Diffusion-Limited Growth of Microbial Colonies

Tronnolone, Hayden; Tam, Alexander; Szenczi, Zoltán; Green, J. E. F.; Balasuriya, Sanjeeva; Tek, Ee Lin; Gardner, J. L.; Sundstrom, Joanna F.; Jiranek, Vladimir; Oliver, Stephen G.; Binder, Benjamin J.

doi:10.1038/s41598-018-23649-z

Cited by 37 publications

(20 citation statements)

References 35 publications

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“…Reynolds & Fink [4] were the first to perform mat formation experiments with S. cerevisiae, and similar methods have been used in subsequent studies [5,12,21]. In these experiments, yeast cells are inoculated on semi-solid (0.3%) agar plates.…”

Section: (A) Biological Backgroundmentioning

confidence: 99%

A thin-film extensional flow model for biofilm expansion by sliding motility

et al. 2019

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In the presence of glycoproteins, bacterial and yeast biofilms are hypothesized to expand by sliding motility. This involves a sheet of cells spreading as a unit, facilitated by cell proliferation and weak adhesion to the substratum. In this paper, we derive an extensional flow model for biofilm expansion by sliding motility to test this hypothesis. We model the biofilm as a two-phase (living cells and an extracellular matrix) viscous fluid mixture, and model nutrient depletion and uptake from the substratum. Applying the thin-film approximation simplifies the model, and reduces it to one-dimensional axisymmetric form. Comparison with Saccharomyces cerevisiae mat formation experiments reveals good agreement between experimental expansion speed and numerical solutions to the model with O ( 1 ) parameters estimated from experiments. This confirms that sliding motility is a possible mechanism for yeast biofilm expansion. Having established the biological relevance of the model, we then demonstrate how the model parameters affect expansion speed, enabling us to predict biofilm expansion for different experimental conditions. Finally, we show that our model can explain the ridge formation observed in some biofilms. This is especially true if surface tension is low, as hypothesized for sliding motility.

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Section: (A) Biological Backgroundmentioning

confidence: 99%

A thin-film extensional flow model for biofilm expansion by sliding motility

et al. 2019

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“…In both cases, TAMMiCol is able to identify the colony with a high degree of accuracy, demonstrating the versatility of the software. The statistics used here are also suitable for analysing these colonies [34, 35].…”

Section: Resultsmentioning

confidence: 99%

“…Cross-validation analysis found that only 6 features were required to classify colonies as either smooth or fluffy. A set of three spatial indices designed specifically to quantify the growth of filamentous yeast colonies [7] have been shown to provide useful information on the morphology of yeast colonies and microbial mats [34, 35].…”

Section: Introductionmentioning

confidence: 99%

TAMMiCol: Tool for analysis of the morphology of microbial colonies

et al. 2018

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Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.

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“…To examine this notion, here we study the formation of branching patterns, which emerge in many microbes when growing on solid surfaces. Examples include bacterial species such as Pseudomonas and Bacillus (Fig 1A), fungi, slime molds, and lichen (Sumner, 2001;Ben-Jacob et al, 2004;Tero et al, 2010;Tronnolone et al, 2018). Since branching patterns in microbial colonies often emerge under nutrient-deprived conditions (Shimada et al, 2004), it is plausible that branched colony growth may represent a survival strategy for microbes.…”

Section: Introductionmentioning

confidence: 99%

Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa

Luo

Wang

et al. 2021

Molecular Systems Biology

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Branching pattern formation is common in many microbes. Extensive studies have focused on addressing how such patterns emerge from local cell-cell and cell-environment interactions. However, little is known about whether and to what extent these patterns play a physiological role. Here, we consider the colonization of bacteria as an optimization problem to find the colony patterns that maximize colony growth efficiency under different environmental conditions. We demonstrate that Pseudomonas aeruginosa colonies develop branching patterns with characteristics comparable to the prediction of modeling; for example, colonies form thin branches in a nutrient-poor environment. Hence, the formation of branching patterns represents an optimal strategy for the growth of Pseudomonas aeruginosa colonies. The quantitative relationship between colony patterns and growth conditions enables us to develop a coarse-grained model to predict diverse colony patterns under more complex conditions, which we validated experimentally. Our results offer new insights into branching pattern formation as a problem-solving social behavior in microbes and enable fast and accurate predictions of complex spatial patterns in branching colonies.

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Diffusion-Limited Growth of Microbial Colonies

Cited by 37 publications

References 35 publications

A thin-film extensional flow model for biofilm expansion by sliding motility

A thin-film extensional flow model for biofilm expansion by sliding motility

TAMMiCol: Tool for analysis of the morphology of microbial colonies

Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa

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