SOURCE-LINKED INTELLIGENCE

Artificial intelligence for synthetic functional genomics of blood

CORDIS · observation · Publication date unknown

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 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 activ

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1499653

unit

EUR