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AI4SYN

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Artificial intelligence for synthetic functional genomics of blood

Our abilities to predict and engineer complex biological systems are in their infancy. In the context of gene regulation, we cannot design artificial promoters with specificity to arbitrary cell states, and we cannot arbitrarily t... ver más

31/08/2027

FUNDACIO PRIVADA C...

1M€

Presupuesto del proyecto: 1M€

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FUNDACIO PRIVADA CENTRE DE REGULACIO GENOMICA Otras actividades deportivas asociacion

TRL 4-5 | 50K€

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-07-18

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2021-STG: ERC STARTING GRANTS

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 61 meses Fecha Inicio: 2022-07-18
Fecha Fin: 2027-08-31

Líder del proyecto

FUNDACIO PRIVADA CENTRE DE REGULACIO GENOMICA

TRL 4-5 | 50K€

Presupuesto del proyecto 1M€

Descripción del proyecto Our abilities to predict and engineer complex biological systems are in their infancy. In the context of gene regulation, we cannot design artificial promoters with specificity to arbitrary cell states, and we cannot arbitrarily trans- and de-differentiate somatic cells, although such abilities would be of high biotechnological and biomedical value. To achieve these ambitious goals, we require quantitative, predictive models of gene regulatory elements (GREs) and gene regulatory networks (GRNs), respectively. Here I propose that the combination of deep learning and single-cell genetic screens is ideally suited to obtain such models, and, in particular, the amounts of highly informative data required for their training. Working with an ex vivo model of hematopoietic stem cell differentiation, we will first screen the activity of hundreds of thousands of synthetic GREs throughout the hematopoietic differentiation landscape. Thereby, we will obtain models of cell type specific GRE activity that can predict new, synthetic GREs with activity in any cell state of interest. Second, we will screen hundreds of thousands of combinatorial GRN perturbations and their effect on cell state. Thereby, we will derive models of GRNs that can predict combinatorial perturbation strategies to achieve arbitrary de- or trans-differentiation events. In sum, work on this project will yield a quantitative model of gene regulation in hematopoiesis at various scales of complexity while introducing a novel, AI-guided concept for biological engineering.

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