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Related Experiment Video

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Precise, High-throughput Analysis of Bacterial Growth
09:00

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Published on: September 19, 2017

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.

Nathan E Lewis1, Kim K Hixson, Tom M Conrad

  • 1Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA.

Molecular Systems Biology
|July 29, 2010
PubMed
Summary
This summary is machine-generated.

Flux balance analysis (FBA) accurately predicts Escherichia coli growth. Gene and protein expression in evolved strains align with FBA predictions, validating metabolic models.

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Published on: November 12, 2012

Area of Science:

  • Microbial physiology
  • Systems biology
  • Metabolic engineering

Background:

  • Flux balance analysis (FBA) uses genome-scale metabolic models (GEMs) to predict microbial growth.
  • Consistency between FBA predictions and gene/protein expression in adaptive evolution is not well understood.

Purpose of the Study:

  • To investigate the correlation between FBA predictions and transcriptomic/proteomic data in evolved Escherichia coli.
  • To understand how gene and protein expression changes contribute to adaptive evolution and optimized growth.

Main Methods:

  • Utilized flux balance analysis (FBA) with genome-scale metabolic models (GEMs) for Escherichia coli.
  • Analyzed transcriptomic and proteomic data from wild-type and adaptively evolved strains.
  • Compared FBA-predicted active reactions with gene and protein expression data.

Main Results:

  • >98% of active reactions predicted by FBA were supported by transcriptomic and proteomic data.
  • Evolved strains upregulated genes within FBA optimal growth predictions and downregulated those outside.
  • Identified regulatory changes, including downregulation of regulons and suppression of stringent response, contributing to optimized growth.

Conclusions:

  • Differential gene and protein expression in evolved E. coli supports observed growth phenotype changes.
  • Experimental data aligns with GEM-computed optimal growth states, validating FBA predictions in adaptive evolution.
  • GEMs and FBA are powerful tools for understanding microbial adaptation and optimizing metabolic functions.