Bacterial Growth Curve Phases and Assessment Methods

Scientists assess bacterial growth trajectories to better predict infectious capacity and the conditions that aid proliferation. This article explores the key factors that influence bacterial replication, the distinct phases of the bacterial growth curve, methods for measuring bacterial expansion, and predictive bacterial growth models in the food industry.

What Is a Bacterial Growth Curve?

A bacterial growth curve is a graphical representation of a bacterial population’s fluctuation over time.¹ Bacteria are prokaryotes that typically replicate via binary fission, an asexual reproduction process in which a single bacterium divides to form two identical daughter cells, resulting in a rapid increase in the bacterial population.

Scientists monitor bacterial growth curves to compare the replication rates of different microbial strains and species under certain growth conditions, which is vital for predicting bacterial responses to external or internal perturbations. In addition, bacterial growth curves help scientists investigate non-growth states and bacterial development.

How do bacteria grow?

When bacteria grow at a constant temperature with optimal nutrient availability, each cell expands uniformly in size and volume at the same time interval, a phenomenon that researchers refer to as balanced growth.² The balanced state is closely linked to bacterial replication because this process leads to cell elongation, genetic material duplication, and binary fission that creates two daughter cells. A change in environmental conditions or nutrient availability may affect macromolecule production, such as DNA, RNA, and protein synthesis, ultimately altering bacterial growth.

Bacterial Growth Phases

Scientists have intensively studied and modeled bacterial growth based on monospecific cultures developed in the laboratory.³ Under limited nutrient conditions, the growth of a bacterial isolate typically follows a sigmoidal or S-shaped curve with four distinct phases: lag, log, stationary, and death. In contrast, when resources are unlimited, bacterial growth commonly exhibits a J-shaped curve, reflecting exponential growth.

The phases of an S-shaped bacterial growth curve include the following.

• Lag phase is the initial growth stage in which bacterial cells sense the environmental conditions and prepare for replication.⁴ Several factors influence the duration of the lag phase, including a cell’s physiological history and the growth medium composition. Even though cell numbers remain stable during lag phase, the bacteria are active, synthesizing new enzymes to harvest nutrients and adapt to their environment.
• Log phase, also known as the exponential phase, is characterized by rapid bacterial cell growth, with cells undergoing binary fission at a constant rate. During this phase, the bacterial population doubles at regular time intervals.⁵ Optimal growth conditions, including ideal nutrient availability, moisture, temperature, and pH, enable the constant growth rate. The log phase terminates when resources become depleted and metabolic waste products accumulate to toxic levels.
• Stationary phase occurs when bacterial growth ceases but the cells remain metabolically active.⁶ During stationary phase, bacteria undergo several physiological changes, including size reduction, increased cell wall rigidity, lower membrane fluidity, and slowed metabolism for stress tolerance, all of which enable them to survive in the new conditions. In the late stationary phase, bacteria enter a viable but non-culturable state. The duration of the stationary phase varies based on bacterial strain, nutrient availability, and the surrounding environment.
• Death phase, also known as the decline phase, is the stage in which the number of viable cells decreases due to nutritional depletion and the accumulation of metabolic waste. In this phase, the bacterial population declines rapidly because the death rate exceeds the reproduction rate.⁷

Table: Factors that influence a bacterial growth curve^3,7

Bacterial Growth Assessment Methods

Scientists commonly record a bacterial growth curve by measuring optical density (OD), which reflects cell concentration.⁸ This process involves cultivating bacteria in a liquid medium and periodically measuring the OD at a specific wavelength, typically 600nm, using a spectrophotometer. As bacteria multiply, the medium becomes increasingly turbid, resulting in higher OD readings. By recording these measurements at regular intervals, scientists can plot a growth curve that illustrates the different bacterial growth phases.

Recent advances in automation and parallel measurements enable scientists to achieve higher throughput. For example, a cost-effective turbidostat is a device that continuously monitors the turbidity of a bacterial culture and estimates bacterial growth rate.⁹ In addition to turbidostats, automated high-throughput cultivation systems can support multi-mode measurements, where scientists can measure fluorescence along with OD to assess cell growth and viability more accurately. This system can simultaneously measure biomass and track specific cellular components or gene expression.¹⁰

Scientists also measure bacterial growth rates at the single-cell level using time-lapse microscopy.¹¹ This method helps researchers analyze bacterial growth through imaging 2D microcolonies over time. Because manual endpoint colony counting is a tedious and time-consuming process, researchers have designed image analysis devices and software that allow high-throughput assessment.¹¹

Additionally, by combining time-lapse microscopy and microfluidics, researchers can monitor an individual cell’s size and division over long periods. Microfluidic devices allow them to control the cell’s environment and rapidly change the culture medium by introducing different growth conditions or adding antibiotics, without disrupting bacterial cell monitoring.

