Biomolecular LQR under Partial Observation

TL;DR

This paper applies biomolecular LQR theory to gene regulatory networks, revealing how optimal control principles explain the emergence of natural network motifs and linking evolutionary pressures to circuit design.

Contribution

It introduces a biomolecular LQR framework that explains the origin of gene regulatory motifs based on optimal control principles, connecting biology and control theory.

Findings

LQR-derived bio-controllers replicate natural motifs like auto-regulation.

The LQR cost function encodes environmental survival demands.

Theoretical basis for biological circuit design and evolution.

Abstract

This paper introduces a biomolecular Linear Quadratic Regulator (LQR) to investigate the design principles of gene regulatory networks. We show that for fundamental gene regulation network, the bio-controller derived from LQR theory precisely recapitulate natural network motifs, such as auto-regulation and incoherent feedforward loops. This emulation arises from a fundamental principle: the LQR cost function mathematically encodes environmental survival demands, which subsequently drives the selection of both network topology and biochemical parameters. Our work thus establishes a theoretical basis for interpreting biological circuit design, directly linking evolutionary pressures to observable regulatory structures.