用图形神经网络解开基因组规模代谢网络的热力学原理
Wenchao Fan1, Yonghong Hao2, Xiangyu Hou2
1Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Cell systems
|September 19, 2025
概括
这项研究介绍了dGbyG,一个图形神经网络模型,可以准确预测代谢反应热力学. 这促进了代谢网络的分析,并揭示了代谢调节和优化的原则.
科学领域:
- 生物化学 生物化学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 代谢热力学数据很少,限制了基因组规模的代谢理解.
- 标准的吉布斯自由能量变化 (ΔrG°) 对于代谢研究至关重要.
研究的目的:
- 开发一个准确的模型来预测 ΔrG°.
- 将热力学预测集成到代谢网络中,以改善分析.
- 揭示控制新陈代谢调节和途径优化的热力学原理.
主要方法:
- 开发了dGbyG,一个图形神经网络 (GNN) 模型.
- 应用dGbyG来预测代谢反应的ΔrG°.
- 整合到代谢网络中的预测.
- 分析了热力学驱动反应 (TDR) 和线性代谢途径.
主要成果:
- dGbyG展示了卓越的准确性,多功能性,稳定性和通用性.
- 整合dGbyG改善了代谢模型治愈和流量预测.
- 识别了具有独特网络拓和酶表达模式的TDR.
- 在线路中发现了一个普遍的热力学模式,通过多目标优化来解释.
结论:
- dGbyG扩展了可用于代谢研究的热力学数据.
- 代谢调节涉及热力学,网络拓和酶表达之间的合.
- 代谢途径表现出最佳性原则,平衡流量,酶和代谢物负载.
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