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Kinetic modeling of Shewanella baltica KB30 growth on different substrates through respirometry

Overview of attention for article published in Microbial Cell Factories, November 2017
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Title
Kinetic modeling of Shewanella baltica KB30 growth on different substrates through respirometry
Published in
Microbial Cell Factories, November 2017
DOI 10.1186/s12934-017-0805-7
Pubmed ID
Authors

Juan Carlos Leyva-Díaz, José Manuel Poyatos, Paolo Barghini, Susanna Gorrasi, Massimiliano Fenice

Abstract

Shewanella baltica KB30 was isolated from seawater collected in Kandalaksha Bay, White Sea (Russia). This strain is known for its ability to grow on a pool of different substrates, including carbohydrates, carboxylic and amino acids, and lipids. However, no data are available on its metabolic efficiency in relation to the use of different carbon sources typologies. This work represents the first attempt to characterize S. baltica by its heterotrophic kinetic performance. Growth and substrate consumption, during the biodegradation of sodium acetate, glucose, tween 80 and peptone, were analyzed through a respirometric method. To find the model best fitting the experimental data and to obtain the kinetic parameters, the equations of Monod, Moser, Contois and Tessier were applied. The kinetic behavior of S. baltica was fitted to Monod model for sodium acetate and tween 80, while it was adjusted to Contois model for glucose and peptone. In this regard, peptone was consumed faster than the other substrates, as indicated by the highest values of substrate degradation rate, which exceeded 60 mg O2 L(-1) h(-1). Proteolytic metabolism was favored than lipidic and glucidic metabolism, which could contribute much more to mineralization and recycling of proteins than lipids and carbohydrates.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Master 5 16%
Other 3 10%
Student > Ph. D. Student 2 6%
Student > Postgraduate 2 6%
Other 2 6%
Unknown 11 35%
Readers by discipline Count As %
Environmental Science 7 23%
Engineering 4 13%
Chemical Engineering 3 10%
Agricultural and Biological Sciences 3 10%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 1 3%
Unknown 11 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 November 2017.
All research outputs
#20,451,228
of 23,007,053 outputs
Outputs from Microbial Cell Factories
#1,375
of 1,612 outputs
Outputs of similar age
#286,718
of 329,030 outputs
Outputs of similar age from Microbial Cell Factories
#34
of 46 outputs
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