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Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

Overview of attention for article published in BMC Systems Biology, September 2014
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  • High Attention Score compared to outputs of the same age and source (80th percentile)

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6 X users

Citations

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84 Dimensions

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170 Mendeley
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Title
Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale
Published in
BMC Systems Biology, September 2014
DOI 10.1186/s12918-014-0110-6
Pubmed ID
Authors

Joanne K Liu, Edward J O’Brien, Joshua A Lerman, Karsten Zengler, Bernhard O Palsson, Adam M Feist

Abstract

BackgroundMembranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models.ResultsThe recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass.Conclusion iJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models.

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X Demographics

The data shown below were collected from the profiles of 6 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 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
United Kingdom 2 1%
Hungary 1 <1%
Germany 1 <1%
Sweden 1 <1%
Brazil 1 <1%
Latvia 1 <1%
Singapore 1 <1%
Canada 1 <1%
Other 2 1%
Unknown 156 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 25%
Researcher 29 17%
Student > Master 29 17%
Student > Bachelor 19 11%
Student > Doctoral Student 12 7%
Other 19 11%
Unknown 19 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 35%
Biochemistry, Genetics and Molecular Biology 26 15%
Engineering 24 14%
Chemical Engineering 14 8%
Computer Science 11 6%
Other 11 6%
Unknown 25 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 27 September 2015.
All research outputs
#7,251,058
of 24,133,587 outputs
Outputs from BMC Systems Biology
#257
of 1,134 outputs
Outputs of similar age
#72,427
of 254,146 outputs
Outputs of similar age from BMC Systems Biology
#7
of 30 outputs
Altmetric has tracked 24,133,587 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,134 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 77% 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 254,146 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 71% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.