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Quantifying complexity in metabolic engineering using the LASER database.

James D Winkler1, Andrea L Halweg-Edwards1, Ryan T Gill1

  • 1Department of Chemical and Biological Engineering, University of Colorado-Boulder, Jennie Smoly Caruthers Biotechnology Building, Research Park, Boulder, CO 80303, USA.

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|February 23, 2018
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Summary
This summary is machine-generated.

The LASER database now includes over 600 engineered strains, aiding metabolic engineering research. Complexity metrics were developed, showing no trade-off with product yield, enabling efficient strain design.

Keywords:
Design toolsMetabolic engineeringStandardizationSynthetic biology

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Area of Science:

  • Metabolic Engineering
  • Synthetic Biology
  • Bioinformatics

Background:

  • The LASER (Learning Assisted Strain EngineeRing) database was previously established to document metabolic engineering practices.
  • The LASER database has expanded by 50% to encompass over 600 engineered strains from 450 publications.
  • Information includes growth conditions, genetic modifications, and other details in a searchable format.

Purpose of the Study:

  • To define and quantify the complexity of metabolic engineering designs.
  • To evaluate complexity metrics based on construction difficulty and novelty.
  • To explore the relationship between design complexity and expected product yield.

Main Methods:

  • Utilizing the expanded LASER database to analyze engineered strains.
  • Developing and applying two distinct complexity metrics for metabolic engineering designs.
  • Correlating design complexity with predicted product yields.

Main Results:

  • No correlation was found between the complexity of a metabolic engineering design and its expected product yield.
  • Complexity minimization is achievable without compromising performance.
  • The developed metrics can help prioritize designs before implementation.

Conclusions:

  • Complexity metrics offer a valuable tool for filtering and prioritizing metabolic engineering designs.
  • Reducing design complexity can lead to significant savings in time, labor, and cost.
  • Future developments will leverage the continuously expanding LASER database for enhanced insights.