1. Develop predictive models that quantify growth kinetics and/or survival behavior of high priority pathogens (including but not limited to Shiga-toxin producing Escherichia coli, Listeria monocytogenes, Salmonella spp., Clostridium perfringens, and Staphylococcus aureus) in foods or food systems. This includes development of predictive models that quantify growth and/or inactivation kinetics of pathogens in food systems during heating and cooling. 1A: Pathogen Behavior in RTE Foods, Liquid Egg Products, and Produce - Measure and model pathogen growth in RTE foods, liquid egg products, and pre-packaged produce as a function of intrinsic and extrinsic factors, pathogen strain and physiological state, and natural background microflora. 1B: Thermal Inactivation Studies - Define combinations of intrinsic and extrinsic factors that delineate minimum heat treatments for pathogen lethality. 1C: Time-Temperature Conditions for Cooling Cooked Meat - Evaluate excessive time in cooling of heated meat and poultry products supplemented with additives to determine if the product remains safe.
2. Develop methods for application in predictive microbiology that are allied to Objective 1. For example: computer simulation of bacterial growth and inactivation under dynamic conditions, and simulation of the growth, inactivation and survival of foodborne pathogens in the presence of competing background flora.
3. Extend technology transfer through the expansion and continued maintenance of the Pathogen Modeling Program (PMP) and the Predictive Microbiology Information Portal (PMIP). Develop a computational framework to make the PMP compatible with Combase, and continue to support the development of ComBase with our associated partners (the Institute of Food Research [IFR] and the University of Tasmania [UTAS]) as an international data resource.

Both static and dynamic temperature models will be developed. Developed models will be validated against data sets not used in model development and data set obtained from ComBase and published literatures. The underlying mathematics of each predictive model will be implemented in the ARS Pathogen Modeling Program. Raw data will be added to ComBase. The project will also collaborate with the IFR and the UTAS to further develop the Combase on improving its interface, functionality, and compatibility with PMP.