Chaos in synthetic microbial communities
Predictability is a fundamental requirement in biological engineering. As we move to building coordinated multicellular systems, the potential for such systems to display chaotic behaviour becomes a concern. Therefore understanding which systems show chaos is an important design consideration. We de...
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| Veröffentlicht in: | PLoS computational biology 2022-10, Vol.18 (10), p.e1010548-e1010548 |
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| creator | Karkaria, Behzad D Manhart, Angelika Fedorec, Alex J. H Barnes, Chris P |
| description | Predictability is a fundamental requirement in biological engineering. As we move to building coordinated multicellular systems, the potential for such systems to display chaotic behaviour becomes a concern. Therefore understanding which systems show chaos is an important design consideration. We developed a methodology to explore the potential for chaotic dynamics in small microbial communities governed by resource competition, intercellular communication and competitive bacteriocin interactions. Our model selection pipeline uses Approximate Bayesian Computation to first identify oscillatory behaviours as a route to finding chaotic behaviour. We have shown that we can expect to find chaotic states in relatively small synthetic microbial systems, understand the governing dynamics and provide insights into how to control such systems. This work is the first to query the existence of chaotic behaviour in synthetic microbial communities and has important ramifications for the fields of biotechnology, bioprocessing and synthetic biology. |
| doi_str_mv | 10.1371/journal.pcbi.1010548 |
| format | Article |
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We have shown that we can expect to find chaotic states in relatively small synthetic microbial systems, understand the governing dynamics and provide insights into how to control such systems. 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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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| subjects | Algorithms Analysis Bacteriocins Bayesian analysis Bayesian statistical decision theory Behavior Bioengineering Biology and Life Sciences Bioprocessing Biotechnology Chaos theory Competition Control systems Kalman filters Methods Microbial activity Microbial colonies Microbiomes Microorganisms Physical Sciences Research and Analysis Methods Social Sciences |
| title | Chaos in synthetic microbial communities |
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