A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition

TL;DR

This paper introduces a bioreactor-based control system for microbial consortia that enables stable, precise regulation of population composition without genetic modifications or environmental alterations, validated through in vivo experiments.

Contribution

It presents a novel bioreactor architecture combined with model-based and sim-to-real learning controllers for stable microbial consortium regulation.

Findings

Achieved precise control of consortium density and composition.

Successfully tracked time-varying references and recovered from perturbations.

Validated in vivo on Escherichia coli two-strain consortium.

Abstract

Microbial consortia offer significant biotechnological advantages over monocultures for bioproduction. However, industrial deployment is hampered by the lack of scalable architectures to ensure stable coexistence between populations. Existing strategies rely on genetic modifications, which impose metabolic load, or environmental changes, which can reduce production. We present a versatile control architecture to regulate density and composition of a two-strain consortium without genetic engineering or drastic environmental changes. Our bioreactor-based control architecture comprises a mixing chamber where both strains are co-cultured and a reservoir sustaining the slower-growing strain. For both chambers we develop model-based and sim-to-real learning controllers. The control architecture is then validated in vivo on a two-strain Escherichia coli consortium, achieving precise and robust regulation of consortium density and composition, including tracking of time-varying references and recovery from perturbations.