Mathematical modeling and simulation of cellular systems

Group members: Nicole Radde, Vera Tahedl, Amatus Beyer, Benjamin Castellaz, Moritz Schweller, Reza Mehdizadeh and Georg Bail.

Life on our planet is fascinating and possesses astonishing complexity. How does a bacterial cell manage to adapt to varying conditions and respond reliably to external influences and signals? What processes occur in the body during the development of diseases, and how can we effectively counteract them? How do different cells in our body communicate with one another, and what happens when this communication is disrupted?

These are all exciting questions, and to answer them, it is helpful to understand living systems as complex dynamic networks of interacting components. We are primarily interested in processes and regulatory mechanisms on a very small scale. More specifically, we focus on cells that are too small to be seen by the naked eye. Our research focuses on the mathematical modeling, simulation, and analysis of intracellular processes and their relationship to phenotypic traits at larger scales.

We aim to contribute to a better, holistic understanding of these processes. Ultimately, our goal is to be able to modify and specifically influence these processes. We conduct research in close collaboration with colleagues in the life sciences. Our main areas of focus are concepts for data-driven modeling and the integration of single-cell and so-called "bulk" data (averages from multiple cells) into computational models. To achieve this, we primarily use statistical methods that allow us to consistently estimate uncertainties across all model parameters. My vision is to derive reliable predictions about the functioning of biological systems based on empirical evidence and mathematical models.

Specifically, the focus areas of our work are:

1. Data-based modeling and model-based design of cellular processes

2. (Stochastic) modeling approaches for describing heterogeneity in cell populations

3. Statistical methods for model identification and uncertainty estimation