Systems biology at the body scale: Toward adaptive digital twins in precision medicine

Digital twins (DTs) represent the logical extension of systems biology modelling toward clinical application; however, current implementations are predominantly static, organ specific, and limited to single-instance personalisation. This review identifies five primary barriers impeding the development of adaptive, body-scale DTs: (1) parameter explosion driven by complexity, (2) weak structural identifiability, (3) lack of bidirectional multiscale coupling, (4) challenges in integrating heterogeneous asynchronous data, and (5) computational intractability for real-time applications. While each barrier has partial solutions, none is fully resolved. Hybrid mechanistic learning frameworks, such as neural ordinary differential equations, can uncover unknown couplings while maintaining interpretability. Modular architectures help manage complexity and identifiability, and uncertainty quantification supports risk-aware clinical reasoning. Agentic systems offer a promising, though still exploratory, avenue that requires further development before clinical deployment. Progress in this field requires three key shifts: prioritising modular, identifiable models over encyclopaedic coverage; integrating mechanistic modelling with data-driven methods; and designing DTs as adaptive reasoning tools that support, rather than replace, clinical evaluation.