发表机构
Northeastern University; Clemson University（东北大学; 克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一个多尺度机制框架，耦合代谢与糖基化网络，通过单细胞随机模型和累积氧摄取率变化，预测CHO细胞培养中的产量和糖基化质量，并在氨胁迫条件下验证。
AI 中文摘要
CHO细胞培养中的抗体产量和糖基化质量源于动态变化的代谢环境，但现有模型往往孤立或单一尺度。本文提出一个多尺度机制框架，连接分子、细胞和过程水平，预测输入如何塑造生物过程轨迹。该框架基于一个单细胞动力学模型，耦合控制产量和关键质量属性（CQA）的代谢和糖基化网络。一个随机单细胞模型描述了生长、生产和衰退之间的环境依赖性转变，捕获群体异质性。我们进一步引入氧摄取率的累积变化，整合随时间累积的总代谢调整，作为预测代谢转变的紧凑生物标志物。与群体平均方法不同，该模型将细胞分辨的代谢状态（包括氨调节的高尔基体pH、核苷酸糖可用性、锰辅因子和合成速率）传播到聚糖加工中。该框架使用产生VRC01 IgG1的CHO-K1补料分批培养进行评估，在靶向氨胁迫、匹配对照条件和更严格控制的金字塔补料策略下。它准确预测了细胞密度、代谢物、产量和糖基化的轨迹，包括氨胁迫下G0F增加和半乳糖基化减少，并量化了代谢异质性如何驱动产量和CQA的变异性。这项工作为预测性生物制造和先进过程控制提供了统一基础。
英文摘要
Antibody productivity and glycosylation quality in CHO cell cultures emerge from a dynamically evolving metabolic environment, yet existing models often work in isolation or at a single scale. Here, we present a multiscale mechanistic framework linking molecular, cellular, and process scales to predict how inputs shape bioprocess trajectories. The framework combines a single-cell kinetic model of metabolism and glycosylation with a stochastic population model that captures environment-dependent transitions among growth, production, and decline states. To characterize metabolic adaptation, we introduce the cumulative variation in oxygen uptake rate, a trajectory-based biomarker that quantifies the total metabolic adjustment experienced during culture. Unlike population-averaged approaches, the model propagates cell-resolved metabolic states (including ammonia-regulated Golgi pH, nucleotide sugar availability, manganese cofactors, and synthesis rates) into glycan processing. The framework was evaluated using CHO-K1 fed-batch cultures producing VRC01 IgG1 under targeted ammonia stress, matched control conditions, and a pyramid-feeding strategy with tighter control. It accurately reproduced trajectories of cell growth, metabolites, productivity, and harvest glycosylation, including increased G0F abundance and reduced galactosylation under ammonia stress. By mechanistically linking process conditions to cell-state dynamics and glycosylation outcomes, the framework provides a unified foundation for digital bioprocessing, predictive biomanufacturing, and advanced process control.
Comments50 pages, 15 figures
Journal refBiotechnol. Bioeng. (2026), online ahead of print
