About

CAFE-BIO is a Marie Skłodowska-Curie Doctoral Network funded by the European Union where 15 Doctoral Candidates will conduct PhD research projects in pursuit of a Coherent Analysis Framework for Emergence in Biological systems. Doctoral Candidates will, through these research and other training activities, gain a rigorous grounding in solving with complex problems in the context of emergence in biological systems, combining approaches from many scientific traditions.

Understanding complex problems in biological systems

A key challenge is understanding dense, many-body systems with complex interactions under noisy, non-equilibrium biological conditions. A unified framework is needed to integrate approaches from statistical physics. Supported by the Marie Skłodowska-Curie Actions programme, the CAFE-BIO project will provide training aimed at addressing problems in biological systems by integrating methods from diverse scientific fields. The focus will be on PhD research projects that incorporate this knowledge in innovative ways. Additionally, the project will deepen understanding of the physical interactions within biological systems, establish quantitative links between microscopic and macroscopic effects, and address real-world complexities through data-driven modelling and experiments. The findings will have critical applications in cancer growth, cardiac health, biofilm formation, and animal fertility.

Building the framework

A coherent framework for analysing emergence in biological systems, grounded in the principles of statistical physics, demands an integration of multiple approaches that are currently applied in isolation and in a fragmented fashion. This network will build this coherence, by bringing together multiple partners with substantial complementary expertise, and through PhD research projects that are each designed to combine this expertise in fundamentally new ways. Through this, we will gain new understanding of the novel physical interactions that biological systems create, establish quantitative relationships between microscopic interactions and macroscopic collective effects, and confront the complexity of real-world systems through data-driven modelling and experimental applications.