The ongoing battle against therapy resistance in antibiotic and cancer treatments remains one of the primary challenges in biomedicine. Densely packed cellular populations, such as microbial biofilms or solid tumors, seem to be particularly resilient.Understanding how physical mechanisms and spatial heterogeneities shape resistance evolution and treatment response in these actively proliferating granular matter systems is essential for the development of novel therapeutic strategies. Our research adopts an interdisciplinary approach that combines genetically engineered microbial and cancer cell in vitro systems with mathematical modeling, agent-based simulations, and machine learning. In my presentation, I will show how collective dynamics in dense cellular populations inherently enhance drug resistance evolution across complex fitness landscapes. I will then use this framework to demonstrate the transformative potential of reinforcement learning for artificial scientific discovery and adaptive therapy optimization. Finally, I will explore how these concepts could be extended to integrate components of immunotherapy. Together, these vignettes aim to demonstrate the power of integrating predictive physical models with data-driven research approaches to unravel —and potentially guide— the evolutionary dynamics in complex living systems.

15:15: Coffee Break

15:30: Enrique Colina  (A10, AG Lautenschläger): Pretubulysin, as a microtubule destabilizing agent. A computational insight to its binding mechanisms

15:45: Dr. Mariana Romeiro Motta (A13, AG Aradilla-Zapata): The role of MAP65-mediated microtubule nucleation in plant development

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