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Mathematical modeling of dormant cell formation in growing biofilm.

Chihara K, Matsumoto S, Kagawa Y, Tsuneda S - Front Microbiol (2015)

Bottom Line: These hypotheses were implemented into a three-dimensional individual-based model of biofilm formation.Numerical simulations of the different mechanisms yielded qualitatively different spatiotemporal distributions of dormant cells in the growing biofilm.Based on these simulation results, we discuss what kinds of experimental studies are effective for discriminating dormant cell formation mechanisms in biofilms.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science and Medical Bioscience, Waseda University Tokyo, Japan.

ABSTRACT
Understanding the dynamics of dormant cells in microbial biofilms, in which the bacteria are embedded in extracellular matrix, is important for developing successful antibiotic therapies against pathogenic bacteria. Although some of the molecular mechanisms leading to bacterial persistence have been speculated in planktonic bacterial cell, how dormant cells emerge in the biofilms of pathogenic bacteria such as Pseudomonas aeruginosa remains unclear. The present study proposes four hypotheses of dormant cell formation; stochastic process, nutrient-dependent, oxygen-dependent, and time-dependent processes. These hypotheses were implemented into a three-dimensional individual-based model of biofilm formation. Numerical simulations of the different mechanisms yielded qualitatively different spatiotemporal distributions of dormant cells in the growing biofilm. Based on these simulation results, we discuss what kinds of experimental studies are effective for discriminating dormant cell formation mechanisms in biofilms.

No MeSH data available.


Spatial distributions of dormant cells obtained for different nutrient and oxygen concentrations in the bulk. The abundances of dormant cells are plotted against the biofilm height under conditions I (red), II (green), and III (black) (the conditions are defined in the text). Dormancy was induced by (A) stochastic process, (B) nutrient-dependent process, (C) oxygen-dependent process, and (D) time-dependent process. Each plot was smoothed by moving-average with a 10 μm window (3 points).
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Figure 3: Spatial distributions of dormant cells obtained for different nutrient and oxygen concentrations in the bulk. The abundances of dormant cells are plotted against the biofilm height under conditions I (red), II (green), and III (black) (the conditions are defined in the text). Dormancy was induced by (A) stochastic process, (B) nutrient-dependent process, (C) oxygen-dependent process, and (D) time-dependent process. Each plot was smoothed by moving-average with a 10 μm window (3 points).

Mentions: To confirm the difference between these three models, we can investigate how the dormant cell distribution responds to an increase or decrease in the nutrient and oxygen concentrations in the bulk liquid. Such an investigation should be simple and straightforward. Thus, biofilm formation was simulated under three conditions of nutrient and oxygen concentrations in the bulk liquid (expressed in units of gCOD/m3 and g/m3, respectively): (condition I) 10 gCOD/m3 and 0.4 g/m3, (condition II) 10 gCOD/m3 and 8 g/m3, and (condition III) 200 gCOD/m3 and 0.4 g/m3. When dormant cell formation was induced by stochastic or time-dependent processes, the dormant cell distribution was not qualitatively affected by altering the bulk concentrations of nutrient and oxygen, i.e., there was a height gradient in the abundance of dormant cells under all three conditions (Figures 3A,D). Conversely, if dormancy was induced by a nutrient-dependent process, dormant cells rarely emerged in the biofilm at very high nutrient concentration (200 gCOD/m3; condition III) (Figure 3B). Similarly, in the oxygen-dependent model, dormant cells rarely emerged when the oxygen concentration was high (8 g/m3; condition II) (Figure 3C). Therefore, these three models can be discriminated by investigating their qualitative responses to altered bulk concentrations of nutrient and oxygen.


Mathematical modeling of dormant cell formation in growing biofilm.

Chihara K, Matsumoto S, Kagawa Y, Tsuneda S - Front Microbiol (2015)

Spatial distributions of dormant cells obtained for different nutrient and oxygen concentrations in the bulk. The abundances of dormant cells are plotted against the biofilm height under conditions I (red), II (green), and III (black) (the conditions are defined in the text). Dormancy was induced by (A) stochastic process, (B) nutrient-dependent process, (C) oxygen-dependent process, and (D) time-dependent process. Each plot was smoothed by moving-average with a 10 μm window (3 points).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4446547&req=5

Figure 3: Spatial distributions of dormant cells obtained for different nutrient and oxygen concentrations in the bulk. The abundances of dormant cells are plotted against the biofilm height under conditions I (red), II (green), and III (black) (the conditions are defined in the text). Dormancy was induced by (A) stochastic process, (B) nutrient-dependent process, (C) oxygen-dependent process, and (D) time-dependent process. Each plot was smoothed by moving-average with a 10 μm window (3 points).
Mentions: To confirm the difference between these three models, we can investigate how the dormant cell distribution responds to an increase or decrease in the nutrient and oxygen concentrations in the bulk liquid. Such an investigation should be simple and straightforward. Thus, biofilm formation was simulated under three conditions of nutrient and oxygen concentrations in the bulk liquid (expressed in units of gCOD/m3 and g/m3, respectively): (condition I) 10 gCOD/m3 and 0.4 g/m3, (condition II) 10 gCOD/m3 and 8 g/m3, and (condition III) 200 gCOD/m3 and 0.4 g/m3. When dormant cell formation was induced by stochastic or time-dependent processes, the dormant cell distribution was not qualitatively affected by altering the bulk concentrations of nutrient and oxygen, i.e., there was a height gradient in the abundance of dormant cells under all three conditions (Figures 3A,D). Conversely, if dormancy was induced by a nutrient-dependent process, dormant cells rarely emerged in the biofilm at very high nutrient concentration (200 gCOD/m3; condition III) (Figure 3B). Similarly, in the oxygen-dependent model, dormant cells rarely emerged when the oxygen concentration was high (8 g/m3; condition II) (Figure 3C). Therefore, these three models can be discriminated by investigating their qualitative responses to altered bulk concentrations of nutrient and oxygen.

Bottom Line: These hypotheses were implemented into a three-dimensional individual-based model of biofilm formation.Numerical simulations of the different mechanisms yielded qualitatively different spatiotemporal distributions of dormant cells in the growing biofilm.Based on these simulation results, we discuss what kinds of experimental studies are effective for discriminating dormant cell formation mechanisms in biofilms.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science and Medical Bioscience, Waseda University Tokyo, Japan.

ABSTRACT
Understanding the dynamics of dormant cells in microbial biofilms, in which the bacteria are embedded in extracellular matrix, is important for developing successful antibiotic therapies against pathogenic bacteria. Although some of the molecular mechanisms leading to bacterial persistence have been speculated in planktonic bacterial cell, how dormant cells emerge in the biofilms of pathogenic bacteria such as Pseudomonas aeruginosa remains unclear. The present study proposes four hypotheses of dormant cell formation; stochastic process, nutrient-dependent, oxygen-dependent, and time-dependent processes. These hypotheses were implemented into a three-dimensional individual-based model of biofilm formation. Numerical simulations of the different mechanisms yielded qualitatively different spatiotemporal distributions of dormant cells in the growing biofilm. Based on these simulation results, we discuss what kinds of experimental studies are effective for discriminating dormant cell formation mechanisms in biofilms.

No MeSH data available.