基因组尺度代谢模型中的蛋白质约束:数据集成,参数估计和代谢表型的预测
Maurício Alexander de Moura Ferreira1, Wendel Batista da Silveira1, Zoran Nikoloski2,3
1Department of Microbiology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
Biotechnology and bioengineering
|January 5, 2024
概括
蛋白质受约束的代谢模型增强了细胞表型预测. 本综述详细介绍了这些模型的运动参数估计,改进了代谢研究.
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) 对于研究细胞代谢和生理学至关重要.
- 基于约束的建模 (CbM) 被广泛用于GEM来预测表型.
- 整合蛋白质约束可以显著提高GEM预测准确度.
研究的目的:
- 系统地审查对蛋白质受约束代谢模型的动力参数估计的最新技术.
- 讨论数据驱动和基于约束的营业额 (kcat) 估计方法.
- 突出蛋白质受约束建模中的挑战和未来方向.
主要方法:
- 系统性文献综述专注于蛋白质受约束模型的动力参数估计.
- 分析数据驱动和基于约束的方法来估计营业额 (kcat).
- 讨论将蛋白质丰度和周转数纳入代谢模型的讨论.
主要成果:
- 介绍并讨论了当前在蛋白质受约束模型中估计动力参数的方法.
- 数据驱动和基于约束的方法显示出改善营业额 (kcat) 估计的前景.
- 蛋白质约束增强了细胞表型代谢模型的预测能力.
结论:
- 准确的运动参数估计对于有效的蛋白质受约束的代谢模型至关重要.
- 对于 kcat 估计,需要进一步开发数据驱动和基于约束的方法.
- 应对当前的挑战将促进系统生物学中受蛋白质约束模型的预测性能.
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