The search for effective therapeutic targets in fields like regenerative medicine and cancer
research has generated interest in cell fate reprogramming. This cellular reprogramming
paradigm can drive cells to a desired target state from any initial state. However, methods for
identifying reprogramming targets remain limited for biological systems that lack large sets of
experimental data or a dynamical characterization. We present NETISCE, a novel
computational tool for identifying cell fate reprogramming targets in static networks. In …