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Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow.

Elizabeth Brunk1, Kevin W George2, Jorge Alonso-Gutierrez2

  • 1Joint Bioenergy Institute (JBEI), 5885 Hollis Street, Emeryville, CA 94608, USA; Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

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This study introduces a novel workflow integrating multi-omics and metabolic modeling to understand how introducing new pathways affects microbial hosts. This approach enhances biofuel production by revealing key genetic targets for metabolic engineering.

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Area of Science:

  • Metabolic Engineering
  • Synthetic Biology
  • Computational Systems Biology

Background:

  • Interactions between heterologous and native biochemical pathways pose challenges in metabolic engineering.
  • Microbial hosts require detailed understanding for successful pathway integration.

Purpose of the Study:

  • To present a workflow for studying heterologous pathway effects in microbial hosts.
  • To provide molecular insights into host microenvironment changes during pathway engineering.
  • To demonstrate exploiting strain variation for increased product yield.

Main Methods:

  • Integration of metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism.
  • Application of computational systems biology, metabolic engineering, and synthetic biology approaches.
  • Analysis of engineered strains producing biofuels (isopentenol, limonene, bisabolene).

Main Results:

  • Identified roles of candidate genes, pathways, and reactions in experimental outcomes.
  • Facilitated construction of a mutant strain with improved biofuel productivity.
  • Provided molecular insights into host microenvironment modifications.

Conclusions:

  • The developed workflow offers a powerful strategy for metabolic engineering and synthetic biology.
  • Understanding host-pathway interactions is crucial for optimizing microbial production systems.
  • The workflow enables the exploitation of biological mechanisms to enhance strain performance.