Experimental approaches
We combine quantitative experiments with computational modeling and data-driven analysis, and collaborate widely within SILS and beyond.
Our experimental approaches include chemostat evolution experiments, high-throughput phenotyping, and quantitative measurements of growth and metabolism across different environmental conditions.
Data-driven analysis
We use computational modeling to understand the constraints and trade-offs that shape microbial physiology and evolution. This includes metabolic modeling, evolutionary simulations, and data-driven analysis of large-scale experimental datasets.
We integrate theory with experiments to develop predictive models of microbial behavior, from single cells to communities.