Digital Twin Cell: the Evolution from Static Atlas to Dynamic Life

Digital twin cells (DTCs) construct digital mirrors of physical cells in virtual space by leveraging key technologies such as multimodal data fusion, geometric modeling and visualization, simulation and prediction, and closed-loop optimization and control, which enable the simulation, prediction, and closed-loop regulation of cellular life activities. In this study, we systematically review research advances in visualization, simulation, prediction, optimization, and control. We propose a five-level maturity model calibrated by interaction depth and autonomy, which delineates the progression from static atlas to dynamic life. Despite this progress, this field still faces significant challenges, including low biological fidelity, model generalization bottlenecks, and high computational costs. Finally, we highlight that the deep integration of artificial intelligence (AI) with biological mechanisms, the development of novel measurement and execution technologies, and the construction of open-source ecosystems will drive digital twin cells toward virtual-real symbiosis, ultimately realizing the evolution from a static atlas to dynamic life.