Probabilistic Model of Microbial Cell Growth, Division, and Mortality

摘要

After a short time interval of length δ t during microbial growth, an individual cell can be found to be divided with probability P_(d) ( t )δ t , dead with probability P_(m) ( t )δ t , or alive but undivided with the probability 1 ? [ P_(d) ( t ) + P_(m) ( t )]δ t , where t is time, P_(d) ( t ) expresses the probability of division for an individual cell per unit of time, and P_(m) ( t ) expresses the probability of mortality per unit of time. These probabilities may change with the state of the population and the habitat's properties and are therefore functions of time. This scenario translates into a model that is presented in stochastic and deterministic versions. The first, a stochastic process model, monitors the fates of individual cells and determines cell numbers. It is particularly suitable for small populations such as those that may exist in the case of casual contamination of a food by a pathogen. The second, which can be regarded as a large-population limit of the stochastic model, is a continuous mathematical expression that describes the population's size as a function of time. It is suitable for large microbial populations such as those present in unprocessed foods. Exponential or logistic growth with or without lag, inactivation with or without a “shoulder,” and transitions between growth and inactivation are all manifestations of the underlying probability structure of the model. With temperature-dependent parameters, the model can be used to simulate nonisothermal growth and inactivation patterns. The same concept applies to other factors that promote or inhibit microorganisms, such as pH and the presence of antimicrobials, etc. With P_(d) ( t ) and P_(m) ( t ) in the form of logistic functions, the model can simulate all commonly observed growth/mortality patterns. Estimates of the changing probability parameters can be obtained with both the stochastic and deterministic versions of the model, as demonstrated with simulated data.