Probing patterning in microbial consortia with a cellular automaton for spatial organisation

On 16 September, 2022

Microbial consortia exhibit spatial patterning across diverse environments. Since probing the self-organization of natural microbial communities is limited by their inherent complexity, synthetic models have emerged as attractive alternatives. In this study, we develop novel frameworks of bacterial communication and explore the emergent spatiotemporal organization of microbes. Specifically, we built quorum sensing-mediated models of microbial growth that are utilized to characterize the dynamics of communities from arbitrary initial configurations and establish the effectiveness of our communication strategies in coupling the growth rates of microbes. Our simulations indicate that the behavior of quorum sensing-coupled consortia can be most effectively modulated by the rates of secretion of acyl homoserine lactones. Such a mechanism of control enables the construction of desired relative populations of constituent species in spatially organized populations. Our models accurately recapitulate previous experiments that have investigated pattern formation in synthetic multi-cellular systems. Additionally, our software tool enables the easy implementation and analysis of our frameworks for a variety of initial configurations and simplifies the development of sophisticated gene circuits facilitating distributed computing. Overall, we demonstrate the potential of spatial organization as a tunable parameter in synthetic biology by introducing a communication paradigm based on the location and strength of coupling of microbial strains.

Original Paper: 

  • [DOI] S. Venkatraghavan, S. Anantakrishnan, and K. Raman, “Probing patterning in microbial consortia with a cellular automaton for spatial organisation,” Scientific Reports, vol. 12, iss. 1, p. 17159, 2022.
    [bibtex]
    @article{Venkatraghavan2022Probing,
      title = {Probing patterning in microbial consortia with a cellular automaton for spatial organisation},
      volume = {12},
      copyright = {2022 The Author(s)},
      issn = {2045-2322},
      url = {https://www.nature.com/articles/s41598-022-20705-7},
      doi = {10.1038/s41598-022-20705-7},
      pmid = {36229548},
      abstract = {Microbial consortia exhibit spatial patterning across diverse environments. Since probing the self-organization of natural microbial communities is limited by their inherent complexity, synthetic models have emerged as attractive alternatives. In this study, we develop novel frameworks of bacterial communication and explore the emergent spatiotemporal organization of microbes. Specifically, we built quorum sensing-mediated models of microbial growth that are utilized to characterize the dynamics of communities from arbitrary initial configurations and establish the effectiveness of our communication strategies in coupling the growth rates of microbes. Our simulations indicate that the behavior of quorum sensing-coupled consortia can be most effectively modulated by the rates of secretion of acyl homoserine lactones. Such a mechanism of control enables the construction of desired relative populations of constituent species in spatially organized populations. Our models accurately recapitulate previous experiments that have investigated pattern formation in synthetic multi-cellular systems. Additionally, our software tool enables the easy implementation and analysis of our frameworks for a variety of initial configurations and simplifies the development of sophisticated gene circuits facilitating distributed computing. Overall, we demonstrate the potential of spatial organization as a tunable parameter in synthetic biology by introducing a communication paradigm based on the location and strength of coupling of microbial strains.},
      language = {en},
      number = {1},
      urldate = {2022-11-15},
      journal = {Scientific Reports},
      author = {Venkatraghavan, Sankalpa and Anantakrishnan, Sathvik and Raman, Karthik},
      month = oct,
      year = {2022},
      keywords = {Computational biology and bioinformatics, Microbiology, Systems biology},
      pages = {17159},
    }

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