Introduction

A bacterial growth curve shows how a bacterial population changes over time under specific environmental conditions. In a typical laboratory experiment, bacteria are introduced into a nutrient medium and their growth is measured at regular intervals. When bacteria grow in a closed culture system, the resulting growth pattern is commonly divided into four phases: lag, exponential, stationary and death.

Each phase reflects changes in the bacterial population and its surrounding environment. However, interpreting a growth curve requires more than recognizing these four stages. A culture may contain metabolically active bacteria even when its population size remains relatively stable. Similarly, a high optical density does not necessarily indicate a large number of viable cells.

Understanding these differences is important for microbiology students and researchers who study bacterial physiology, antimicrobial activity, microbial growth kinetics and laboratory culture methods.

What is a bacterial growth curve?

Bacterial growth generally involves an increase in cell number or biomass. Most bacteria reproduce by binary fission, a process in which one bacterial cell divides into two daughter cells. When environmental conditions are favourable, repeated cell divisions can produce a rapid increase in population size.

In laboratory microbiology, growth curves are commonly obtained from batch cultures. A batch culture contains a defined volume of growth medium in which nutrients are not continuously replenished and metabolic waste is not routinely removed. As bacteria multiply, they consume available nutrients and change the chemical conditions of their environment.

These changes influence how quickly the population grows and how long it can maintain active growth.

A bacterial growth curve may be constructed using optical density, colony-forming units, direct cell counts or other measurements. The resulting curves can differ because these methods measure different properties of the bacterial population.

The classical four-phase model provides a useful explanation of batch-culture growth, although individual cultures do not always show four clearly separated phases.

The four phases of bacterial growth

1. Lag phase: Adaptation before rapid growth

The lag phase occurs when bacteria adjust to their new culture conditions before establishing rapid population growth. During this period, the measured population size may remain relatively stable, but the cells are not necessarily inactive.

Bacteria may need to repair cellular damage, synthesize enzymes, adjust nutrient transport systems and reorganize their metabolism. The extent of these changes depends on the bacterial species, its previous growth conditions and the composition of the new medium.

For example, bacteria transferred from an actively growing culture into fresh medium with similar nutrients may have a short lag phase. Cells recovered from prolonged starvation may require more time before rapid division resumes.

Rolfe et al. (2012) investigated the lag phase of Salmonella enterica serovar Typhimurium and demonstrated substantial changes in gene expression and cellular physiology during a two-hour lag period. Their study also identified transient accumulation of metals, including iron, calcium and manganese, during adaptation.

These findings show that lag phase can involve active physiological preparation for subsequent growth. However, the precise mechanisms and duration of adaptation vary among bacterial species and culture conditions.

2. Exponential phase: Rapid bacterial multiplication

During the exponential phase, bacterial populations increase at an approximately constant specific growth rate, provided environmental conditions remain favourable.

The increase is multiplicative. For example, a population of 1,000 cells that doubles every hour would theoretically reach 2,000 cells after one hour, 4,000 after two hours and 8,000 after three hours.

This pattern is called exponential growth.

When cell number is plotted against time on an ordinary arithmetic scale, exponential growth produces an upward-curving line. However, when the logarithm of cell number is plotted against time, the exponential portion appears approximately linear.

The exponential phase is particularly useful for studying bacterial growth kinetics because researchers can estimate specific growth rates and doubling times from measurements collected during this period.

Nevertheless, bacterial cells within the same culture are not necessarily physiologically identical. Individual cells may differ in size, metabolic activity and division timing.

Growth rates also depend on the culture conditions. The same bacterial strain may multiply rapidly in a nutrient-rich laboratory medium but grow much more slowly when nutrients or other essential resources are limited.

3. Stationary phase: Growth slows or stabilizes

As a batch culture develops, nutrients become less available and environmental conditions may become increasingly restrictive. Metabolic products can accumulate, pH may change and oxygen or other essential resources may become limiting.

Eventually, the measured bacterial population or biomass may approach a plateau. This period is called the stationary phase.

Stationary phase is sometimes described as a condition in which bacterial division and death occur at equal rates. Although this can explain a stable population count in some circumstances, it is not the only possibility.

A culture may also reach a plateau because cell division has slowed substantially. Within the same population, some cells may remain metabolically active while others become growth-arrested or lose their ability to form colonies under the selected culture conditions.

Many bacteria alter their metabolism and stress-response systems during stationary phase. Certain species may produce specialized metabolites, while some endospore-forming bacteria can initiate sporulation under appropriate conditions.

