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Social interactions in myxobacterial swarming.

Wu Y, Jiang Y, Kaiser D, Alber M - PLoS Comput. Biol. (2007)

Bottom Line: Also, the model is able to quantify the contributions of S motility and A motility to swarming.Some pathogenic bacteria spread over infected tissue by swarming.The model described here may shed some light on their colonization process.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of Notre Dame, Notre Dame, Indiana, United States of America.

ABSTRACT
Swarming, a collective motion of many thousands of cells, produces colonies that rapidly spread over surfaces. In this paper, we introduce a cell-based model to study how interactions between neighboring cells facilitate swarming. We chose to study Myxococcus xanthus, a species of myxobacteria, because it swarms rapidly and has well-defined cell-cell interactions mediated by type IV pili and by slime trails. The aim of this paper is to test whether the cell contact interactions, which are inherent in pili-based S motility and slime-based A motility, are sufficient to explain the observed expansion of wild-type swarms. The simulations yield a constant rate of swarm expansion, which has been observed experimentally. Also, the model is able to quantify the contributions of S motility and A motility to swarming. Some pathogenic bacteria spread over infected tissue by swarming. The model described here may shed some light on their colonization process.

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Related in: MedlinePlus

Simulation Images and Linear Growth of Colony(A) and (B) are pictures of the edges of the A+S− and wild-type (A+S+) swarms, respectively, after 200 min of simulation.(C) Linear increase in the number of cells in the simulation domain with time. The red lines are best fits of simulation data with slopes indicated in the plot.
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pcbi-0030253-g006: Simulation Images and Linear Growth of Colony(A) and (B) are pictures of the edges of the A+S− and wild-type (A+S+) swarms, respectively, after 200 min of simulation.(C) Linear increase in the number of cells in the simulation domain with time. The red lines are best fits of simulation data with slopes indicated in the plot.

Mentions: This relation shows that the cell number flux across the lower boundary of the initial area (or the increase rate of total cell number in the whole simulation domain) is linearly correlated with the colony expansion rate in Figure 2. We calculate the cell number flux rather than expansion rate directly. Therefore, we do not have to increase the simulation domain or the total number of simulated cells. Further details of the simulation setup, implementation of the algorithm, and the choices of parameters are described in Methods and Table 1. Simulations show formation of long clusters (peninsulas) in both A+S+ and A+S− cases (see Figure 6A and 6B), which was observed experimentally [10].


Social interactions in myxobacterial swarming.

Wu Y, Jiang Y, Kaiser D, Alber M - PLoS Comput. Biol. (2007)

Simulation Images and Linear Growth of Colony(A) and (B) are pictures of the edges of the A+S− and wild-type (A+S+) swarms, respectively, after 200 min of simulation.(C) Linear increase in the number of cells in the simulation domain with time. The red lines are best fits of simulation data with slopes indicated in the plot.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2230681&req=5

pcbi-0030253-g006: Simulation Images and Linear Growth of Colony(A) and (B) are pictures of the edges of the A+S− and wild-type (A+S+) swarms, respectively, after 200 min of simulation.(C) Linear increase in the number of cells in the simulation domain with time. The red lines are best fits of simulation data with slopes indicated in the plot.
Mentions: This relation shows that the cell number flux across the lower boundary of the initial area (or the increase rate of total cell number in the whole simulation domain) is linearly correlated with the colony expansion rate in Figure 2. We calculate the cell number flux rather than expansion rate directly. Therefore, we do not have to increase the simulation domain or the total number of simulated cells. Further details of the simulation setup, implementation of the algorithm, and the choices of parameters are described in Methods and Table 1. Simulations show formation of long clusters (peninsulas) in both A+S+ and A+S− cases (see Figure 6A and 6B), which was observed experimentally [10].

Bottom Line: Also, the model is able to quantify the contributions of S motility and A motility to swarming.Some pathogenic bacteria spread over infected tissue by swarming.The model described here may shed some light on their colonization process.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of Notre Dame, Notre Dame, Indiana, United States of America.

ABSTRACT
Swarming, a collective motion of many thousands of cells, produces colonies that rapidly spread over surfaces. In this paper, we introduce a cell-based model to study how interactions between neighboring cells facilitate swarming. We chose to study Myxococcus xanthus, a species of myxobacteria, because it swarms rapidly and has well-defined cell-cell interactions mediated by type IV pili and by slime trails. The aim of this paper is to test whether the cell contact interactions, which are inherent in pili-based S motility and slime-based A motility, are sufficient to explain the observed expansion of wild-type swarms. The simulations yield a constant rate of swarm expansion, which has been observed experimentally. Also, the model is able to quantify the contributions of S motility and A motility to swarming. Some pathogenic bacteria spread over infected tissue by swarming. The model described here may shed some light on their colonization process.

Show MeSH
Related in: MedlinePlus