Principles of feedback control have been shown to naturally arise in biological systems and have been applied with success to build synthetic circuits. Here, we present an implementation of a proportional-integral-derivative (PID) controller as a chemical reaction network with mass-action kinetics. This makes the controller synthesizable in vitro using DNA strand displacement technology, owing to its demonstrated capability of realizing arbitrary reaction-network designs as interacting DNA molecules. Previous related work has studied biological PID architectures using linearizations of nonlinear dynamics arising in both the controller components and in the plant. In this article, we present a proof of correctness of our nonlinear design in closed loop using arguments from singular perturbation theory. As an application to show the effectiveness of our controller, we provide numerical simulations on a genetic model to perform PID feedback control of protein expression.

PID Control of Biochemical Reaction Networks

Tribastone M.;
2022

Abstract

Principles of feedback control have been shown to naturally arise in biological systems and have been applied with success to build synthetic circuits. Here, we present an implementation of a proportional-integral-derivative (PID) controller as a chemical reaction network with mass-action kinetics. This makes the controller synthesizable in vitro using DNA strand displacement technology, owing to its demonstrated capability of realizing arbitrary reaction-network designs as interacting DNA molecules. Previous related work has studied biological PID architectures using linearizations of nonlinear dynamics arising in both the controller components and in the plant. In this article, we present a proof of correctness of our nonlinear design in closed loop using arguments from singular perturbation theory. As an application to show the effectiveness of our controller, we provide numerical simulations on a genetic model to perform PID feedback control of protein expression.
Biological systems
Chemical process control
Modeling
Nonlinear systems
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11771/20857
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