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

Digital Twin Cells (DCT) construct digital mirrors of physical cells in virtual space by integrating multi-modal and multi-scale biological data, which enable the monitoring, simulation, prediction, and closed-loop regulation of cellular life activities. In this study, we review the research progress of DTCs across visualization, simulation, prediction, optimization, and control. We proposes a five-level maturity model for Digital Twin Cells, using interaction depth and autonomy as metrics. The analysis explicitly points out that current research is transitioning from Level 3 (Predictive Deduction) to Level 4 (Optimized Intervention), while beginning preliminary explorations into Level 5 (Virtual-Real Symbiosis). Despite this progress, this field still faces significant challenges, including low biological fidelity, model generalization bottlenecks, and high computational costs. Finally, we highlights that the deep integration of Artificial Intelligence 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.