Predictive microbiology is based on the idea that the response of microorganisms to environmental conditionals is reproducible, and therefore if conditions are known then the response of characterized microorganisms is predictable.
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Predictive microbiology has historically been employed particularly in the food industry, where it is useful to estimate the shelf life of products and understand the conditions under which microbial growth could influence batch production. Accurate predictive models significantly reduce the need for microbial testing during food production and therefore generate a more cost-effective and rapid method of ensuring safety during food production, transport, and storage.
The field of predictive microbiology has matured with the development of genomic analysis and statistical methodology to encompass the modeling of ecological interactions and evolutionary dynamics, useful in predicting the potential reaction of a strain of drug-resistant bacteria towards a particular antibiotic treatment, for example.
The history of predictive microbiology
In 1937 Scott studied the rate of growth of microorganisms at different temperatures on beef, paying close attention to the rates of growth at the sides and edges where the meat would cool the most quickly. He also studied the role of moisture and CO2 concentration in the meat and surroundings, enabling the earliest shipments of non-frozen meat around the world.
In the 1960s and ’70s, buspar canadian pharmacy further studies on the role of temperature in microbial growth on various food products were performed, and a universal spoilage curve was developed as an early predictive model. In the 1980s widespread reports of various food poisonings reinvigorated interest in the use of predictive microbiology as a tool for food safety, with botulism, Salmonella, and Listeria monocytogenes posing a serious threat to consumers.
It was known at this time that microorganisms generally grow through lag, exponential, stationary, and death phases, though researchers were beginning to appreciate the variability in phase length dependant on conditions, where growth curves more commonly follow a gamma or inverse gaussian distribution.
Additionally, deadly pathogens with low infective doses such as E. coli 0157:H7 were being recognized, and novel protocols to cause their inactivation developed. By testing food products under a variety of conditions such as differing temperature, pH, preservative concentrations, and atmospheres researchers produced models to estimate the specific growth rate, lag phase duration, time to reach a threshold cell number, and the death rate of various organisms.
As conditions become less suitable for microbial growth increasingly large variability is observed in lag time as microbes may be in any one of several stages during initial exposure, with additional variability between strains, thus limiting the accuracy of the kinetic models described above in conditions that preclude growth.
Interestingly, predictive microbiology studies have consistently noted that there is a minimum rate of growth for microorganisms, wherein poorer conditions no growth occurs at all. Thus, knowing precisely where this line is drawn allows optimal implementation of anti-microbial efforts in terms of safety, cost, and impact on food quality.
Modern predictive microbiology
Dynamic mathematical models are employed to describe ever-changing microbiological systems, with a gradually increasing number of trackable variables available to researchers. The recognition of interlinked gene networks and genetic switches that control the response of a microorganism at the turn of the millennium allowed researchers to study the fundamental basis for cellular decision-making.
Intrinsic factors such as the transcription and translation rate of particular genes, the rate of cellular metabolism, and the resulting population growth rate provide a level of genetic information on an organism in relation to extrinsic factors, such as environmental conditions and interactions with neighboring microorganisms, and the resulting genetic drift.
The expression of particular genes can be monitored by tagging the ultimate protein product of gene expression or any upstream target using fluorescent probes, or by proteomics studies that characterize the full proteome using techniques such as mass spectrometry. The metabolic activity of microorganisms can be similarly monitored, providing valuable information regarding the response to growth-permissive environments.
Metabolic and genomic studies are used to develop a predictive framework for microorganisms in various settings, for example, under the exposure of antibiotics or in poor growth conditions to uncover the mechanism of antibacterial action and describe optimum inhibitory conditions.
Typical microbial populations are heterogeneous, bearing several species and also notable genetic variation between cells of the same species. A wide variety of communal interactions have been noted between microorganisms, acting competitively or synergistically towards neighboring cells and engaging in horizontal gene transfer.
Mathematical models are increasingly developed to describe and predict such interactions, allowing previously unrecognized processes to be explained. For example, in silico modeling of strong selection pressures in microbial communities reveals a consistently enriched population of mutants from which better-adapted strains rise to dominance in a shortened time scale, as is observed in the development of drug-resistant strains of bacteria.
- Ross & McMeekin (1994) Predictive microbiology. International journal of food microbiology. https://pubmed.ncbi.nlm.nih.gov/7873329/
- Scott (1937) The growth of micro-organisms on ox muscle. II. The influence of temperature. Journal of the Council for Scientific and Industrial Research. https://publications.csiro.au/rpr/pub?list=BRO&pid=procite:ef91729c-4044-4e79-b934-ccaf12c9c271
- McMeekin et al. (2002) Predictive microbiology: towards the interface and beyond. International Journal of Food Microbiology. https://www.sciencedirect.com/science/article/pii/S0168160501006638
- Lopatkin & Collins (2020) Predictive biology: modelling, understanding and harnessing microbial complexity. Nature reviews microbiology. https://www.nature.com/articles/s41579-020-0372-5
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Last Updated: Nov 23, 2021
Michael graduated from Manchester Metropolitan University with a B.Sc. in Chemistry in 2014, where he majored in organic, inorganic, physical and analytical chemistry. He is currently completing a Ph.D. on the design and production of gold nanoparticles able to act as multimodal anticancer agents, being both drug delivery platforms and radiation dose enhancers.
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