Evidence map›Paper›PMID 25678603›Full record

ArticleMolecular systems biology2015

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Sheng Hui, Josh M Silverman, Stephen S Chen, David W Erickson, Markus Basan, Jilong Wang, Terence Hwa, James R Williamson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers.

0numbers the graph read from it
0cells of the map it votes in
196citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

196 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Sodium ion stress accelerates cell lysis inApplied and environmental microbiology · 2026
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  15. Article
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  18. Review
  19. Bacterial iron acquisition byProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  20. Review

136 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sheng HuiDepartment of Physics, University of California at San Diego, La Jolla, CA, USA.
Josh M SilvermanDepartment of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA.
Stephen S ChenDepartment of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA.
David W EricksonDepartment of Physics, University of California at San Diego, La Jolla, CA, USA.
Markus BasanDepartment of Physics, University of California at San Diego, La Jolla, CA, USA.
Jilong WangDepartment of Physics, University of California at San Diego, La Jolla, CA, USA.
Terence HwaDepartment of Physics, University of California at San Diego, La Jolla, CA, USA Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA hwa@ucsd.edu jrwill@scripps.edu.
James R WilliamsonDepartment of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA hwa@ucsd.edu jrwill@scripps.edu.

Funding

Quantitative Studies of Bacterial Growth PhysiologyR01GM095903 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HWA, TERENCE · 2011 to 2023
$3.8M
Quantitative Studies of Metabolic Switches in enteric bacteriaR01GM109069 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HWA, TERENCE · 2014 to 2022
$3.0M
NIGMS NIH HHS R01 GM095903NIGMS NIH HHS R01-GM095903NIGMS NIH HHS R01 GM109069NIGMS NIH HHS R37-GM053357
6 · The paper itself

Abstract

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

Indexed as

Gene Expression Regulation, BacterialBacterial ProteinsEscherichia coliMass SpectrometryModels, MolecularProteomeProteomicsBacterial ProteinsProteomegrowth physiologymetabolic networkmicrobiologyquantitative proteomicssystems biology

Identifiers

PMID25678603
PMCPMC4358657

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.