SOURCE-LINKED INTELLIGENCE

Decoding animal genomes into cell types

CORDIS · observation · Publication date unknown

s cell types and developmental trajectories from egg to adult, using whole-organism single-cell multi-omics, thus capturing the spectrum of “activation states” that emerge from the regulatory genome. Deep learning models will be trained on regulatory sequences to predict and explain gene regulatory networks (GRN) and GRN transitions between cell states, encoded by enhancers, promoters, transcription factors (TF), effector genes, and feedback loops. Based on a better mechanistic understanding, we will translate this framework to other animals, including octopus, birds, and mammals, and ask how regulatory programs evolve, with a focus on neuronal diversity in the brain. Using new algorithms for cross-species deep learning and combinatorial optimization, we will study how combinations of expressed TFs co-evolve with genomic enhancer logic. We are unique in our approach because we will develop and use new technological assays, deep learning, and massively parallel reporter assays, and combine these with perturbation experiments and synthetic biology to test our hypotheses. After iterativ

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recordType

award

status

SIGNED

region

EU

value

2500000

unit

EUR