How Does <i>Escherichia coli</i> Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in <i>Escherichia coli</i>. Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of <i>E. coli</i> proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

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Publication Details

Journal
ACS Synthetic Biology
Published
2024-08-09
DOI
https://doi.org/10.1021/acssynbio.3c00537
Citations
7
Primary Topic
Bacterial Genetics and Biotechnology
Type
article
Field-Weighted Citation Impact
3.85

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article

How Does Escherichia coli Allocate Proteome?

Yi-Hui Lai, Christopher V. Rao, Chen Liao, Ting Lu et al.
7 citations
ACS Synthetic Biology
Bacterial Genetics and Biotechnology
3.85
article

How Does Escherichia coli Allocate Proteome?

Yi-Hui Lai, Christopher V. Rao, Chen Liao, Ting Lu, Priyanka Priyanka
article en
7 citations

Abstract

Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in <i>Escherichia coli</i>. Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of <i>E. coli</i> proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

ACS Synthetic BiologyVol. 13(9)
Memorial Sloan Kettering Cancer Center (US), University of Illinois Urbana-Champaign (US)
U.S. Department of Energy, National Institutes of Health, Office of Science, National Institute of General Medical Sciences, Biological and Environmental Research
Zero hunger
Openalex Percentile: Top 6%
Bacterial Genetics and Biotechnology
3.85
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