用于预测细胞培养过程的多尺度混合建模：代谢相转变

为深入了解细胞代谢并减少细胞培养过程中的批次间变异性，本研究引入了一种多尺度混合建模框架，用于模拟和预测CHO细胞培养过程在代谢相转变下的动态行为。该模型捕捉到分子、细胞和宏观动力学水平之间的依赖关系，考虑到单个细胞代谢相的可变性。它整合了三个组件：(i)单个细胞代谢网络的随机机制模型，(ii)相转变的概率模型，和(iii)异构种群动力学的宏观动力学模型。这种模块化架构 enables flexible representation of process trajectories under diverse conditions and incorporates heterogeneous online (e.g., oxygen uptake, pH) and offline measurements (e.g., viable cell density, metabolite concentrations)。 Leveraging these data and single-cell insights, the framework predicts culture dynamics using only readily available online measurements and initial conditions, delivering accurate long-term forecasts of multivariate culture behavior and uncertainty-aware estimates of batch-to-batch variation. Overall, this work establishes a robust foundation for digital twin platforms and predictive bioprocess analytics, supporting systematic experimental design and process control to improve yield and production stability in biomanufacturing.