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Monitoring of nutrient limitation in growing E. coli: a mathematical model of a ppGpp-based biosensor

Overview of attention for article published in BMC Systems Biology, November 2017
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Title
Monitoring of nutrient limitation in growing E. coli: a mathematical model of a ppGpp-based biosensor
Published in
BMC Systems Biology, November 2017
DOI 10.1186/s12918-017-0490-5
Pubmed ID
Authors

Alexandra Pokhilko

Abstract

E. coli can be used as bacterial cell factories for production of biofuels and other useful compounds. The efficient production of the desired products requires careful monitoring of growth conditions and the optimization of metabolic fluxes. To avoid nutrient depletion and maximize product yields we suggest using a natural mechanism for sensing nutrient limitation, related to biosynthesis of an intracellular messenger - guanosine tetraphosphate (ppGpp). We propose a design for a biosensor, which monitors changes in the intracellular concentration of ppGpp by coupling it to a fluorescent output. We used mathematical modelling to analyse the intracellular dynamics of ppGpp, its fluorescent reporter, and cell growth in normal and fatty acid-producing E. coli lines. The model integrates existing mechanisms of ppGpp regulation and predicts the biosensor response to changes in nutrient state. In particular, the model predicts that excessive stimulation of fatty acid production depletes fatty acid intermediates, downregulates growth and increases the levels of ppGpp-related fluorescence. Our analysis demonstrates that the ppGpp sensor can be used for early detection of nutrient limitation during cell growth and for testing productivity of engineered lines.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 31%
Researcher 5 14%
Student > Bachelor 4 11%
Student > Master 4 11%
Professor 2 6%
Other 6 17%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 33%
Agricultural and Biological Sciences 11 31%
Engineering 2 6%
Mathematics 1 3%
Unspecified 1 3%
Other 4 11%
Unknown 5 14%