| Abstract: |
| Understanding how heterogeneous cellular populations evolve is central to uncovering the mechanisms of tissue regeneration and cancer progression. In healthy neural stem cell systems and glioblastoma, cellular hierarchies and stem-like states govern long-term dynamics, adaptation, and functional plasticity. In this talk, I will present mathematical approaches that integrate mechanistic modelling with single-cell omics data to study the evolution of cellular states in space and time. Different mathematical frameworks will be discussed, including compartmental and structured population models, as well as their generalisation to the evolution of measures, which describe the dynamics of cell populations in the high-dimensional state spaces revealed by single-cell data. These approaches provide insight into transitions between stemness and differentiation and into how cellular hierarchies emerge and evolve in regeneration and cancer. The work highlights the potential of combining single-cell data with mechanistic mathematical modelling to uncover principles governing heterogeneous cellular systems. |
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