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Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

Overview of attention for article published in PLoS Computational Biology, October 2012
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133 Mendeley
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Title
Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002746
Pubmed ID
Authors

Elsa W. Birch, Nicholas A. Ruggero, Markus W. Covert

Abstract

Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 5%
French Polynesia 1 <1%
Germany 1 <1%
Austria 1 <1%
France 1 <1%
Belgium 1 <1%
Iran, Islamic Republic of 1 <1%
Unknown 120 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 28%
Researcher 30 23%
Student > Master 14 11%
Other 7 5%
Professor > Associate Professor 7 5%
Other 20 15%
Unknown 18 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 38%
Biochemistry, Genetics and Molecular Biology 20 15%
Engineering 10 8%
Environmental Science 7 5%
Computer Science 5 4%
Other 19 14%
Unknown 22 17%
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 02 May 2019.
All research outputs
#16,045,990
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#6,967
of 8,958 outputs
Outputs of similar age
#117,827
of 193,730 outputs
Outputs of similar age from PLoS Computational Biology
#69
of 109 outputs
Altmetric has tracked 25,371,288 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 8,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% 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 193,730 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.