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Mathematical Modelling in Biosciences

Nature is a book written in the language of mathematics.

(G. Galilei, Il Saggiatore, 1632)

The importance of mathematics in understanding nature is one of the oldest concepts agreed upon in science. The development of accurate mathematical and (more recently) stochastic models plays a crucial role in studying the behavior of complex systems in Life Sciences, where different scales are at play at the same time. On the other hand, the widespread availability of more powerful computers render numerical simulations a relevant tool in assessing the main features of a system, with the aim of realizing reliable predictions about the future dynamics of the system itself. In addition, the large amount and enhanced quality of data available in the recent years makes it possible to fit more sophisticated models, allowing to significantly reduce the gap between complex mathematical models, based on scientific theory, and statistical estimation.

In this scenario, the objective of the group is building bridges between mathematics and other Life Science disciplines. In particular, we are interseted in both applications and methodology, in order to devise reliable predictive tools for biomedical applications, disease ecology, and conservation biology.

Goal
Building bridges between mathematics and other Life Science disciplines, particularly in terms of biomedical applications, disease ecology, and conservation biology.
Method
Partial differential equations
Finite elements
Reaction-diffusion models
Data assimilation and Bayesian statistics
Stochastic partial differential equations
Eusko Jaurlaritza - Gobierno Vasco ikerbasque - Basque Foundation for Science Bizkaia xede. Bizkaiko Foru Aldundia innobasque - Agencia vasca de la innovación Universidad del PaÌs Vasco (UPV/EHU)