| Title |
Influence of Nutrient Availability and Quorum Sensing on the Formation of Metabolically Inactive Microcolonies Within Structurally Heterogeneous Bacterial Biofilms: An Individual-Based 3D Cellular Automata Model
|
|---|---|
| Published in |
Bulletin of Mathematical Biology, January 2017
|
| DOI | 10.1007/s11538-017-0246-9 |
| Pubmed ID | |
| Authors |
Lakshmi Machineni, Anil Rajapantul, Vandana Nandamuri, Parag D. Pawar |
| Abstract |
The resistance of bacterial biofilms to antibiotic treatment has been attributed to the emergence of structurally heterogeneous microenvironments containing metabolically inactive cell populations. In this study, we use a three-dimensional individual-based cellular automata model to investigate the influence of nutrient availability and quorum sensing on microbial heterogeneity in growing biofilms. Mature biofilms exhibited at least three structurally distinct strata: a high-volume, homogeneous region sandwiched between two compact sections of high heterogeneity. Cell death occurred preferentially in layers in close proximity to the substratum, resulting in increased heterogeneity in this section of the biofilm; the thickness and heterogeneity of this lowermost layer increased with time, ultimately leading to sloughing. The model predicted the formation of metabolically dormant cellular microniches embedded within faster-growing cell clusters. Biofilms utilizing quorum sensing were more heterogeneous compared to their non-quorum sensing counterparts, and resisted sloughing, featuring a cell-devoid layer of EPS atop the substratum upon which the remainder of the biofilm developed. Overall, our study provides a computational framework to analyze metabolic diversity and heterogeneity of biofilm-associated microorganisms and may pave the way toward gaining further insights into the biophysical mechanisms of antibiotic resistance. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 23% |
| Student > Ph. D. Student | 8 | 21% |
| Student > Bachelor | 4 | 10% |
| Other | 2 | 5% |
| Student > Doctoral Student | 2 | 5% |
| Other | 0 | 0% |
| Unknown | 14 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 13% |
| Agricultural and Biological Sciences | 4 | 10% |
| Chemistry | 2 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 10 | 26% |
| Unknown | 14 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 March 2017.
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#20,410,007
of 22,959,818 outputs
Outputs from Bulletin of Mathematical Biology
#1,002
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#354,895
of 418,986 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#16
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