IMCell: influence-maximization prediction of minimal transcription factor sets for cell fate engineering

IMCell predicts minimal, optimized transcription factor (TF) sets that drive cell identity transitions by formulating TF selection as an influence-maximization problem over a signed gene-regulatory network. IMCell jointly maximizes activation of target genes and repression of off-target genes using a greedy Monte-Carlo solver, supports optional expression-based node weighting, and can be extended (dynIMCell, via Epoch) to predict step-wise differentiation protocols. Software accompanying Su, Ly & Cahan, "Prediction of parsimonious and temporally-sensitive sets of cell fate engineering transcription factors with IMCell."