Mathematics in Agronomy

Mathematics in Immunology

Mathematics in Epidemiology

Mathematics in Neuroscience

Our Research Topics

Mathematical Modelling
 

Mathematical Modelling

Our team focuses on applying mathematical expertise for the study of biological systems. At the forefront of this activity lies mathematical modelling, which consists in building mathematical models allowing to emulate the behavior of biological systems of interest. A good model, while being a simplified version of reality, captures the essential ingredients driving the system’s behaviour and allows identifying which physical mechanisms and/or chemical processes play a critical role. It additionally allows conducting virtual experiments through model simulations and provides quantitative predictions regarding the system’s functioning.
Mathematical Analysis
 

Mathematical Analysis

Once a model has been built, the next step is to study its mathematical properties. Depending on the type of model under consideration, several questions can arise, ranging from model identifiability to the existence of a solution for a system of partial differential equations (PDE) or to uncertainty and sensitivity analysis.
Statistical Inference
 

Statistical Inference

statistical inference is of primary importance given the potentially relatively high number of parameters involved in the models our team is dealing with. If model parameters are badly calibrated, even though the mathematical description of the underlying physical phenomena might be decent, the model output will not be relevant, which is crucial in order to make predictions or data assimilation. It is therefore necessary to estimate likely values for these parameters.