Scientists have also developed artificial intelligence (AI) and machine learning (ML) algorithms to monitor bacterial growth.¹² By combining high-throughput bacterial imaging data with ML, they can quantitatively connect the environmental factors such as temperature, pH, and oxygen availability to bacterial growth patterns. High-throughput imaging provides a large, comprehensive visual dataset on bacterial cells over time. ML algorithms analyze this data to identify subtle correlations that are generally overlooked in traditional methods. This integrated approach enables scientists to predict how bacteria respond to different growth conditions.

ScanGrow, a deep learning-based software, enables real-time bacterial growth monitoring by automatically scanning and processing images of live bacteria within their culture environment.¹³ The analysis of microbial growth assays (AMiGA) is another software that facilitates bacterial growth curve monitoring without assuming a specific curve shape.¹⁴ This software utilizes Gaussian process (GP) regressions to model growth and estimate key parameters, including lag time and growth rate.

Bacterial Growth Prediction and Food Science

Bacterial growth in food causes spoilage and can lead to foodborne illness.¹⁵ Predicting bacterial growth is essential to prevent food spoilage and ensure food safety. Numerous predictive models help analyze growth curves of various bacterial species.¹⁶

Salmonella enterica, Escherichia coli O157:H7, and Listeria monocytogenes are bacterial pathogens frequently associated with foodborne illnesses. For instance, bacteria may contaminate fresh produce during growth, harvest, washing, packaging, shipping, or storing. Modeling the growth and survival of these pathogenic bacteria helps scientists predict food safety risks and microbial spoilage in the food chain.

In real-world settings, factors such as temperature, pH, and nutrient levels can fluctuate significantly. However, numerous models predict bacterial growth curves under constant conditions. To overcome this limitation, scientists have designed dynamic models to predict bacterial replication rates during fluctuating environmental conditions. For example, researchers applied the Baranyi model and the Baranyi-Ratkowsky model to predict bacterial growth in vegetables and meat products under variable conditions.^16,17

Another key limitation of the current growth models is that they cannot account for contamination by multiple bacterial populations in changing environments.¹⁸ To improve the accuracy and practical relevance of bacterial growth predictions, scientists are integrating technologies such as metagenomics, AI, ML, whole-genome sequencing (WGS), robotics, and time-temperature indicators.¹⁸ This strategy will enhance the predictive capacity of models to assess the safety and shelf life of fresh produce and meat products.

What Comes Next for Bacterial Growth Research?

It is crucial to understand how bacteria behave under varied conditions and changing scenarios. This knowledge enables scientists to manage bacterial growth more effectively, particularly where bacteria may adapt rapidly. Recent technological advances, including microfluidics, high-throughput imaging, and ML, have significantly improved the ability to observe, analyze, and predict bacterial behavior at both the population and single-cell levels. These tools offer detailed insights into how bacteria respond to environmental changes, enabling the development of better strategies to control harmful bacteria and harness beneficial strains.

FAQ

What is bacterial growth?

• Bacterial growth is the process by which the number of bacterial cells in a population increases. Bacteria are prokaryotic organisms that typically reproduce through binary fission, resulting in a larger population of individual cells over time.

What are the main stages of a bacterial growth curve?

• A bacterial growth curve often includes four stages: lag (adaptation), exponential (rapid division), stationary (stabilization), and death (decline). The growth curve varies with environmental and nutritional factors.

What factors affect bacterial growth?

• Several factors influence bacterial growth, including temperature, pH, nutrient availability, oxygen levels, moisture, and the presence of inhibitory substances such as antibiotics or disinfectants. Every bacterial species requires specific growth conditions; for example, some thrive in oxygen-rich environments (aerobic bacteria), while others proliferate in the absence of oxygen (anaerobic bacteria).

Why is understanding bacterial growth important?

• Understanding bacterial growth is crucial for controlling infections, ensuring food safety, and managing pathogens in medical, industrial, and environmental settings. It helps scientists prevent the spread of harmful bacteria, develop effective treatments, and harness beneficial bacteria for useful applications such as biotechnology and waste management.