使用GECKO工具箱3.0重建,模拟和分析受酶约束的代谢模型
Yu Chen1,2, Johan Gustafsson1, Albert Tafur Rangel1,3
1Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
Nature protocols
|January 18, 2024
概括
GECKO 3.0通过结合酶制约因素来增强基因组规模的代谢模型 (GEMs),改善了细胞代谢的预测. 这种方法可以创建受酶约束的代谢模型 (ecModels),在各种生物体中表现更好.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
背景情况:
- 基因组规模代谢模型 (GEMs) 对于探索细胞代谢至关重要,但往往无法预测关键的表型.
- 现有的GEM缺乏酶制约因素,限制了它们在生物技术和生物医学中的预测准确性.
研究的目的:
- 为了介绍GECKO 3.0,一种通过结合酶制约来增强GEM的方法.
- 为各种生物体重建精确的受酶约束的代谢模型 (ecModels).
主要方法:
- 从一个初始代谢模型扩展到一个ecModel结构.
- 酶周转数和蛋白质组学数据的整合.
- 使用GECKO 3.0.0进行模型调整,模拟和分析.
- 纳入深度学习预测的酶动力学.
主要成果:
- GECKO 3.0 便于创建 ecModels,其预测性能比传统的 GEM 更好.
- 该协议允许对各种生物体进行代谢模型重建,即使没有实验动力学数据.
- 该方法证明了效率,酵母的运行时间为5小时.
结论:
- GECKO 3.0显著提升了代谢模型的预测能力.
- 酶受约束的代谢模型对于准确的细胞行为模拟至关重要.
- 该协议为构建任何生物体或细胞系的预测性生态模型提供了一个强大的框架.
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