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Complex systems in metabolic engineering.

James D Winkler1, Keesha Erickson1, Alaksh Choudhury1

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Summary
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Metabolic engineering faces complexity challenges due to intricate biological networks. This review explores tools to manage complexity for designing high-performing microbial strains.

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Area of Science:

  • Metabolic engineering
  • Synthetic biology
  • Systems biology

Background:

  • Biological networks are complex, hindering the design of efficient engineered organisms.
  • Existing tools to manage complexity in other fields are not comprehensively applied in metabolic engineering.
  • Understanding and mitigating complexity is crucial for advancing strain design.

Purpose of the Study:

  • To review how complexity impacts metabolic engineering at different biological levels (protein, pathway, genome).
  • To examine existing tools used by metabolic engineers to address complexity.
  • To compare complexity management in metabolic engineering with traditional engineering fields.

Main Methods:

  • Literature review of complexity management strategies in engineering disciplines.
  • Analysis of complexity at protein, pathway, and genome levels in metabolic engineering.
  • Discussion of quantitative complexity metrics and their applications.

Main Results:

  • Complexity in metabolic engineering arises from extensive and often unknown biological interconnections.
  • Various tools can be adapted to manage complexity at different biological scales.
  • Quantitative metrics for complexity exist but require broader application in metabolic engineering.

Conclusions:

  • Explicitly considering design complexity can advance metabolic engineering practices.
  • Integrating complexity management tools will lead to more predictable and efficient strain designs.
  • Future metabolic engineering will likely involve more systematic approaches to complexity.