Bacterial Population
Bacterial Population - Related Videos
03:27
Establishing a Defined Gut Bacterial Population in Adult Mosquitoes
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2026
Source: Liu, X., et al. A Bacterial Oral Feeding Assay with Antibiotic-Treated Mosquitoes. J. Vis. Exp. (2020)This video demonstrates the depletion of native gut microbiota in mosquitoes using an antibiotic-containing sucrose solution. This is followed by controlled recolonization with a bacterial suspension delivered through a membrane feeding system to establish a defined bacterial population in the mosquito gut.
08:03
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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2021
This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.
06:45
Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains
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Cited by 7 •
2014
Bacteria may accumulate either detrimental or beneficial mutations during their lifetime. In a population of cells individuals that have accumulated beneficial mutations may rapidly outcompete their fellows. Here we present a simple procedure to visualize intraspecies competition in a bacterial cell population over time using fluorescently labeled...
12:29
Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli
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Cited by 3 •
2023
Antibiotic persistence describes the ability of small subpopulations within a sensitive isogenic population to transiently tolerate high doses of bactericidal antibiotics. The present protocol combines approaches to characterize the antibiotic persistence phenotype at the molecular and cellular levels after exposing Escherichia coli to lethal doses of ofloxacin.
14:47
Population Growth - Student Protocol
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2019
Exponential Population Growth Model ExpandNOTE: In these experiments you will use computer software to simulate different types of growth models. HYPOTHESES: The experimental hypothesis might be that if we increase either R, the reproductive rate or the initial population size, NT, that this will increase the population size after 10 generations in an exponential growth model compared to unmanipulated control populations. The null hypothesis may be that populations of different R and initial...
FAQs
Choosing between exponential and logistic growth depends on whether resource limits are relevant. Exponential growth represents continued increase under favorable conditions, whereas logistic growth incorporates a carrying capacity, the level at which resources constrain further population increase. Comparing these models helps determine whether observed bacterial population trends are consistent with unrestricted division or resource-limited behavior.
Binary fission provides the biological basis for modeling repeated population increase: cells divide, and the number of cells can rise rapidly when conditions remain favorable. A mathematical model can use the population’s growth rate to estimate doubling time, the time associated with an increase by a factor of two. This connects a measurable growth pattern with a quantitative parameter.
Population size is not the only quantity that can change. Composition may also shift as bacteria respond to environmental conditions, so a model can track variables and rates rather than treating the culture as unchanging. Examining these changes helps researchers compare growth conditions and quantify survival or environmental responses alongside overall increases or decreases in cell number.
An analysis can begin by representing bacterial population size with a variable and describing its change over time with a rate. Researchers then compare the resulting trend with exponential or logistic growth, estimate doubling time when appropriate, and evaluate how different conditions alter the population. This workflow turns observations of culture change into mathematical comparisons.
Mathematical population models can help assess antibiotic effects by comparing how population trends change under different conditions. A decline, slower increase, or altered pattern can be examined quantitatively rather than described only qualitatively. The resulting estimates support comparisons of bacterial survival and growth responses, while interpretation depends on the model selected for the situation.
These models are useful beyond a single culture because they support several kinds of scientific planning and interpretation. Researchers can use predicted population trends to analyze microbial cultures, examine ecological interactions, and inform bioprocess design. In each case, the mathematical representation links biological behavior to quantities such as growth rates, population size, carrying capacity, or doubling time.