Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback

• Published in: Nature communications Vol. 13; no. 1; p. 4808
• Main Authors: Gutiérrez Mena, Joaquín, Kumar, Sant, Khammash, Mustafa
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.08.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/2253; 631/326/2565/855; 631/553/1745; 631/553/2695; 631/553/552; Automation; Bacteria; Biology; Cell culture; Composition; Control systems; Dynamic models; E coli; Feedback; Humanities and Social Sciences; Labor; Microorganisms; multidisciplinary; Optimal control; Robust control; Science; Science (multidisciplinary); Strains (organisms); Synthetic biology; Time measurement
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-32392-z

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. In addition, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities. Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. Here, in a community of two competing E. coli strains, the authors show that the relative abundances of the strains can be stabilized and steered dynamically with remarkable precision by coupling the cells to an automated computer-controlled feedback-loop.