Integrating mathematical modeling and data-driven methods
for biological and medical sciences
Granada, June 15-18 2026
Organized by Tomás Alarcón, Juan Calvo, Martina Conte, David Poyato and Juan Soler
The twenty-second edition of the Biomat International Summer School focuses on the interaction between quantitative model formulation and experimental data analysis in Biology and Medicine. Usually predictive models are formulated in such a way that they depend on a number of parameters, some of which may be unknown. Finding those parameter values that make the constructed model yield predictions as consistent as possible with known observations is a very important aspect of the modeling process. This aspect of the modeling process is closely related to the field of inverse problems, where great advances are currently being made, especially in situations where little data is available or, on the contrary, where a very large amount of data is at hand. We also mention that, during the modeling process, it is important to determine which parameters are the most relevant. This enables, among other things, to reduce the starting model to simpler versions of it with similar predictive power. At the same time, recent developments in the field of AI provide a totally different approach for model building: given a sufficiently large dataset, the equations themselves might be inferred right from the very data, with no need of a priori assumptions. Combining these approaches with the standard ones in modeling is also worthwhile. The current state of affairs in biosciences is one in which it is becoming more and more common to have a good supply of data along with huge computing power, which is changing the way we devise, implement and use predictive models. The aim of this summer school is to gather renowned experts in the aforementioned fields together with students who are competent in quantitative techniques, in order to foster interactions that can promote advancements both in the modeling process and in its applications to biosciences.
David Basanta (Moffitt Cancer Center): Mathematical Oncology and Cancer Evolution.
Helen Byrne & Joshua Bull (U. Oxford): Mechanistic Models and Spatial Data in Biomedicine.
Andrzej Mizera (U. Warsaw): Towards Scalable Control of Boolean Models of Complex Biological Networks.
Rusell Rockne (City of Hope Cancer Center): Using LLMs to accelerate mathematical modeling in cancer research.
Bernadette Stolz (Max Planck Institue of Biochemistry): Bridging Data and Models: Topological Data Analysis for Biomedical Applications.
Sergio Branciamore (City of Hope Cancer Center): Scalable Temporal Network Dynamics Through Interpretable AI.
Núria Folguera (AstraZeneca): The perfect 'prescription': mathematical modelling and clinical trial data for drug approval.
Guillermo Lorenzo (U. A Coruña): From longitudinal multimodal data to personalized spatiotemporal forecasting of tumor growth and treatment response.
Francesco Albanese (Politecnico di Torino): Impact of Hypoxia on Radiotherapy Fractionation: A Phenotype-Structured PDE Approach (YR1)
Lorenzo Huang (Swinburne & Politecnico di Torino): Constrained Motion of T-Cells: Statistical Characterisation and Model Evaluation (YR1)
Francesco Maria Mori (U. Parma): A Nonlocal Kinetic Model for Density-Dependent Cell Migration: Stability and Pattern Formation (YR2)
Rimpa Pal (Heidelberg U.): PDE constrained tumor growth modeling with Kalman filter data assimilation (YR2)
Amaia Vielba (CRM): Near-Extinction Tumour Oscillations from Asymmetric Drug Effects in Transgenic Oncolytic Virotherapy: a modelling study (YR3)
Lucie Wintrowa (Charles University Prague): A Multiscale Mixture Model for Cortical Tissue Clearance (YR3)
Sara Xhahysa (TU Wien): Multiphase cross-diffusion models for tissue structures (YR4)
The deadline for application has expired.
There are some available slots for contributed talks by young researchers.
If you want to apply, state it in the registration form (see tab Registration). The outcome is due to May 8th.
Write us at kinetic@ugr.es