Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic stochastic reaction networks representing a multi-scale bioprocess mechanistic model that allows us to integrate disparate data from diverse production processes and leverage the information from existing macro-kinetic and genome-scale models. To support forward prediction and backward reasoning, we develop a convergent adjoint SA algorithm studying how the perturbations of model parameters and inputs (e.g., initial state) propagate through enzymatic reaction networks and impact on output trajectory predictions. This SA can provide a sample efficient and interpretable way to assess the sensitivities between inputs and outputs accounting for their causal dependencies. Our empirical study underscores the resilience of these sensitivities and illuminates a deeper comprehension of the regulatory mechanisms behind bioprocess through sensitivities.

翻译：受生物制造系统数字孪生开发中紧迫挑战的驱动，本文引入了一种伴随敏感性分析方法，以加速机理模型参数的学习。本文研究了代表多尺度生物过程机理模型的酶促随机反应网络，该模型使我们能够整合来自不同生产过程的异构数据，并利用现有宏观动力学模型与基因组尺度模型的信息。为支持前向预测与后向推理，我们开发了一种收敛的伴随敏感性分析算法，用于研究模型参数与输入（如初始状态）的扰动如何通过酶促反应网络传播，并影响输出轨迹预测。该敏感性分析能够提供一种样本高效且可解释的方法，在考虑输入与输出间因果依赖关系的前提下评估其敏感性。我们的实证研究证实了这些敏感性的鲁棒性，并通过敏感性分析揭示了生物过程背后调控机制的更深刻理解。