Computational Cellular Programming: In Silico Modeling of Direct and Reprogrammed Hepatic Lineage Induction via Gene Regulatory and Functional Dynamics

Understanding how cellular identity emerges from regulatory and energetic interactions remains a central question in developmental and synthetic biology. Here, we introduce a computational framework for in silico cellular programming, enabling the simulation of lineage acquisition through gene-regulatory and functional feedback dynamics. Two paradigms of hepatic identity induction were modeled: direct programming, representing immediate activation of hepatocyte master regulators from an undifferentiated baseline, and reprogramming, representing the conversion of a lineage-committed fibroblast into a hepatocyte-like state.

Each simulation integrates a minimal hepatocyte gene regulatory network—comprising HNF4A, FOXA2, CEBPA, ALB, CYP3A4, and HNF1A—with condition-dependent feedbacks reflecting metabolic and morphological stability. Comparative analyses reveal that direct programming rapidly converges to a stable hepatic attractor with low variance and tight network coherence, whereas reprogrammed trajectories display delayed stabilization, higher variance, and context-dependent adaptability.

These findings demonstrate that functional maturity can be algorithmically achieved through feedback-driven regulatory dynamics, and that morphological order predicts metabolic coherence. Together, they establish a conceptual foundation for computational cellular programming—where cell identity can be represented, manipulated, and matured as an emergent property of coded regulatory logic.