Therapy resistance remains a significant challenge in modern healthcare and is intricately linked to evolutionary processes. However, how the collective dynamics in spatially structured populations, such as solid tumors or microbial biofilms, impacts the emergence of treatment resistance and therapy response remains unclear.

Our research aims to fill this gap by combining empirical insights from tailored in vitro tumor models with physics-based computer simulations. Our goal is to understand resistance evolution and treatment failure dynamics as emergent phenomena within a wider conceptual framework of actively proliferating granular matter.

Using a diverse array of tools, ranging from CRISPR-based gene editing and population-scale 4D imaging to Brillouin microscopy and real-time deformability cytometry (RT-DC), gives us unprecedented multimodal access. We use this data to develop a quantitative understanding of the factors shaping the multicellular dynamics within a tumor under therapy.

Building on the insights gained, we aim to enhance evolution-based therapies via advanced machine learning techniques, such as reinforcement learning and generative AI.

A particularly intriguing avenue is the combination of targeted therapy and immunotherapy in an adaptively alternating manner. In this context, we aim to decipher the mechanical heterogeneities within the cancer and immune cell populations, as well as the tumor microenvironment, and how their modulation can be exploited for improved therapy efficacy.

Our multidisciplinary approach aims to bridge the gap between physics-inspired fundamental research and biomedical applications to facilitate an insight-driven decision process in clinics.