Biological systems rely on complex interaction networks at various scales. Using published datasets or working in collaboration with experimentalists, we are building models to understand emergent phenomena in biology as well as predict their temporal evolution. We are particularly interested by decision making in early embryogenesis and in building predictive dynamical models for microbial communities. All our projects rely on the theory of non-linear dynamics, statistical learning and statistical physics techniques.
- Embryonic development: We work in collaboration with the lab of H. Yasuo and with Geneviève Dupont and Aleksandra Walczak to study the embryonic development of ascidians which are model organisms for vertebrate development. How do cells take decision about their fate? What are the molecular mechanisms underlying this decision? To answer those questions and understand reproducibility in the process, we are focusing on neural induction in early embryonic development. More recently, we started working on information theory in embryogenesis.
- Engineering genetic circuits for synthetic biology: In the long term, we aim to design bottom-up microbial cell factories for the production of bioplastics, in collaboration with Eveline Peeters and Wim Vranken. In the short term, we aim to 1) develop and optimise metabolite-responsive biosensor modules and 2) rationally design synthetic regulatory circuits to improve the performance of production pathways in E. coli - the most studied prokaryotic organism.
- Dynamical modeling of microbial communities: We aim at building dynamical models for human-associated microbial communities. We are interested in mechanisms leading to community structure and in predicting responses to perturbations, such as antibiotic treatments.
Papers
Géraldine Williaume, Sophie de Buyl, Cathy Sirour, Nicolas Haupaix, Kaoru Imai, Yutaka Satou, Geneviève Dupont, Rossana Bettoni, Clare Hudson & Hitoyoshi Yasuo
