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General Theory for Integrated Analysis of Growth, Gene, and Protein Expression in Biofilms

Overview of attention for article published in PLOS ONE, December 2013
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Title
General Theory for Integrated Analysis of Growth, Gene, and Protein Expression in Biofilms
Published in
PLOS ONE, December 2013
DOI 10.1371/journal.pone.0083626
Pubmed ID
Authors

Tianyu Zhang, Breana Pabst, Isaac Klapper, Philip S. Stewart

Abstract

A theory for analysis and prediction of spatial and temporal patterns of gene and protein expression within microbial biofilms is derived. The theory integrates phenomena of solute reaction and diffusion, microbial growth, mRNA or protein synthesis, biomass advection, and gene transcript or protein turnover. Case studies illustrate the capacity of the theory to simulate heterogeneous spatial patterns and predict microbial activities in biofilms that are qualitatively different from those of planktonic cells. Specific scenarios analyzed include an inducible GFP or fluorescent protein reporter, a denitrification gene repressed by oxygen, an acid stress response gene, and a quorum sensing circuit. It is shown that the patterns of activity revealed by inducible stable fluorescent proteins or reporter unstable proteins overestimate the region of activity. This is due to advective spreading and finite protein turnover rates. In the cases of a gene induced by either limitation for a metabolic substrate or accumulation of a metabolic product, maximal expression is predicted in an internal stratum of the biofilm. A quorum sensing system that includes an oxygen-responsive negative regulator exhibits behavior that is distinct from any stage of a batch planktonic culture. Though here the analyses have been limited to simultaneous interactions of up to two substrates and two genes, the framework applies to arbitrarily large networks of genes and metabolites. Extension of reaction-diffusion modeling in biofilms to the analysis of individual genes and gene networks is an important advance that dovetails with the growing toolkit of molecular and genetic experimental techniques.

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X Demographics

The data shown below were collected from the profiles of 5 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 2%
United States 1 2%
Germany 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 7 16%
Student > Doctoral Student 6 14%
Student > Master 6 14%
Professor > Associate Professor 4 9%
Other 4 9%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 42%
Medicine and Dentistry 4 9%
Biochemistry, Genetics and Molecular Biology 4 9%
Immunology and Microbiology 3 7%
Mathematics 2 5%
Other 6 14%
Unknown 6 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 March 2015.
All research outputs
#12,890,747
of 22,738,543 outputs
Outputs from PLOS ONE
#100,573
of 194,081 outputs
Outputs of similar age
#158,438
of 306,693 outputs
Outputs of similar age from PLOS ONE
#2,737
of 5,627 outputs
Altmetric has tracked 22,738,543 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,081 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 47th percentile – i.e., 47% 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 306,693 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,627 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 50% of its contemporaries.