title: Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.
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.
authors: Department of Physics, University of California at San Diego, La Jolla, CA, USA.
authors: Sheng
authors: S
authors: Hui
authors: Department 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.
authors: Josh M
authors: JM
authors: Silverman
authors: Department 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.
authors: Stephen S
authors: SS
authors: Chen
authors: Department of Physics, University of California at San Diego, La Jolla, CA, USA.
authors: David W
authors: DW
authors: Erickson
authors: Department of Physics, University of California at San Diego, La Jolla, CA, USA.
authors: Markus
authors: M
authors: Basan
authors: Department of Physics, University of California at San Diego, La Jolla, CA, USA.
authors: Jilong
authors: J
authors: Wang
authors: Department 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.
authors: Terence
authors: T
authors: Hwa
authors: Department 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.
authors: James R
authors: JR
authors: Williamson
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics* / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D015964:Gene Expression Regulation, Bacterial; D013058:Mass Spectrometry; D008958:Models, Molecular; D020543:Proteome / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
chemical_list: D001426:Bacterial Proteins; D020543:Proteome
keywords: growth physiology; metabolic network; microbiology; quantitative proteomics; systems biology