MICROBIAL MODELING AND BIOINFORMATICS FOR FOOD SAFETY AND SECURITY

To evaluate, validate, and where necessary, develop new innovative, robust and valid predictive models for the responses of microbial pathogens, including foodborne threat agents, in select food matrices, as a function of: temperature, food formulation, competitive microflora, physiological history, and surface transfer. To develop novel approaches to assess model performance and robustness, leading to more efficient strategies for producing and extrapolating models to different classes of food. To determine the probability distribution of lag phase duration (LPD) for foodborne pathogens, as a function of the previous bacterial physiological history, to allow risk managers to estimate worst-and best-case scenarios for pathogen behavior, depending on likely sources of contamination; To identify molecular markers that discriminate bacterial lag, growth and stationary phases, thus leading to more mechanistic models and greater certainty for LPD prediction.