Disruption of microbial communication yields a two-dimensional percolation transition

Silva, Kalinga Pavan T.; Yusufaly, Tahir; Chellamuthu, Prithiviraj; Boedicker, James

doi:10.1103/physreve.99.042409

Cited by 9 publications

(4 citation statements)

References 55 publications

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“…The effect of spatial correlations is a general question that is fundamental to understanding multicellular behaviors. The length scale of cell-to-cell signaling in quorum sensing bacterial communities depends on the establishment of spatial correlations [7, 29]. Moreover, the interplay of spatial heterogeneity and signaling lengthscale dictates the cooperativity of pathogenic Pseudomonas aeruginosa biofilms [30].…”

Section: Discussionmentioning

confidence: 99%

“…As a result, many investigators have turned to percolation theory to describe signal transmission in multicellular systems. In bacterial communities, percolation theory has been used to predict the scaling laws that result from signal disruption during quorum sensing [7]. In neuroscience, percolation theory has been used to describe (i) the transition from a fully connected to a disconnected electrical network in rat hippocampus cultures [8, 9], (ii) the spatiotemporal structure of viral propagation within astrocyte monolayers [10], and (iii) the transition from conscious to unconscious brain activities during general anesthesia [11].…”

Section: Introductionmentioning

confidence: 99%

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Statistics of correlated percolation in a bacterial community

et al. 2019

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Signal propagation over long distances is a ubiquitous feature of multicellular communities, but cell-to-cell variability can cause propagation to be highly heterogeneous. Simple models of signal propagation in heterogenous media, such as percolation theory, can potentially provide a quantitative understanding of these processes, but it is unclear whether these simple models properly capture the complexities of multicellular systems. We recently discovered that in biofilms of the bacterium Bacillus subtilis, the propagation of an electrical signal is statistically consistent with percolation theory, and yet it is reasonable to suspect that key features of this system go beyond the simple assumptions of basic percolation theory. Indeed, we find here that the probability for a cell to signal is not independent from other cells as assumed in percolation theory, but instead is correlated with its nearby neighbors. We develop a mechanistic model, in which correlated signaling emerges from cell division, phenotypic inheritance, and cell displacement, that reproduces the experimentally observed correlations. We find that the correlations do not significantly affect the spatial statistics, which we rationalize using a renormalization argument. Moreover, the fraction of signaling cells is not constant in space, as assumed in percolation theory, but instead varies within and across biofilms. We find that this feature lowers the fraction of signaling cells at which one observes the characteristic power-law statistics of cluster sizes, consistent with our experimental results. We validate the model using a mutant biofilm whose signaling probability decays along the propagation direction. Our results reveal key statistical features of a correlated signaling process in a multicellular community. More broadly, our results identify extensions to percolation theory that do or do not alter its predictions and may be more appropriate for biological systems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistics of correlated percolation in a bacterial community

et al. 2019

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“…Quorum sensing similarly can exhibit a percolation transition. Spatial patterns of the expression of quorum sensing-regulated gene expression were studied in a synthetic two-strain community composed of a signal producing strain and a signal degrading strain [94]. The signal producing strain released C4-AHL and responded to a high concentration of signal by producing a fluorescent reporter protein.…”

Section: Spatial Self-organization and Percolation Improve The Effici...mentioning

confidence: 99%

Computation in bacterial communities

Ostovar¹,

Naughton²,

Boedicker³

2020

Phys. Biol.

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Bacteria across many scales are involved in a dynamic process of information exchange to coordinate activity and community structure within large and diverse populations. The molecular components bacteria use to communicate have been discovered and characterized, and recent efforts have begun to understand the potential for bacterial signal exchange to gather information from the environment and coordinate collective behaviors. Such computations made by bacteria to coordinate the action of a population of cells in response to information gathered by a multitude of inputs is a form of collective intelligence. These computations must be robust to fluctuations in both biological, chemical, and physical parameters as well as to operate with energetic efficiency. Given these constraints, what are the limits of computation by bacterial populations and what strategies have evolved to ensure bacterial communities efficiently work together? Here the current understanding of information exchange and collective decision making that occur in microbial populations will be reviewed. Looking toward the future, we consider how a deeper understanding of bacterial computation will inform future direction in microbiology, biotechnology, and biophysics.

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“…Many bacteria regulate gene expression in response to the external concentration of autoinducer, including regulation of processes related to biofilm formation, virulence, and horizontal gene transfer [1–4]. Although QS is historically viewed as a process of a single species regulating its own gene expression, numerous reports have shown signal exchange between species contributed to regulation of QS phenotypes [5–10]. Such crosstalk between cells is usually the result of two bacterial strains producing chemical variants of a QS signal.…”

Section: Introductionmentioning

confidence: 99%

A neural network model predicts community-level signaling states in a diverse microbial community

Silva

Boedicker

2019

PLoS Comput Biol

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Signal crosstalk within biological communication networks is common, and such crosstalk can have unexpected consequences for decision making in heterogeneous communities of cells. Here we examined crosstalk within a bacterial community composed of five strains of Bacillus subtilis , with each strain producing a variant of the quorum sensing peptide ComX. In isolation, each strain produced one variant of the ComX signal to induce expression of genes associated with bacterial competence. When strains were combined, a mixture of ComX variants was produced resulting in variable levels of gene expression. To examine gene regulation in mixed communities, we implemented a neural network model. Experimental quantification of asymmetric crosstalk between pairs of strains parametrized the model, enabling the accurate prediction of activity within the full five-strain network. Unlike the single strain system in which quorum sensing activated upon exceeding a threshold concentration of the signal, crosstalk within the five-strain community resulted in multiple community-level quorum sensing states, each with a unique combination of quorum sensing activation among the five strains. Quorum sensing activity of the strains within the community was influenced by the combination and ratio of strains as well as community dynamics. The community-level signaling state was altered through an external signal perturbation, and the output state depended on the timing of the perturbation. Given the ubiquity of signal crosstalk in diverse microbial communities, the application of such neural network models will increase accuracy of predicting activity within microbial consortia and enable new strategies for control and design of bacterial signaling networks.

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Disruption of microbial communication yields a two-dimensional percolation transition

Cited by 9 publications

References 55 publications

Statistics of correlated percolation in a bacterial community

Statistics of correlated percolation in a bacterial community

Computation in bacterial communities

A neural network model predicts community-level signaling states in a diverse microbial community

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