that the optimal systems fall on a curve in parameter space. Due to this feature, even if one is able to measure only a small fraction of the system's parameters, one can infer the rest. We applied this approach to estimate parameters in three biological systems: response to heat shock and response to DNA damage in bacteria, and calcium homeostasis in mammals.

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Mendeley readers

The data shown below were compiled from readership statistics for 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 4%
Portugal 3 2%
Switzerland 1 <1%
Sweden 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 174 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 58 31%
Researcher 39 21%
Student > Master 17 9%
Student > Bachelor 14 7%
Professor > Associate Professor 12 6%
Other 25 13%
Unknown 25 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 71 37%
Biochemistry, Genetics and Molecular Biology 29 15%
Physics and Astronomy 13 7%
Computer Science 10 5%
Engineering 9 5%
Other 24 13%
Unknown 34 18%