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Multiobjective H2/H∞ synthetic gene network design based on promoter libraries.

Chih-Hung Wu1, Weihei Zhang, Bor-Sen Chen

  • 1Lab of Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

Mathematical Biosciences
|July 27, 2011
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Summary

This study presents a systematic method for engineering synthetic gene networks with desired behaviors using promoter libraries. The approach utilizes multiobjective H(2)/H(infinity) reference tracking and fuzzy approximation to simplify complex optimization problems, reducing trial-and-error experiments.

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Area of Science:

  • Synthetic Biology
  • Systems Biology
  • Control Theory

Background:

  • Existing promoter libraries lack systematic methods for selecting promoters to engineer synthetic gene networks with specific behaviors.
  • Synthetic gene networks are susceptible to intrinsic parameter fluctuations and environmental disturbances in vivo.
  • Developing efficient methods for designing robust synthetic gene networks is crucial for advancing synthetic biology.

Purpose of the Study:

  • To develop a systematic method for efficiently employing promoter libraries in the engineering of synthetic gene networks with desired behaviors.
  • To design a synthetic gene network that can robustly and optimally track desired behaviors despite in vivo fluctuations and disturbances.
  • To simplify a complex optimization problem for promoter selection using approximation methods.

Main Methods:

  • Modeling the synthetic gene network as a nonlinear stochastic system.
  • Introducing a multiobjective H(2)/H(infinity) reference tracking design for optimal performance and noise filtering.
  • Redefining promoter libraries based on promoter activities and employing fuzzy approximation to convert a Hamilton-Jacobi Inequality (HJI)-constrained problem into a linear matrix inequality (LMI)-constrained problem.
  • Utilizing the LMI toolbox in Matlab for promoter set selection.

Main Results:

  • A systematic method was developed to select adequate promoter sets from redefined libraries to achieve the multiobjective H(2)/H(infinity) reference tracking design.
  • The proposed method simplifies the design process by transforming a complex HJI-constrained optimization into a solvable LMI-constrained problem.
  • In silico design examples confirmed the effectiveness of the method in selecting promoters for robust and optimal synthetic gene network behavior.

Conclusions:

  • The developed systematic method significantly reduces trial-and-error experiments in selecting promoters for synthetic gene networks.
  • This approach accelerates the design and engineering of synthetic gene networks with predictable and desired behaviors.
  • The method holds promise for advancing synthetic biology by enabling more efficient and reliable gene network construction.