Modelling microbial communities in time

In most environments, microorganisms coexist within complex communities, interacting with each other and their environment. These communities play fundamental roles in ecological and physiological processes, such as the degradation of organic matter or the health of humans and animals. Predicting the trajectories of microorganisms over time is crucial for understanding community function. We aim to understand the interactions between the members of the microbial communities, their dynamics, and forecast the microbial community trajectory in time. We use mathematical frameworks, including generalized Lotka-Volterra models, as well as developing deep neural networks to forecast the trajectory of the communities. We utilize hundreds of real-world longitudinally sampled microbiome datasets to develop techniques, tools, and ultimately enhance our understanding of the microbial communities and their inherent dynamics.

PhD start: 09/01/2022