Project description
First comprehensive framework for multi-omics trajectory inference
Nature excels at optimising processes, including how cells develop via dynamic gene networks and the translocation of molecules. Understanding these processes is a monumental task. Optimal transport theory, a mathematical formalisation developed nearly 250 years ago to study the optimal transportation and allocation of resources, offers a promising avenue. Applying optimal transport flows – treating cells as particles evolving on an energy landscape within an ‘omics’ space – could shed light on cell development. However, current approaches ignore biological relationships between genes. The ERC-funded WOLF project aims to address this problem by incorporating complex genetic relations and fusing multiple omics datasets, developing new types of non-Euclidean and multi-linear optimal transport flows.
Objective
"Single cell molecular profiling allows to map cellular development at an unprecedented level of detail. Optimal transport (OT) enables the analysis of this dynamical process as a trajectory inference problem, using OT flows. These flows treat cells as particles evolving on an energy landscape over an ""omics'' space (such as transcriptomic, epigenomic, proteomic and location). Learning this model from large scale omics datasets poses however formidable mathematical and computational challenges, which will be tackled by WOLF. The first one is the joint learning of both the gene embedding space and the energy landscape. Existing approaches use ad-hoc Euclidean embeddings, ignoring biological relationships between genes. WOLF will develop a new type of non-Euclidean OT flows, which takes into account complex genetic relations. The second challenge is the fusion of multiple omics dataset (for instance transcriptomics, proteomic and space) without having access to an explicit pairing between the cells across the omics. Multi-omics is the next frontier in developmental analysis, and the corresponding trajectories cannot be captured with existing OT flows. WOLF will develop a new class of multi-linear OT flows where interaction terms couple particles together across different omics. These advances will be integrated in an efficient computational package where the parameters of the models are learned using parallelizable OT flow solvers. Leveraging the connexion between OT flows and attention mechanisms in deep learning, these methods will be approximated using transformers architectures and optimized using implicit differentiation. These theoretical and numerical contributions will work hand in hand to offer the first comprehensive framework for multi-omics trajectory inference. This will unlock biological findings for the characterization of developmental molecular pathways and the understanding of disease mechanisms."
Keywords
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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-2023-ADG
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75794 PARIS
France
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