Optimizing production of transplantable neural cells
This interdisciplinary project integrates systems biology and machine learning with experimental neuroscience to enhance the production of specific neural cell types for therapeutic applications. In collaboration with the Ottosson lab at Lund University, a unique computational framework will be developed using single-cell experimental data to optimize the generation of Parvalbumin and Somatostatin neurons from glial cells and stem cells (ref. Stamouli). Machine learning models will identify key factors driving cell transitions, while quantitative mechanistic models will be embedded into multi-scale 3D organoid simulations. This approach will not only uncover the mechanisms regulating cell conversions but also provide predictive capabilities, offering groundbreaking insights into direct reprogramming and differentiation.
Selected publications
Project participants
