bio-gene-regulatory-networks-perturbation-simulation

Predict cell-state shifts by simulating TF perturbations on a base gene regulatory network built from chromatin accessibility data, calculating embedding shifts to forecast potential transitions across cell types in scRNA-seq datasets.

Simulating perturbations on a gene regulatory network works by applying in silico TF knockout or overexpression to predict how cell identities change, ranking transcription factors by their predicted disruption to cell-fate trajectories.

Yes, you can simulate both TF knockout and overexpression to prioritize perturbation experiments. The simulation ranks transcription factors by their predicted disruption to cell fate, guiding which perturbations to validate physically.

Yes, you need chromatin accessibility data to build the base GRN for trajectory simulation. The GRN-based perturbation requires this base network to accurately predict embedding shifts and potential cell-type transitions in scRNA-seq datasets.

Rank transcription factors by predicted disruption to cell fate by running TF knockout and overexpression simulations on a base GRN, then comparing the resulting embedding-shift calculations to identify the most significant cell-state transitions.