Network Biology: Current Status, Applications, Challenges, and Future Prospects

Network biology provides a systems-level framework for understanding how interactions among molecular components, cells, microbial communities, tissues, and organs generate biological function and disease phenotypes. Recent advances in multiomics, single-cell and spatial profiling, network inference, organoid and organ-on-chip technologies, computational modeling, and artificial intelligence have shifted the field from static molecular maps toward dynamic, multiscale, and predictive network models. Major branches include molecular interaction networks, intracellular regulatory networks, single-cell and multicellular networks, microbial networks, disease networks, and interorgan interaction networks. Experimental and computational approaches are compared with respect to network reconstruction, causal validation, and cross-scale integration. Biomedical applications span disease classification, biomarker discovery, therapeutic-target identification, drug-response prediction, regenerative medicine, and precision healthcare. Particular emphasis is placed on interorgan interaction networks, in which carrier, route, range, timescale, recipient specificity, directionality, and feedback are treated as distinct mechanistic dimensions while mechanistic confidence is distinguished from translational maturity. Persistent challenges include incomplete context-specific network reconstruction, uncertain causal directionality, limited cross-scale integration, model interpretability, and insufficient clinical validation. Further progress will depend on integrating longitudinal and perturbation-resolved measurements with human-relevant experimental systems and multiscale computational models that quantify uncertainty and support prospective validation of patient-specific predictions.