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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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The Dependence of Cell Number Flux on the Relative Slime Strength
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pcbi-0030253-g013: The Dependence of Cell Number Flux on the Relative Slime Strength

Mentions: The relative strength of slime guidance is modeled by the parameter β in Equation 8. We used a value of 0.5 in the simulations (see Methods). Here, we varied β from 0 to 1.5, and calculate the cell number flux in swarms of wild-type cell and A+S− mutant type at the density of 50 K-S units (the same simulation setup as in Figure 6). The simulation data are plotted in Figure 13 along with the linear fits. We find that as the slime guidance effect gets stronger, the cell number flux increases. It increases slightly faster in the case of A+S− mutants than in the case of wild-type cells, with the slopes being 0.33 and 0.19 for A+S− mutant and wild-type cells, respectively. This result suggests that as the effect of slime guidance gets stronger, the local alignment of cells and the order of collective motion are both increased. However, this does not affect the results in Figure 7B much, since the increase of cell number flux in the case of A+S− mutants is only slightly faster than that in the case of wild-type cells. The ratio of two fitting functions remains greater than 2-fold until β = 18.5. This demonstrates robustness of our model with respect to the relative slime strength (see Figure 7B).


Social interactions in myxobacterial swarming.

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

The Dependence of Cell Number Flux on the Relative Slime Strength
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-0030253-g013: The Dependence of Cell Number Flux on the Relative Slime Strength
Mentions: The relative strength of slime guidance is modeled by the parameter β in Equation 8. We used a value of 0.5 in the simulations (see Methods). Here, we varied β from 0 to 1.5, and calculate the cell number flux in swarms of wild-type cell and A+S− mutant type at the density of 50 K-S units (the same simulation setup as in Figure 6). The simulation data are plotted in Figure 13 along with the linear fits. We find that as the slime guidance effect gets stronger, the cell number flux increases. It increases slightly faster in the case of A+S− mutants than in the case of wild-type cells, with the slopes being 0.33 and 0.19 for A+S− mutant and wild-type cells, respectively. This result suggests that as the effect of slime guidance gets stronger, the local alignment of cells and the order of collective motion are both increased. However, this does not affect the results in Figure 7B much, since the increase of cell number flux in the case of A+S− mutants is only slightly faster than that in the case of wild-type cells. The ratio of two fitting functions remains greater than 2-fold until β = 18.5. This demonstrates robustness of our model with respect to the relative slime strength (see Figure 7B).

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