Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Issue 2 (12th February 2015) - Hui, Sheng
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. Synopsis: Quantitative relative and absolute protein abundance data allow the use of coarse‐graining analysis to reveal strategies of resource allocation by E. coli . A predictive, mathematical model of the proteome is constructed requiring only a few parameters. Coarse‐graining procedure makes proteomics data amenable to quantitative analysis. Five functionally distinct proteome sectors each exhibit linear relations with the growth rate. A simple flux model captures proteome‐wide responses accurately with few parameters. Proteome economy is shown to be a principle governing global gene regulation. Abstract : Quantitative relative and absolute protein abundance data allow the use of coarse‐graining analysis to reveal strategies of resource allocation by E. coli . A predictive, mathematical model of the proteome is constructed requiring only a few parameters.