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Related Experiment Video

Updated: Jul 12, 2026

A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles.

Paul Van Lent1, Sara Moreno Paz2, Joep Schmitz2

  • 1Delft Bioinformatics Lab, Intelligent Systems, Delft University of Technology, Delft, Zuid-Holland, Netherlands.

Frontiers in Bioengineering and Biotechnology
|July 11, 2026
PubMed
Summary
This summary is machine-generated.

Screening capacity is key for successful microbial strain optimization using Design-Build-Test-Learn (DBTL) cycles. Simulation frameworks can guide effective DBTL workflow design for metabolic engineering.

Keywords:
DBTL cycleskinetic modelingmachine learningmetabolic engineeringsystems biology

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Area of Science:

  • Metabolic Engineering
  • Synthetic Biology
  • Computational Biology

Background:

  • Design-Build-Test-Learn (DBTL) cycles are crucial for metabolic engineering.
  • The impact of various experimental and algorithmic choices on DBTL performance is not fully understood.

Purpose of the Study:

  • To quantitatively assess how key process parameters influence microbial strain optimization outcomes.
  • To evaluate the effects of DNA library design, experimental budget, and machine learning configuration on DBTL cycles.

Main Methods:

  • In silico Design-Build-Test-Learn (DBTL) cycles were performed.
  • Metabolic kinetic models were utilized for quantitative assessment.
  • Four distinct metabolic pathway models were analyzed.

Main Results:

  • Screening capacity significantly drives optimization success; DNA sequencing capacity has minimal impact.
  • Selecting top-producing strains for sequencing is more effective than stratified sampling.
  • DNA library structure impacts performance: more editable positions improve outcomes, while more gene targets can hinder optimization.

Conclusions:

  • Simulation frameworks provide valuable insights for designing efficient DBTL workflows.
  • Actionable guidance is offered for optimizing metabolic engineering strategies before experimental implementation.