用神经微分方程直接从基因表达数据预测代谢通路动态通量。
Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes
- 基于结构化神经微分方程建模，端到端学习代谢系统动态。
- 在156组实验中准确预测未见时间点的通量与平衡状态。
- 适用于基因敲除和不规则采样等复杂场景，适合系统代谢研究者。
我们提出一种新型数据驱动框架，作为动态通量平衡分析的替代方案，无需深度领域知识或手动构建优化问题。该框架端到端训练结构化神经微分方程过程（SNODEP）模型，利用基因表达时间序列数据估计通量与平衡样本。SNODEP克服了标准神经微分方程模型的局限性，如隐变量和解码器采样分布被限制为正态分布，且上下文点间缺乏结构关联以计算隐变量，因而更适于建模代谢系统的内在动态。通过总计156组综合实验，我们证明SNODEP不仅能准确预测真实世界基因表达数据中的未见时间点，以及通量与平衡估计值，还能推广至更具挑战性的未见基因敲除配置和不规则数据采样场景，这些对代谢通路分析至关重要。我们希望本工作能推动更可扩展、更强大的基因组尺度代谢分析模型的发展。代码已公开：https://github.com/TrustMLRG/SNODEP。
原文摘要 · Abstract (English)
We develop a novel data-driven framework as an alternative to dynamic flux balance analysis, bypassing the demand for deep domain knowledge and manual efforts to formulate the optimization problem. The proposed framework is end-to-end, which trains a structured neural ODE process (SNODEP) model to estimate flux and balance samples using gene-expression time-series data. SNODEP is designed to circumvent the limitations of the standard neural ODE process model, including restricting the latent and decoder sampling distributions to be normal and lacking structure between context points for calculating the latent, thus more suitable for modeling the underlying dynamics of a metabolic system. Through comprehensive experiments ($156$ in total), we demonstrate that SNODEP not only predicts the unseen time points of real-world gene-expression data and the flux and balance estimates well but can even generalize to more challenging unseen knockout configurations and irregular data sampling scenarios, all essential for metabolic pathway analysis. We hope our work can serve as a catalyst for building more scalable and powerful models for genome-scale metabolic analysis. Our code is available at: \url{https://github.com/TrustMLRG/SNODEP}.
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