Developing Computational Tools to Identify Critical Control Points for Mitigating the Spread of Antibiotic Resistance in Agro-ecosystems

The overall goals are to 1) develop publicly-accessible computational tools for identifying CCPs that most effectively mitigate dissemination of antibiotic resistance in agro-ecosystems and 2) engage in education and outreach efforts to advance the use of metagenomics and computational modeling to inform policy and decision-making for mitigating the spread of antibiotic resistance in agro-ecosystems.The specific objectives are to:Conduct an integrated meta-analysis of the response of the antibiotic resistome to various agricultural management practices through cross-comparison of multiple data sets;Construct computational models of the antibiotic resistome through candidate CCPs in manure-affected agro-ecosystems;Build a computational system that allows users such as veterinarians and farm managers to predict the potential for management practices to mitigate the spread of antibiotic resistance as a function of local constraints, such as soil and livestock type;Conduct a field-scale experiment to compare observed and predicted resistomes and inform model validation;Develop and implement educational and outreach activities and resources focused on mitigation of agricultural sources of antibiotic resistance for undergraduate and graduate students and disseminate to relevant stakeholders.