SOURCE-LINKED INTELLIGENCE

Unraveling the regulatory code of gene expression in plants

CORDIS · observation · Publication date unknown

cific gene expression challenging. This is a major obstacle towards engineering plant promoters with predictable and tuneable expression. multiCODE’s aim is uniting single-cell genomics, explainable artificial intelligence (xAI), and synthetic promoter engineering, to efficiently learn and validate regulatory sequences controlling gene expression in plants. Based on high-resolution single-cell gene expression profiling in the model Arabidopsis and the crop Brassica rapa, xAI models will be built to predict gene expression in leaves under control and stress conditions while at the same time identifying the underlying regulatory DNA sequences and syntax. The power of these predictive models will be validated by exploiting inter- and intra-species sequence variation, revealing the evolutionary conservation of the regulatory code. Furthermore, a novel multi-tier synthetic promoter engineering approach will be used to experimentally validate, directly in plants, the learned regulatory code in a high-throughput manner. By combining the power of advanced computational and experimental me

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recordType

award

status

SIGNED

region

EU

value

2498937

unit

EUR