A growth curve depicts the change in the number of organisms in a population over time under defined conditions. In microbiology, a typical bacterial growth curve obtained in a closed batch culture displays characteristic lag, exponential, stationary and death phases.
Explanation
When a microbial culture is inoculated into a fresh medium, cells initially undergo a lag phase during which they adapt to the new environment, synthesize essential enzymes and repair damage; no net increase in cell number occurs. Once acclimated, the population enters the exponential or log phase, where cells divide at a constant rate and the population size doubles at regular intervals. This phase is characterized by balanced growth, and growth rates are influenced by temperature, nutrient concentration and genetic factors. As nutrients become depleted and waste products accumulate, growth slows and the population reaches the stationary phase: the rate of cell division equals the rate of cell death, and total cell numbers remain relatively constant. Eventually, cells enter the death phase as conditions become unfavourable, leading to a decline in viable cell counts. Growth curves can be monitored by measuring optical density, viable plate counts or biomass dry weight. Parameters derived from growth curves, such as doubling time and maximum specific growth rate (μmax), are important for understanding microbial physiology, optimizing fermentation processes and evaluating the effects of antimicrobial agents.
Notable examples and facts
In laboratory practice, plotting the growth curve of Escherichia coli in Luria–Bertani broth at 37 °C reveals a lag phase of about 30 minutes followed by a log phase with a doubling time of roughly 20 minutes under optimal conditions. Yeast growth curves in brewing and baking show similar phases but different timescales. In industrial biotechnology, controlling the length of the log and stationary phases is key to maximizing product yield, as many secondary metabolites are produced during stationary phase. Growth curves are also used to test antibiotic efficacy: exposing bacteria to an antibiotic and monitoring changes in the growth curve can reveal bacteriostatic or bactericidal effects. Mathematical models such as the logistic equation and Gompertz model describe growth curves and allow prediction of population behaviour under various conditions. A growth curve provides a graphical summary of microbial population dynamics, revealing how environmental factors and growth conditions affect replication and survival. Related Terms: Lag phase, Log phase, Stationary phase, Death phase, Doubling time