Fig. 3: Predictor-guided generator optimization enables gene-specific navigation of the regulatory sequence-expression landscape.
From: Controlling gene expression with deep generative design of regulatory DNA
a Schematic depiction of the procedure to optimize the generator using a trained predictor7, which introduces codon frequency information into the generative approach and explores the input latent space of the generator to produce sequence variants across the whole range of gene expression, providing precise navigation of the gene regulatory sequence-expression landscape. b Predicted expression levels of generated sequence variants across optimization iterations set to either maximize (red) or minimize (blue) expression levels (n = 64,000). Black lines denote average expression levels and TPM transcripts per million. c T-distributed stochastic neighbor embedding (t-SNE)60 mapping of the input latent subspaces that produce unique sequence variants spanning ~6 orders of magnitude of gene expression (black and colored dots: progression of low to high expression levels is marked with progression from blue to red, respectively), uncovered using the predictor-guided generator optimization. Black dots represent selections of 10 sequence variants per each of the 4 expression groups covering a 4 order-of-magnitude range of predicted expression levels from TPM ~10 to ~10,000. Source data are provided as a Source Data file.
