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Metabolic modeling of synthesis gas fermentation in bubble column reactors

Overview of attention for article published in Biotechnology for Biofuels and Bioproducts, June 2015
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  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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3 X users

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175 Mendeley
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Title
Metabolic modeling of synthesis gas fermentation in bubble column reactors
Published in
Biotechnology for Biofuels and Bioproducts, June 2015
DOI 10.1186/s13068-015-0272-5
Pubmed ID
Authors

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

Abstract

A promising route to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas) streams to synthesize desired products such as ethanol and 2,3-butanediol. While commercial development of syngas fermentation technology is underway, an unmet need is the development of integrated metabolic and transport models for industrially relevant syngas bubble column reactors. We developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. Our modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs) and solved using the MATLAB based code DFBAlab. Simulations were performed to analyze the effects of important process and cellular parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions. Our computational study demonstrated that mathematical modeling provides a complementary tool to experimentation for understanding, predicting, and optimizing syngas fermentation reactors. These model predictions could guide future cellular and process engineering efforts aimed at alleviating bottlenecks to biochemical production in syngas bubble column reactors.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 <1%
Germany 1 <1%
Italy 1 <1%
Unknown 172 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 24%
Student > Master 30 17%
Researcher 28 16%
Student > Bachelor 14 8%
Other 8 5%
Other 21 12%
Unknown 32 18%
Readers by discipline Count As %
Chemical Engineering 36 21%
Biochemistry, Genetics and Molecular Biology 28 16%
Agricultural and Biological Sciences 23 13%
Engineering 21 12%
Environmental Science 10 6%
Other 13 7%
Unknown 44 25%
Attention Score in Context

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 14 July 2015.
All research outputs
#16,048,009
of 25,374,917 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#881
of 1,578 outputs
Outputs of similar age
#146,798
of 278,563 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#10
of 25 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,578 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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 278,563 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.