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Incorporating molecular data into a quantitative microbial risk assessment framework for Salmonella in chicken


The overall goal of this project is to develop novel methods and tools to utilize high-resolution genomic data in a quantitative microbial risk assessment framework to improve the prediction of public health risk posed by foodborne pathogens. In this project, the persistent pathogen-food pair of Salmonella in chicken will be used as an example to demonstrate the use of advanced bioinformatics and computational methods to translate genomic data into a risk assessment framework. Specifically, we will use machine learning, computational methods, and the latest experimental techniques to achieve the following objectives.Objective 1: Develop a tool incorporating bioinformatics methods and machine learning models to predict bacterial pathogenicity phenotypes from whole genome sequencing (WGS) data to bridge the gap between genes and their functional derivatives for Salmonella in chicken as a model pathogen-food pair.Objective 2: Develop pathogenicity, survival, and antimicrobial resistance profiles for Salmonella using the predicted phenotypic behavior, and utilize these to modulate estimates for bacterial growth, survival, and dose-response relationship.Objective 3: Bridge knowledge gaps in traditional risk assessment pertaining to Salmonella growth and survival kinetics by re-evaluating growth and survival under simulated abuse conditions, based on laboratory-derived and predictive modeling data.Objective 4: Develop an innovative quantitative microbial risk assessment (QMRA) framework by integrating the estimates obtained from analyzing WGS data and survival kinetics, and compare against a traditional risk assessment for Salmonella in chicken, in order to determine the differences attributable to different pathogenicity profiles.

Pradhan, Abani
University of Iceland
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