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Spatiotemporal modeling of microbial metabolism

Overview of attention for article published in BMC Systems Biology, March 2016
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 tweeters

Citations

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51 Dimensions

Readers on

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144 Mendeley
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Title
Spatiotemporal modeling of microbial metabolism
Published in
BMC Systems Biology, March 2016
DOI 10.1186/s12918-016-0259-2
Pubmed ID
Authors

Jin Chen, Jose A. Gomez, Kai Höffner, Poonam Phalak, Paul I. Barton, Michael A. Henson

Abstract

Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention. We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution. Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 <1%
United States 1 <1%
Singapore 1 <1%
Unknown 141 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 26%
Researcher 31 22%
Student > Master 22 15%
Student > Bachelor 14 10%
Other 7 5%
Other 17 12%
Unknown 15 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 23%
Biochemistry, Genetics and Molecular Biology 30 21%
Chemical Engineering 27 19%
Engineering 15 10%
Computer Science 5 3%
Other 15 10%
Unknown 19 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 March 2016.
All research outputs
#3,336,082
of 7,356,005 outputs
Outputs from BMC Systems Biology
#332
of 793 outputs
Outputs of similar age
#127,462
of 280,864 outputs
Outputs of similar age from BMC Systems Biology
#9
of 24 outputs
Altmetric has tracked 7,356,005 research outputs across all sources so far. This one has received more attention than most of these and is in the 52nd percentile.
So far Altmetric has tracked 793 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,864 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.