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bio-gene-regulatory-networks-perturbation-simulation

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Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.

skills/bioskills/bio-gene-regulatory-networks-perturbation-simulation/SKILL.md

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## Version Compatibility

Reference examples tested with: CellOracle 0.18+, scanpy 1.10+, anndata 0.10+; Dynamo (dynamo-release 1.4+), GEARS, CPA (scvi-tools ecosystem) where used.

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Perturbation Simulation

**"Predict what happens if I knock out this transcription factor"** -> Propagate a forced expression change through a learned GRN (or a learned velocity vector field), then project the shift onto the cell-state manifold to predict the DIRECTION cells move.