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Unravelling Signalling Heterogeneity using DEEP Learning and MECHANIstic Modelling

CORDIS · observation · Publication date unknown

Unravelling Signalling Heterogeneity using DEEP Learning and MECHANIstic Modelling Signalling enables cells to respond to external cues, but the inherent heterogeneity of individual cell responses, essential for multicellular organization, complicates disease treatment. Heterogeneity arises from drivers at system and molecular scales, intertwined through feedback loops, making quantitative understanding and prediction challenging. I will address this by pioneering transformative computational methods that predict phospho-signalling responses by integrating deep learning with mechanistic modelling to integrate systems and molecular scales. By using unbiased pattern recognition of deep learning models, I will learn cell states and simple phosphorylation rate laws. These will be combined with mechanistic models, integrating biological knowledge, to build simple and interpretable models that predict signalling responses from baselin

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recordType

award

status

SIGNED

region

EU

value

1499466

unit

EUR