School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China.
Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China.
mSystems. 2024 Aug 20;9(8):e0069724. doi: 10.1128/msystems.00697-24. Epub 2024 Jul 26.
Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.
微生物代谢动态建模对于生物合成系统和工业过程的合理优化都很重要，有助于实现绿色高效的生物制造。传统方法利用显式方程系统来表示代谢网络，能够量化途径通量以识别代谢瓶颈。然而，这些白盒模型尽管应用广泛，但在模拟代谢动态方面存在局限性，并且对于缺乏网络结构和动力学参数信息的工业菌株来说，其本身的准确性也不高。另一方面，黑盒模型不依赖于菌株的先验机制知识，而是基于生物合成系统在实际运行中的观测时间序列轨迹构建。实际上，这些观测通常是不规则的，在多个独立批次中观测时间点不连续，每个时间点都可能包含缺失的测量值。对于现有方法来说，从这种不规则数据中学习仍然具有挑战性。为了解决这个问题，我们提出了双向时间序列状态转移网络（BTSTN），可以直接从不规则观测中对代谢动态进行建模。我们使用从理想动态系统和实际发酵过程中获得的评估数据集来演示，BTSTN 可以准确地重建动态行为并预测未来轨迹。与最先进的方法相比，该方法在应对缺失测量值和噪声方面表现出更强的稳健性。
重要性
工业生物合成系统通常涉及遗传背景不明确的菌株，这给建模其独特的代谢动态带来了挑战。在这种情况下，通常依赖于推断网络的白盒模型的适用性和准确性受到限制。相比之下，黑盒模型，如统计模型和神经网络，是直接从生物合成系统实际运行的观测时间序列轨迹拟合或学习得到的。这些方法通常假设观测是规则的，没有缺失的时间点或测量值。如果观测是不规则的，则需要进行预处理步骤以获得完整的数据集，然后才能进行后续的模型训练，但同时，这也不可避免地会给生成的模型带来误差。BTSTN 是一种从不规则观测中直接学习的新方法。这一独特的特性使其成为代谢动态建模现有技术的独特补充。
