Computational blueprints for cell fate programming

Cell fate programming enables applications in disease modeling, drug discovery, and regenerative medicine. Foundational studies established differentiation protocols, but their scalability is constrained by combinatorial complexity. Computational methods enable cell annotation, network inference, trajectory analysis, and have been applied to prioritize transcription factors and small molecules for cell fate programming, although prospective adoption for protocol design remains uneven. Single-cell and spatial omics, perturbation screens, and deep learning expand predictive scope while introducing challenges in domain shift, interpretability, and reproducibility. Here, I synthesize these approaches as pragmatic computational blueprints embedded in an iterative design-test-learn pipeline for cell fate programming.