However, stationary phase does not automatically cause sporulation, and most bacterial species do not form endospores.

These physiological changes are important when studying bacterial responses to antibiotics, nutrient limitation and other environmental stresses.

4. Death phase: Decline in recoverable bacteria

During prolonged cultivation, unfavourable conditions may cause the number of recoverable colony-forming units to decrease. This period is commonly described as the death or decline phase.

The interpretation depends on how the bacterial population is measured.

A reduction in colony-forming units indicates that fewer units can produce visible colonies under the conditions used for cultivation. It does not necessarily mean that all bacterial cells have disappeared or undergone complete physical destruction.

Some cells may die, while others may remain structurally intact but lose culturability. In certain bacterial species, environmental stress can produce a viable but nonculturable state, in which cells remain viable according to suitable physiological criteria but fail to grow on routine laboratory media.

Importantly, the absence of colony formation alone is not sufficient evidence that a cell is viable but nonculturable.

Optical density may also remain relatively high when culturable cell numbers decline because intact non-growing cells and other suspended particles can continue to scatter light.

Consequently, a decline phase may appear differently in optical-density and viable-count measurements.

How is bacterial growth measured?

Bacterial growth can be measured using several laboratory techniques. Optical-density measurement and viable plate counting are among the most widely used methods for routine bacterial growth experiments.

Although both can describe changes in a bacterial culture, they measure different biological properties.

Optical density: What does OD600 measure?

Optical density at 600 nm, written as OD600, is commonly used to estimate the turbidity of a bacterial suspension.

When light passes through a bacterial culture, cells scatter part of the incident light. The instrument records the resulting attenuation of transmitted light, producing an apparent optical-density measurement.

Within an appropriate measurement range, OD600 can provide a convenient indirect estimate of bacterial concentration or biomass.

The method is fast, does not require plating and allows repeated measurements during a growth experiment.

However, OD600 does not directly measure the number of living bacterial cells.

Dead but intact cells, cell aggregates and other suspended particles can contribute to light scattering. Bacterial cell size, shape and the optical properties of the measurement instrument can also influence the recorded value.

Therefore, an OD600 value of 1.0 does not represent a universal bacterial concentration across different species, strains or instruments.

When researchers need to convert optical density into an estimated cell concentration, they should establish an appropriate calibration relationship for their experimental system.

Viable plate counting: What does CFU/mL measure?

Viable plate counting estimates the concentration of colony-forming units in a bacterial sample.

A known volume of a suitably diluted bacterial suspension is spread or otherwise inoculated onto an appropriate culture medium. Following incubation, visible colonies are counted and the concentration in the original sample is calculated using the dilution factor and plated volume.

Results are commonly expressed as colony-forming units per millilitre, or CFU/mL.

For example, if 80 colonies develop from 0.1 mL of a sample diluted 10,000-fold, the estimated concentration in the original sample is:

CFU/mL = 80 × 10,000 ÷ 0.1

CFU/mL = 8,000,000

This estimate assumes that the dilution and plating were performed correctly and that the colonies can be counted reliably.

A colony-forming unit does not always represent a single bacterial cell. Several attached cells may produce one colony. Furthermore, only organisms capable of forming colonies under the selected incubation conditions are detected.

Therefore, CFU/mL represents recoverable colony-forming units rather than an exact count of all living bacterial cells.

Why can OD600 and CFU results differ?

Imagine that a bacterial culture is exposed to an antimicrobial treatment.

If many bacteria lose their ability to form colonies but remain structurally intact, the CFU count may decrease while the optical density changes relatively little.

This does not necessarily indicate an experimental error. OD600 measures turbidity, whereas viable plate counting measures colony formation.

Conversely, changes in bacterial cell size, aggregation or morphology can alter optical density without a proportional change in colony-forming units.

Parratt et al. (2025) compared several microbial cell-counting methods using Escherichia coli samples. Their investigation included colony counting, Coulter-principle measurements and fluorescence- and impedance-based flow cytometry.

The study demonstrated differences in measurement variability and proportionality between methods, particularly when comparing estimates of viable cells. These findings emphasize the importance of selecting a counting method that matches the biological quantity being investigated.

How to calculate bacterial doubling time

Doubling time is the period required for a bacterial population to increase twofold during approximately exponential growth.

If the initial population is N₀ and the population after a defined interval is Nₜ, the number of doublings can be calculated as:

n = [log₁₀(Nₜ) − log₁₀(N₀)] / log₁₀(2)

The doubling time is then calculated as:

g = t / n

Here, g is the doubling time, t is the elapsed time and n is the number of doublings.

