August 22 - 26, 2022

Lipschitz-Saal (Endenicher Allee 60, Bonn)

Organizers: Lorenzo Contento, Jan Hasenauer, Yannik Schälte

Description: The key aim of mathematical, computational and systems biology is to achieve a holistic understanding of biological systems. Over decades, this aim was mainly approached by moving from the study of single molecules to the analysis of biological networks. However, it has been recognised that the study of cellular networks is only the first step, as multicellular organisms are more than just a collection of pathways. A mechanistic understanding of complex biological functions requires the consideration of multiple spatial and temporal scales. Multi-scale models for a variety of processes have been developed, such as the beating of the heart, the development of cancer, and drug delivery and action. A single multi-scale model usually requires the application of multiple mathematical techniques. For example, stochastic descriptions of individual cells can be coupled with continuous fields for extracellular substances, thereby connecting branching processes with partial differential equations.

In this Hausdorff School we will focus on the estimation of the parameters of multi-scale models from experimental data. The inverse problem of determining parameter values from experimental data is mathematically and computationally challenging. We will introduce the participants to advanced likelihood-based and -free statistical inference methods, including state-of-the-art Approximate Bayesian Computing (ABC) methods. Furthermore, novel strategies for the reduction of the computational complexity will be discussed, including multi-resolution and surrogate modelling.

Key Speakers:

• Brian Drawert (Asheville)
  
• Stefan Engblom (Uppsala)
  
• Christiane Fuchs (Bielefeld)
  
• Andreas Hellender (Uppsala)
  
• Linda Petzold (Santa Barbara)
• Dennis Prangle (Bristol)