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Characterization of Integrase and Excisionase Activity in a Cell-Free Protein Expression System Using a Modeling and

Ayush Pandey1, Makena L Rodriguez2, William Poole3

  • 1Control and Dynamical Systems, California Institute of Technology, Pasadena, California91125, United States.

ACS Synthetic Biology
|January 30, 2023
PubMed
Summary

We developed an automated pipeline for modeling and designing biological systems. This approach simplifies complex biological circuits, enabling precise control over protein expression via DNA recombination in cell-free systems.

Keywords:
Bayesian inferenceDNA recombinationcell-freemodel reductionmodelingsynthetic biology

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Area of Science:

  • Synthetic Biology
  • Computational Biology
  • Biochemical Engineering

Background:

  • Designing complex biological circuits requires robust modeling and parameterization tools.
  • Existing methods struggle with high-dimensional models common in synthetic biology.
  • Accurate characterization of DNA recombination enzymes is crucial for predictable circuit behavior.

Purpose of the Study:

  • To present a full-stack pipeline for biological system design, from specification to parameter identification.
  • To demonstrate the pipeline's utility by characterizing integrase and excisionase activity in cell-free systems.
  • To enable the design of complex biological circuits with precise control over protein expression.

Main Methods:

  • Utilized Python tools: BioCRNpyler for detailed chemical reaction network modeling, AutoReduce for model reduction, and Bioscrape for sensitivity analysis and Bayesian inference.
  • Generated hierarchical reduced models from high-dimensional initial models.
  • Performed sensitivity analysis-guided Bayesian inference to identify model parameters.

Main Results:

  • Successfully characterized integrase and excisionase activity in a cell-free protein expression system.
  • Developed a hierarchy of reduced models, culminating in minimal ODE models.
  • Quantified enzyme activity, enabling designs dependent on accurate protein expression control.

Conclusions:

  • The presented automated pipeline offers a novel approach for the design, modeling, reduction, and parametrization of complex biological circuits.
  • This methodology facilitates the engineering of sophisticated synthetic biological systems.
  • The pipeline streamlines the process from high-level design to detailed parameter identification.