Example calculation

Suppose a bacterial culture increases from 50,000 to 800,000 CFU/mL over four hours during exponential growth.

The population has increased by a factor of:

800,000 ÷ 50,000 = 16

Because 2⁴ = 16, the population has undergone four doublings.

Doubling time = 4 hours ÷ 4

Doubling time = 1 hour, or 60 minutes.

The specific growth rate, represented by μ, can also be estimated:

μ = [ln(Nₜ) − ln(N₀)] / t

For this example:

μ = ln(16) ÷ 4

μ ≈ 0.693 h⁻¹

These calculations assume positive population measurements and approximately exponential growth over the selected interval.

If measurements include lag phase, stationary phase or a period of changing growth rate, the resulting estimate may not represent the true exponential growth rate.

Why bacterial growth experiments sometimes give misleading results

A bacterial growth curve can appear smooth and consistent while still producing an inaccurate estimate of growth rate.

One important source of error is inadequate background correction during optical-density measurements.

Culture medium, microplate wells and other components of the measurement system can contribute to the recorded optical signal. If this background is not properly accounted for, the estimated growth rate may be distorted.

Atolia et al. (2020) demonstrated this effect using E. coli MG1655. In one experiment, the estimated maximum growth rate was 1.83 h⁻¹ after appropriate well-blank subtraction, compared with 0.49 h⁻¹ without subtraction.

These values were reported for a particular experimental system and should not be interpreted as universal growth rates for E. coli.

The study also demonstrated that initial cell density and previous culture conditions can influence growth-rate estimates.

Another source of uncertainty is the mathematical model used to describe the data.

Pla et al. (2015) compared several primary growth models using plate-count and absorbance measurements from Bacillus cereus, Listeria monocytogenes and Escherichia coli. Their findings showed that estimates of microbial growth parameters can depend on the selected model and measurement method.

Ghenu et al. (2024) further examined the challenges of estimating microbial growth rates using mathematical analyses, simulations and published growth datasets. Their work showed that inaccurate growth-rate estimates can also influence conclusions about relative microbial fitness.

Together, these studies demonstrate why bacterial growth experiments require appropriate background correction, suitable sampling intervals, consistent culture conditions and careful interpretation of mathematical models.

How to interpret a bacterial growth curve correctly

The first step is to identify what the vertical axis represents.

If the vertical axis shows OD600, the curve describes changes in culture turbidity. If it shows log₁₀ CFU/mL, it describes changes in recoverable colony-forming units.

These measurements should not be treated as interchangeable.

Next, consider the experimental conditions. Temperature, medium composition, oxygen availability, inoculum density and previous culture history can all influence the observed growth pattern.

The sampling interval is also important. Measurements collected too far apart may miss a short lag phase or provide insufficient information to estimate the maximum exponential growth rate accurately.

When comparing two bacterial strains, a higher final optical density does not necessarily indicate faster growth. One strain may grow rapidly but reach a lower final biomass, while another may grow more slowly and eventually reach a higher density.

Similarly, a longer lag phase does not necessarily imply a lower exponential growth rate.

For reliable interpretation, researchers should consider lag duration, exponential growth rate, final population density or biomass, measurement limitations and the reproducibility of the results.

Limitations of the classical bacterial growth curve

The classical four-phase model is particularly useful for describing bacterial populations grown in batch cultures under controlled laboratory conditions.

However, bacterial growth in natural environments is often more complex.

In soil, water, animal intestines and other microbial ecosystems, bacteria encounter changing nutrient availability, competition, environmental stresses and interactions with other organisms.

Different microbial populations may grow, remain inactive or decline simultaneously.

A growth curve obtained from an entire culture also represents a population-level measurement. It does not necessarily reveal differences in the physiological behaviour of individual bacterial cells.

Furthermore, growth measurements alone cannot determine whether bacteria are pathogenic, antibiotic-resistant or beneficial to a host. Such conclusions require additional experimental evidence.

Conclusion

The bacterial growth curve is a fundamental tool for studying microbial physiology. Its four classical phases describe how bacterial populations respond to changing conditions during batch cultivation.

However, the biological meaning of a growth curve depends on how growth is measured. Optical density provides a convenient estimate of turbidity, while viable plate counting estimates recoverable colony-forming units.

Understanding these distinctions, together with doubling-time calculations and common experimental limitations, helps researchers interpret bacterial growth measurements more accurately.

References

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