Sophie De Buyl

Biological systems rely on complex interaction networks at various scales. Using published datasets or working in collaboration with experimentalists, the team of Sophie De Buyl is building models to understand emergent phenomena in biology and predict their temporal evolution. She is particularly interested in decision making during early embryogenesis and in building predictive dynamic models for microbial communities. All projects rely on the theory of non-linear dynamics, statistical learning and statistical physics techniques.

• Williaume, G., de Buyl, S., Sirour, C., Haupaix, N., Bettoni, R., Imai, K. S., Satou, Y., Dupont, G., Hudson, C., & Yasuo, H. (2021). Cell geometry, signal dampening, and a bimodal transcriptional response underlie the spatial precision of an ERK-mediated embryonic induction. Developmental Cell, 56(21). https://doi.org/10.1016/j.devcel.2021.09.025
• Descheemaeker, L., & de Buyl, S. (2020). Stochastic logistic models reproduce experimental time series of microbial communities. ELife, 9. https://doi.org/10.7554/eLife.55650
• Teng, S. W., Mukherji, S., Moffitt, J. R., de Buyl, S., & O’Shea, E. K. (2013). Robust circadian oscillations in growing cyanobacteria require transcriptional feedback. Science, 340(6133). https://doi.org/10.1126/science.1230996