Research

We combine mathematical models (bottom-up approach) and metagenomic sequencing (top-down approach) to help understand and predict microbial interactions mediated by metabolism, in natural and engineered microbial communities.

How did metabolism, an organized network of chemical reactions capable of collective self-reproduction, emerge and evolve on our planet? What can we learn about the evolutionary history of metabolism by looking at metabolic networks in present-day organisms? We address these questions using different methods, including detailed stoichiometric models of cellular metabolism, network expansion algorithms that mimic the growth of biochemistry from initial molecular "seeds", and artificial chemistry models that allow us to explore alternative hypothetical universes, and search for general principles of metabolic complexity. Furthermore, we use the concept of epistasis to understand how, in biological networks, the whole is different than the sum of its parts.