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Granada, Spain
February-May 2021

A course organized in a series of weekly seminars on current topics of mathematical modeling in developmental biology, biophysics, biomechanics and biomedicine aimed at researchers in training and the entire scientific community.

List of extended 1-hour seminar starting at 16:30 hours (EU time). They are open seminars but require prior registration.

Coordinated by: Juan Calvo, David Poyato and Juan Soler


For any contact about this activity:


February 3

José Antonio Carrillo, University of Oxford, UK      

Aggregation-Diffusion models for differential adhesion and applications

February 10

Nicola Bellomo, University of Granada, Spain    

Mathematical Models for Covid-19 Pandemics in a Globally Connected World

February 17

Alex Mogilner, Courant Institute, New York, USA    

Mathematics of Mitotic Spindle

February 24

Anna Marciniak-Czochra, Heidelberg University, Germany    

Turing and non-Turing aspects of developmental pattern formation: New insights from mathematical modeling

March 3

Benoît Ladoux , Jacques Monod Institute, CNRS & University of Paris, France          

Active nematic behaviours of cellular monolayers.

March 10

Thomas Hillen, University of Alberta, Canada        

Mathematical Modelling of Metastasis

March 17         

Andreas Deutsch, Technical University of Dresden, Germany

Collective effects in cancer invasion and progression

March 24

Philip Maini, University of Oxford, UK

Modelling collective cell movement in biology and medicine

April 14     

Christian Schmeiser, University of Vienna

Heterogeneous protein aggregates - p62-ubiquitin aggregation in cellular autophagy

April 21 

Eitan Tadmor, University of Maryland

On the emergent behaviour of swarming dynamics

April 28   

Henri Berrestycki, EHESS, Paris

Reaction-diffusion systems in epidemiology

May 5     

Martina Conte, University of Granada

Cell dynamics in glioblastoma invasion and progression

May 12

Lorenzo Pareschi, University of Ferrara

Spatial spread of epidemics under uncertain data

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