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Related Experiment Video

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Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms
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Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering.

Babita K Verma1, Ahmad A Mannan2, Fuzhong Zhang3

  • 1School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K.

ACS Synthetic Biology
|December 30, 2021
PubMed
Summary
This summary is machine-generated.

Synthetic biology enables dynamic control circuits for metabolic engineering by using metabolite biosensors to self-regulate gene expression. This research optimizes biosensor design for improved production flux and reduced host burden, advancing automated strain engineering.

Keywords:
biosensor optimizationdynamic pathway controlmetabolic engineeringmetabolite biosensormodel-based designpathway optimization

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Area of Science:

  • Synthetic biology
  • Metabolic engineering
  • Systems biology

Background:

  • Dynamic control circuits in metabolic engineering offer self-regulation in response to bioreactor perturbations, overcoming limitations of traditional methods.
  • Metabolite biosensors are key components for sensing pathway signals and actuating enzyme expression within these circuits.
  • Designing effective metabolite biosensors and understanding their dose-response curve impact on pathway performance remain significant challenges.

Purpose of the Study:

  • To employ multiobjective optimization for quantifying performance trade-offs in metabolite biosensor design.
  • To reveal strategies for tuning biosensor dose-response curves for optimal production flux and host expression burden.
  • To analyze control architectures for performance, robustness, and identify advantages and caveats.

Main Methods:

  • Multiobjective optimization was utilized to analyze trade-offs in metabolite biosensor design.
  • Dose-response curves were tuned to balance production flux against host expression burden.
  • Literature-based control architectures were evaluated for performance and robustness.

Main Results:

  • The study identified strategies for optimizing biosensor dose-response curves, balancing production and host burden.
  • Analysis revealed performance characteristics and robustness limitations of existing control architectures.
  • A control circuit for glucaric acid production in Escherichia coli demonstrated a 2.5-fold increase in titer compared to static designs.

Conclusions:

  • The findings provide a framework for the automated design of dynamic control circuits in metabolic engineering.
  • Optimized biosensor design is crucial for enhancing pathway performance and reducing host metabolic load.
  • This work has broad implications for industrial biotechnology, including food, energy, and pharmaceutical production.