BWRC and LEADERS Virtual Seminar Series – Laurence Yang

Sophie Felleiter

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The presence of antibiotic resistant bacteria in aquatic environments constitutes a public health issue. The mechanisms by which antibiotic tolerance is induced remain poorly understood in these environments. However, microbial stress responses are observed in contexts ranging from skin infection to treated wastewater and appear to play a key role for inducing antibiotic tolerance. We have developed a modeling framework that can predict microbial response to thermal, oxidative, and acid stresses. The models accurately compute how E. coli changes gene expression and metabolic states in response to these stresses, together with variations in nutrient availability. Furthermore, these models explain the molecular mechanisms underlying improved fitness observed for several microbes that were adaptively evolved under oxidative stress. We have extended the framework to enable complex simulations spanning intracellular reaction networks, microbial communities, and spatiotemporal transport in biofilms. We also dive deeper into the molecular mechanisms, computing properties of individual proteins from structure. This systems biology modeling suite can help predict microbial responses to emerging contaminants in water, and potentially be used to devise strategies for protecting water quality.


Dr. Laurence Yang is an Assistant Professor in Chemical Engineering at Queen’s and Queen’s National Scholar in Systems Biology. He earned a BSc, MASc and PhD all in Chemical Engineering from the University of Toronto. Prior to his arrival to Queen’s in 2019, he was an Assistant Project Scientist in the Department of Bioengineering at University of California San Diego, where he also completed a postdoctoral fellowship. Dr. Yang’s lab develops predictive models of cell metabolism, protein expression, and gene regulation. Specifically, they develop holistic models that integrate multiple biological processes and large-scale networks. Interpreting biological data in the context of these integrated models provides a systems-level perspective on cellular functions.


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