Project description
Machine-learning approach to determine the rules of cellular transdifferentiation
Cell identity is determined by cell type-specific expression of transcription factors (TFs), while the cell’s epigenetic landscape is maintained by epigenetic regulator proteins (ERs). Increasing evidence suggests the cooperation of multiple TFs and ERs in establishing the spectrum of different cell types and cell states in the human body. The EU-funded SingleCellAI project is developing a machine-learning approach for the in silico prediction of TF/ER cocktails that can reprogramme any cell type into any other cell type, creating an algorithm of cellular transdifferentiation. The study will train a machine-learning model on large-scale CRISPR single-cell sequencing data sets. The data-driven approach will be experimentally validated in the context of the human hematopoietic system.
Objective
Cellular identity is controlled by cell type specific expression of transcription factors (TFs), and it is reflected in the cell’s epigenetic landscape maintained by epigenetic regulator proteins (ERs). Functional dissection of cellular identity has focused mainly on a small number of lineage-defining master regulators, yet there is increasing evidence that multiple TFs and ERs work together to establish and retain the vast number of different cell types and cell states in the human body. For a more quantitative understanding of cellular identity, and of the complexities of its regulation, I propose to develop a machine-learning approach for in silico prediction of TF/ER cocktails that can transdifferentiate any human cell type into any other cell type, thus defining an operational rulebook of cellular transdifferentiation. To this end, I will train a machine-learning model called generative adversarial networks (GANs) on large-scale CRISPR single-cell sequencing (CROP-seq) datasets generated in the host lab. Exploiting unique features of the deep-learning generative approach, the resulting model will be able to generalize the learned genetic perturbations across cell types in silico. I will experimentally validate several of these predicted TF/ER transdifferentiation cocktails in the context of the human hematopoietic system. Importantly, the proposed approach is hypothesis-free and data-driven, exploiting recent advances in machine learning to infer fundamental aspects of the regulation of cellular identity from high-throughput functional CRISPR single-cell sequencing data.
Fields of science (EuroSciVoc)
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.
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.
- natural sciences biological sciences biochemistry biomolecules proteins
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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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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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
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.
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
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.
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.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2018
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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.
1090 Wien
Austria
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.