Bacterial regulatory evolution during adaptation to chronic infection: Individuals with Cystic Fibrosis (CF) have impaired lung function that leads to life-long bacterial infections of the lung. One of the primary colonizers of the CF lung is the opportunistic pathogen Pseudomonas aeruginosa (PA). Once established, PA infections are particularly hard to clear and can undergo years and even decades of within-host evolution – often displaying parallelism across patients in evolutionary trajectories of key virulence phenotypes (e.g. reduced division rate, cell-cell signaling, toxin production; increased biofilm formation).
Effective and evolutionarily robust pathogen control: The development of evolutionarily robust strategies to control beta-lactamase and efflux resistance mechanisms is a major goal in microbiology, and would allow us a return to use of many otherwise failing antibiotics. In pilot studies, we have demonstrated that beta-lactamase inhibitors can be made more evolutionarily robust by simple dosing modifications, and that efflux-binding phages can increase antibiotic sensitivity in vivo, during antibiotic co-administration. The project goal is to perform an evolutionary risk analysis of adjuvant/combination control strategies to identify drug combinations that work – and continue to work in the face of bacterial evolution. Our overarching hypothesis is that modulating adjuvant dosing we can significantly slow the evolution of resistance to front line antibiotics.
The ecological importance of combinatorial signaling in bacterial populations: Investigating the importance and biological function of using multiple signals in bacterial communication. This work also made use of long term evolution and selection experiments in which we tried to alter the affinity for signal molecules in order to develop a response to non-cognate molecules.
Co-evolution of bacteriophage and their hosts: Investigating co-evolution in bacteriophage under a number of different environments and the implications for phage therapy. I aim to expand this work into how phage might shape pathogens during chronic infections such as the CF lung.
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Yifei received a PhD degree in Computer Science in 2016. He also hold a M.Eng. degree in Astronautics Engineering and a B.Eng. degree in Computer Science. Yifei has a strong interest in a broad area of biology and collective intelligence. Currently, his research focuses on understanding evolutionary dynamics of complex interactions in bacteria. Combining mathematical modeling and stochastic simulations of agent-based modeling approaches, he is working on building computation models to test hypotheses raise from observations in microbial experiments. He also employs cooperation and communication theories inspired from various research fields, for example, in animal or human societies to study behaviors and consequences for microbes and vice versa.