SOURCE-LINKED INTELLIGENCE

Decoding the Multi-facets of Cellular Identity from Single-cell Data

CORDIS · observation · Publication date unknown

network reconstruction, probabilistic spatial inference and spectral analysis of single-cell data, I will take a unique approach to this challenge by developing computational methodologies combining machine learning and dynamical systems approaches to: (1) tease apart multiple cellular facets encoded in single-cell data; (2) infer interactions between these facets and mechanisms shaping spatiotemporal expression across them; (3) derive generative models to sample and predict unobserved cell states and design optimal perturbations, providing an interpretable platform to study conditions leading to a physiological disruption and therapies aimed at reversing it. My research program will tackle the core challenge in the single-cell era - transforming this exponentially growing, complex data into insight and principles for the underlying biology of multicellular systems. It will advance our understanding and control of collective tissue behavior, and uncover the multiple facets of cellular identity in health and disease, and thus expected to be valuable for both basic and translational r

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recordType

award

status

SIGNED

region

EU

value

1484125

unit

EUR