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Chaos in synthetic microbial communities

Author

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  • Behzad D Karkaria
  • Angelika Manhart
  • Alex J H Fedorec
  • Chris P Barnes

Abstract

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.Author summary: In chaotic systems, infinitesimally small differences in the initial conditions will become amplified over time, making forecasting and prediction of behaviour impossible. Although we know that chaos can be observed in the complex networks of natural ecosystems, the field of biotechnology is interested in designing and building new microbial communities and the presence of chaotic behaviour is unexplored. In this paper, we present a statistical pipeline that can tell us how, when and why chaos arises in small microbial communities. We apply this approach to study a set of communities involving quorum sensing systems and amensal interactions through antimicrobial peptides. Out of 4182 interaction networks in these three strain communities, we identify the networks that have the highest propensity to produce chaos. We then explore the levers we can pull to bring these networks in and out of chaotic regimes. Our work is the first to look at chaos in synthetic microbial communities and indicates that chaos is an important design consideration.

Suggested Citation

  • Behzad D Karkaria & Angelika Manhart & Alex J H Fedorec & Chris P Barnes, 2022. "Chaos in synthetic microbial communities," PLOS Computational Biology, Public Library of Science, vol. 18(10), pages 1-24, October.
    • Handle: RePEc:plo:pcbi00:1010548
    DOI: 10.1371/journal.pcbi.1010548
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    References listed on IDEAS

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    1. Lutz Becks & Frank M. Hilker & Horst Malchow & Klaus Jürgens & Hartmut Arndt, 2005. "Experimental demonstration of chaos in a microbial food web," Nature, Nature, vol. 435(7046), pages 1226-1229, June.
    2. Dechert, W. Davis & Sprott, Julien C. & Albers, David J., 1999. "On the probability of chaos in large dynamical systems: A Monte Carlo study," Journal of Economic Dynamics and Control, Elsevier, vol. 23(8), pages 1197-1206, August.
    3. Stefano Allesina & Si Tang, 2012. "Stability criteria for complex ecosystems," Nature, Nature, vol. 483(7388), pages 205-208, March.
    4. Daniel Silk & Paul D.W. Kirk & Chris P. Barnes & Tina Toni & Anna Rose & Simon Moon & Margaret J. Dallman & Michael P.H. Stumpf, 2011. "Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes," Nature Communications, Nature, vol. 2(1), pages 1-6, September.
    5. Behzad D. Karkaria & Alex J. H. Fedorec & Chris P. Barnes, 2021. "Automated design of synthetic microbial communities," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
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