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

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 a...

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Published inNature communications Vol. 13; no. 1; p. 4808
Main Authors Gutiérrez Mena, Joaquín, Kumar, Sant, Khammash, Mustafa
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 16.08.2022
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Abstract 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.
AbstractList 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. 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.
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.
Abstract 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.
ArticleNumber 4808
Author Kumar, Sant
Gutiérrez Mena, Joaquín
Khammash, Mustafa
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  surname: Khammash
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Snippet Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology...
Abstract Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic...
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631/326/2565/855
631/553/1745
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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
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Title Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback
URI https://link.springer.com/article/10.1038/s41467-022-32392-z
https://www.proquest.com/docview/2702707411
https://search.proquest.com/docview/2703417161
https://pubmed.ncbi.nlm.nih.gov/PMC9381578
https://doaj.org/article/c491d0537f1c4d25bed1d2e006a4e3ea
Volume 13
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