Project lead: Dr. Klas Udekwu

The human gut microbiome is structured by a complex network of nutrient use and exchange, yet therapeutic efforts to alter the gut flora often neglect these interactions. The frequent failure of purely empirical remedies, such as probiotic remedies via fecal microbiome transplantation (FMT), highlights how little we currently understand about transitioning to and from dysbiotic and healthy states of the community. Even current usage of antibiotics to destabilize community structure while at times effective, remain empirical and a rational approach to designing both FMT and antibiotic pre-treatment is desirable. The overarching goal of this project is the rational design and application of pre-treatment protocols for FMT regimen using a combined theoretical and experimental approach while minimizing failure risk and extensive collateral effects on beneficial bacteria.  The central hypothesis is that short-term exposure to antibiotics, singly or in combination can be used to destabilize gut microbial communities, specifically enabling successful manipulation with ‘therapeutic’ microbes via FMT or probiotics. To succeed in this objective, we will establish a theoretically and experimentally testable and tractable mock bacterial community by accomplishing four sub-aims: (1) We will establish a mock bacterial community by defining the environmental and resource utilization preference of four representative groups of similarly metabolizing bacteria. i.e. guilds. (2) We will define the conditions of interaction between all combinations of microbes in terms of ecological synergy or antagonism between groups. (3) We

This data-driven multiscale model will predict the effect of host influenza infection on contact-dependent transmission in domestic animals. The expected outcome is to provide computational evidence toward a comprehensive understanding of host infection and immunity to contact-dependent transmission. The project brings new features to multiscale modeling that will improve our technical capabilities to integrate data of different scales in biology and beyond.