Mathematical modeling of Listeria overgrowth phenomenon
The standard method to test for Listeria n7onocj'logenes in foods is prone to false negatives if also non-pathogenic members of the Listeria genus are present. These can overgrow L.monocylogenes during the selective enrichment process and mask the presence of the pathogen. This "overgrowth'" phenomenon is not well understood, partially because the mathematical models that are used in food microbiology to determine growth kinetics of bacteria are not able to capture interaction between species correctly. We will develop improved models that can shed light on this phenomenon and, therefore, lead to more reliable food pathogen detection methods. They will be formulated and studied using dynamic systems techniques and methods for inverse and ill-posed problems. A successful completion of this project can reduce the economic losses of the food industry associated with product recalls and sales decrease, and enhance the ability of regulatory agencies to identify this pathogen in foods.