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Unravelling Signalling Heterogeneity using DEEP Learning and MECHANIstic Modelling

Project description

Advanced computational approaches to investigate cell signalling

Signalling enables cells to respond to external cues, but varying responses across cells complicate disease treatment. This variability, attributed to complex drivers at both system and molecular levels, makes understanding and predicting responses challenging. To address this, the ERC-funded DeepMechanism project proposes innovative computational methods that combine deep learning and mechanistic modelling. These methods aim to predict signalling responses by analysing cell states and phosphorylation processes, while integrating biological knowledge for simpler models. The approach investigates the drivers of heterogeneity in receptor tyrosine kinase and rat sarcoma signalling in cancer, with potential applications in patient-derived organoids. The proposed research could significantly enhance understanding of signalling regulation and has the potential for broad applications in biological and other systems.

Objective

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 baseline omics
profiles across distinct time-resolved and perturbational conditions. I will apply these methods to investigate
drivers of heterogeneity in receptor tyrosine kinase (RTK) and rat sarcoma (RAS) signalling, in response to
growth factors and targeted inhibitors in cancer cell lines. I will validate the approach by reprogramming
patient-derived organoids using model-proposed inhibitor combinations.

The proposed research will advance our fundamental understanding of signalling regulation and co-regulation
with cellular states. Given the vital role of RTK and RAS signalling in human health, it also holds the potential
for translational impact. More broadly, the proposed computational methods are versatile and could be applied
to a broad range of biological and non-biological systems.

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

THE FRANCIS CRICK INSTITUTE LIMITED
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 499 466,00
Address
1 MIDLAND ROAD
NW1 1AT London
United Kingdom

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Region
London Inner London — West Camden and City of London
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 499 466,00

Beneficiaries (1)

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