Understand what controls a gene — and what to do next.

Darwin is an AI research agent for gene regulation.

Explore regulatory networks, trace biological mechanisms, compare species, evaluate evidence, predict perturbations, and develop experimental hypotheses from a single biological question.

Ask Darwin: “What regulators could I target to reduce anthocyanin production in petunia flowers?”
8 species · ~142,000 genes · >1 million regulatory relationships · measured and inferred evidence tracked separately

Ask → Investigate → Connect → Predict → Test

A research flow that moves from question to experiment.

1

Ask

Start with a gene, phenotype, pathway, or biological question.

2

Investigate

Darwin selects and runs the appropriate regulatory, expression, sequence, pathway, and evidence analyses.

3

Connect

Trace relationships across genes, regulatory mechanisms, biological processes, and species.

4

Predict

Explore candidate regulators and the likely consequences of perturbing them.

5

Test

Turn the strongest hypotheses into candidate experiments.

One workspace. From biological question to experimental hypothesis.

Capabilities built around how researchers actually work.

Discover regulators

Explore TF → target relationships across species.

Evaluate evidence

Combine regulatory sources, expression, motifs, pathways, and literature.

Compare across species

Follow conserved relationships and orthologs.

Predict interventions

Model what happens when regulators are activated or suppressed.

Design experiments

Move from hypothesis to RNAi/dsRNA design workflows.

Prioritize experiments

Rank candidates and produce collaborator-ready outputs.

Built for researchers, not just bioinformaticians.

Every result traces back to its source. Darwin keeps curated and inferred evidence distinct, tracks provenance across regulatory sources, expression data, motifs, pathways, and literature, and builds on the GRN Atlas data layer — a growing multi-species gene regulatory network resource.

Darwin helps researchers spend less time assembling biological evidence and more time deciding what experiment to run next.