Growth Curve Plotting
Growth Curve Plotting - Related Videos
12:15
Growth Curves: Generating Growth Curves Using Colony Forming Units and Optical Density Measurements
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2023
Source: Andrew J. Van Alst1, Rhiannon M. LeVeque1, Natalia Martin1, and Victor J. DiRita1 1 Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America Growth curves provide valuable information on bacterial growth kinetics and cell physiology. They allow us to determine how bacteria respond in variable growth conditions as well as to define optimal growth parameters for a given bacterium. An archetypal growth curve progresses...
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Bacterial Growth Curve Analysis and its Environmental Applications
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2023
Source: Laboratories of Dr. Ian Pepper and Dr. Charles Gerba - The University of Arizona Demonstrating Author: Luisa Ikner Bacteria are among the most abundant life forms on Earth. They are found in every ecosystem and are vital for everyday life. For example, bacteria affect what people eat, drink, and breathe, and there are actually more bacterial cells within a person’s body than mammalian cells. Because of the importance of bacteria, it is preferable to study particular species of bacteria...
01:28
Bacterial Growth Curve
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2025
The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
03:30
Assessing Rotavirus Growth Curve by Plaque Assay
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2026
Source: Kadoya, S., Sano, D. Assays for the Specific Growth Rate and Cell-binding Ability of Rotavirus. J. Vis. Exp. (2019).This video demonstrates the use of a plaque assay to assess the rotavirus growth curve. The method involves infecting host cells with diluted virus, overlaying with agarose to restrict spread, and staining to visualize plaques formed by viral replication. By counting plaque-forming units at defined time points, the resulting growth curve reveals the lag, exponential, and...
02:34
Arrhenius Plots
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2020
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules. The Arrhenius equation can be used to...
FAQs
The curve can distinguish lag, exponential, stationary, and death phases, allowing researchers to describe how population size changes across an experiment. These phases provide a structured way to compare growth dynamics between biological conditions. Examining their appearance and timing can show whether a population is actively increasing, remaining stable, or declining.
Logarithmic scaling can make exponential growth easier to recognize because it presents changes in population measurements in a form that clarifies the growth pattern. This view supports more consistent estimation of growth rate and doubling time. Researchers can therefore examine rapid increases more effectively than with a simple linear presentation alone.
Growth Curve Plotting can use cell number, density, or biomass as the population measurement. The selected measure should be recorded at defined time intervals so that changes can be compared across the experiment. Using these alternatives allows researchers to describe population growth in a way suited to microbial physiology or cell culture studies.
Begin by selecting a population measurement, then collect values at defined intervals throughout the observation period. Plot those measurements against time and inspect the resulting pattern for recognizable growth phases. The completed curve can then support estimates of growth rate and doubling time and enable comparisons between experimental conditions.
Researchers can plot population measurements from different nutrient or environmental conditions on comparable time axes. Differences in the resulting curves can reveal how those conditions affect growth dynamics, including the timing or prominence of observed phases. This comparison provides a quantitative framework for assessing population responses rather than relying only on a final measurement.
Growth curves allow researchers to compare population changes with and without an antimicrobial treatment over time. The plotted measurements can show whether growth dynamics differ under treatment and can support interpretation of population decline or altered progression through growth phases. This makes the approach useful for studying biological responses to antimicrobial conditions.