ThermOptCobra:热力学最优的构建和分析的代谢网络可靠的表型预测
Pavan Kumar S1,2,3, Nirav Pravinbhai Bhatt1,2,3,4
1BioSystems Engineering and Control (BiSECt) Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
iScience
|August 8, 2025
概括
在基因组尺度代谢模型 (GEM) 中,ThermOptCOBRA解决了热力学不可行的循环 (TIC). 这提高了代谢模型的质量和预测准确度,以获得更深入的细胞代谢洞察力.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
- 计算生物学 计算生物学
背景情况:
- 基因组规模的代谢模型 (GEMs) 对于理解细胞代谢至关重要.
- 热力学不可行循环 (TICs) 损害了GEMs的预测能力.
- 现有的方法很难有效地解决ICT和热力学不一致性.
研究的目的:
- 引入ThermOptCOBRA,这是一个新的计算框架,用于在GEM中处理ICT.
- 提高代谢模型的热力学一致性和预测准确性.
- 为最佳GEM构建和分析提供综合解决方案.
主要方法:
- 在ThermOptCOBRA中开发了四个集成算法.
- 利用网络拓和热力学约束来进行ICT识别.
- 适用于7,401个已发布的GEM,以评估性能和识别受阻反应.
主要成果:
- 从大量的GEM中有效地识别和删除TIC.
- 确定热力学上可行的流量方向,从而产生精细的模型.
- 构建紧,热力学一致的特定环境模型,在80%的案例中表现优于Fastcore.
- 在流量分布中改进了循环检测和删除,并增强了无循环样本生成.
结论:
- 通过有效管理ICT,ThermOptCOBRA显著提高了GEM的质量和可靠性.
- 该框架推进了代谢模型的构建,分析和预测能力.
- 通过改进的计算工具,ThermOptCOBRA可以更深入地了解细胞代谢.
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