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Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering.

Thomas Eng1, Deepanwita Banerjee1, Javier Menasalvas1

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Summary

We engineered Pseudomonas putida for growth-coupled production of indigoidine, achieving high yields from para-coumarate. This systems biology approach is adaptable for various biomanufacturing applications.

Keywords:
ALECP: MicrobiologyCRISPR/recombineeringPseudomonas putida KT2440bioproductiongenome-scale metabolic modelsgrowth couplingindigoidineligninproteomics analysisstrain engineering

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Area of Science:

  • Synthetic biology
  • Metabolic engineering
  • Biotechnology

Background:

  • Maximizing heterologous biomolecule production requires understanding cellular metabolism and regulation.
  • Growth-coupling strategies enhance product titers, yields, and production rates.
  • Applying these strategies to non-canonical carbon sources is hindered by metabolic model limitations.

Purpose of the Study:

  • To develop a growth-coupled system for indigoidine production in Pseudomonas putida KT2440 using para-coumarate.
  • To overcome challenges associated with non-canonical carbon streams and metabolic model gaps.

Main Methods:

  • Iterative design-build-test-learn cycles over four iterations.
  • Exploration of 4,114 potential growth-coupling solutions.
  • Laboratory evolution and ensemble data-driven methods for strain refinement.
  • Functional genomics and experimental validation.

Main Results:

  • Engineered Pseudomonas putida KT2440 for growth-coupled indigoidine production from para-coumarate.
  • Achieved 7.3 g/L indigoidine production with 77% maximum theoretical yield.
  • Demonstrated the effectiveness of iterative design and data-driven refinement.

Conclusions:

  • The developed growth-coupling strategy is highly effective and generalizable across different hosts, carbon streams, and products.
  • This systems-level approach offers a robust framework for optimizing biomanufacturing processes.
  • The study highlights the power of integrating computational design with experimental validation.