10.45-11.15 Faculty club (1870-816)

Evangelia Petsalaki: 
From data towards digital twins of biological systems: Network-based approaches to study context-specific cell signalling

Cells constantly sense and respond to their environment through signalling, a process essential for survival, development, and communication. When signalling goes wrong, it can drive diseases such as cancer and metabolic disorders. Despite decades of research, most studies still describe signalling as linear pathways, overlooking the fact that it functions as complex, adaptive networks. Crucially, these networks behave differently depending on the genetic background, tissue type, or disease state. This means that static pathway maps often fail to capture how signalling truly works.

My group develops data-driven approaches to reveal how context shapes signalling networks, with the goal of understanding disease mechanisms and uncovering new therapeutic strategies. In this talk, I will present two examples. First, I will show how we used network analysis of patient data to reconstruct the progression of Metabolic Dysfunction Associated Liver Disease (MASLD), identifying biomarkers that could support more personalised diagnosis and treatment. Second, I will discuss our approach to study how tissue context rewires signalling networks downstream of cancer-driving mutations, shaping vulnerabilities and drug responses. Finally, I will outline our vision of combining experiments with computational modelling to create predictive “Digital Twins” of cellular systems, offering new ways to explore and ultimately control deregulated signalling in disease.

13.15-13.45 Faculty club (1870-816)

Anna Maria Langmüller: 

        Rapid evolution in a changing world

Evolution is often considered slow, but under strong selective pressures, populations can change surprisingly fast. Rapid evolutionary changes — from insecticide resistance and the spread of infectious diseases to transposable elements and gene drives — can occur over short timescales and have major ecological and practical consequences, making them important to study. I use statistical methods, machine learning, and large-scale simulations to analyze these fast dynamics and examine how they challenge traditional views of gradual evolution. This approach reveals the mechanisms driving rapid change and helps anticipate its effects in natural and managed populations.