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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-STG
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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.
NW1 1AT London
United Kingdom
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