Bio-digital twins represent a revolutionary convergence of computational biology , systems physiology , and artificial intelligence , enabling high-fidelity virtual simulations of human physiological systems. By integrating molecular, cellular, organ-level, and systemic data, these platforms allow researchers to model complex biological interactions in silico , offering unprecedented insights into health, disease progression, and therapeutic responses across multiple scales of human biology. The construction of bio-digital twins relies on the integration of multi-omics datasets , including genomics, transcriptomics, proteomics, metabolomics, and epigenetics, combined with real-time physiological monitoring and clinical data. This multi-layered approach enables predictive modeling of individual variability, offering a mechanistic understanding of how molecular and cellular processes influence organ function, systemic homeostasis, and disease susceptibility. Advanced computa...