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Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks
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Absolutely robust controllers for chemical reaction networks.

Jinsu Kim1, German Enciso1

  • 1Department of Mathematics, University of California Irvine, Irvine, CA 92614, USA.

Journal of the Royal Society, Interface
|May 13, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new controller for chemical reaction networks, enhancing control over target species in both deterministic and stochastic systems. The controller, utilizing absolute concentration robustness (ACR), effectively manages mean and variance, ensuring robust perfect adaptation.

Keywords:
Poission distributionabsolute concentration robustnesscontroldeficiency zeromultiscalingreaction networks

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Area of Science:

  • Systems Biology
  • Chemical Kinetics
  • Control Theory

Background:

  • Chemical reaction networks (CRNs) are fundamental to biological processes.
  • Controlling specific species concentrations in CRNs is crucial for understanding and engineering biological systems.
  • Existing methods for CRN control face challenges in both deterministic and stochastic regimes.

Purpose of the Study:

  • To design a novel controller for mass-action chemical reaction networks.
  • To achieve precise control over a target species' concentration, including its mean and variance.
  • To demonstrate the controller's effectiveness in both deterministic and stochastic settings.

Main Methods:

  • Designing a specific set of reactions to add to existing CRNs.
  • Employing the concept of absolute concentration robustness (ACR).
  • Utilizing the deficiency zero theorem and multiscaling model reduction methods.

Main Results:

  • The controller effectively manages the target species in both deterministic and stochastic CRNs.
  • In stochastic networks, the controller regulates both the mean and variance of the target species.
  • The target species exhibits an approximately Poisson distribution with a controlled mean.
  • Absolute concentration robustness (ACR) controllers achieve robust perfect adaptation.

Conclusions:

  • The proposed ACR controllers offer a robust method for controlling target species in CRNs.
  • This approach is effective for both deterministic and stochastic reaction systems.
  • ACR controllers are complementary to existing methods like antithetic feedback controllers for stochastic systems.