A cell’s identity isn’t a list of active genes — it’s a circuit. Transcription factors switch each other on and off in tangled feedback loops, and reconstructing that wiring from data is, as one landmark put it, ‘one of the pressing open problems of computational systems biology.’ Gene regulatory network inference is how AI reads the circuit. GENIE3 turned it into a stack of regression problems — feature importance as a regulatory link. DREAM5’s blind test of 30+ methods delivered a humbling verdict — ’no single inference method performs optimally’ — and a fix: ensembles, ‘wisdom of crowds.’ SCENIC brought it to single cells, reconstructing networks and cell states at once; BEELINE benchmarked the field and found accuracy only ‘moderate,’ with pseudotime-free methods ahead. Then the payoff: CellOracle uses inferred networks ’to perform in silico transcription factor perturbations … using only unperturbed wild-type data’ — knock out a gene on a laptop, predict the phenotype, validate at the bench. SCENIC+ adds chromatin for enhancer-driven wiring. It’s the causal layer a virtual cell can’t do without.
