Wenying Shou to join GEE as AMS Professor
6 August 2020
Professor Wenying Shou has been awarded an AMS Professorship from the Academy of Medical Sciences and will join Genetics, Evolution and Environment in January 2021. This is the first such award to UCL and we heartily congratulate Professor Shou and look forward to her joining us.
Wenying Shou Research Summary
We study biology using experiments and mathematical models. Experiments help constrain mathematical models, and model predictions drive new experiments. This synergy is reinforced when mathematical models are used to explore a wide variety of alternative scenarios to help reveal general principles. Examples of our research interests are outlined below.
Evolution of cooperation and cheating: Cooperators who pay a cost to produce benefits for others are threatened by cheaters who take but do not give. How has cooperation evolved and been stabilised? Using a novel, engineered yeast cooperative community, we are examining how cooperation might evolve, step-by-step, from inception. We are also investigating how community’s robustness (ability to survive perturbations) might change during evolution.
Artificial selection of microbial communities: Multispecies microbial communities often display “community functions” arising from species interactions, but interactions are difficult to decipher. To improve community function (e.g. pharmaceutical production, waste digestion), one can perform artificial selection on whole communities: Many communities are repeatedly grown and mutations allowed to arise. Communities with the highest desired function are “reproduced” where each is randomly partitioned into multiple offspring communities for the next cycle. However, previous efforts have often encountered difficulties. Using computer simulations, we have identified failure modes, and predicted effective strategies. We will experimentally test these strategies using a liquid-handling robot to select a variety of communities.
Causal inference from observational time series data: Although mechanistic knowledge in biology is generally rooted in manipulative experiments, perturbing living systems can encounter practical or ethical barriers. A parallel strategy is to infer causal knowledge by analysing observational time series data. We are developing statistical methods to diagnose and potentially resolve limitations associated with current causal inference approaches. We hope to contribute the urgently-needed rigor as the microbiome field moves from correlation to causality.
At UCL, we will be happy to explore collaborations with experimentalists interested in extracting quantitative insights from their data, and with mathematicians/physicists familiar with stochastic branching processes. We would also like to collaborate with medical scientists and engineers to tackle practical problems.