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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... see more

31/08/2027

FUNDACIO PRIVADA C...

1M€

Project Budget: 1M€

Project leader

FUNDACIO PRIVADA CENTRE DE REGULACIO GENOMICA Otras actividades deportivas asociacion

TRL 4-5 | 100K€

PARTICIPATION DEPralty Sin fecha límite de participación.

Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2022-07-18

ERC-2021-STG: ERC STARTING GRANTS Scope:Objectives

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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1 Participantes

FUNDACIO PRIVADA CENTRE DE R...

1.50M€ | Leader

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Project duration: 61 months Date Start: 2022-07-18
End date: 2027-08-31

Project leader

FUNDACIO PRIVADA CENTRE DE REGULACIO GENOMICA Otras actividades deportivas asociacion

TRL 4-5 | 100K€

Project Budget 1M€

participation deadline Sin fecha límite de participación.

Project description 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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