Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101163005 |
| Start date: | 01-10-2024 |
| End date: | 30-09-2029 |
| Total budget - Public funding: | 1 499 466,00 Euro - 1 499 466,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2024-STGUpdate Date
26-11-2024
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