基因组规模资源分配模型的现状,挑战和机遇:一个数学视角
Wheaton L Schroeder1,2, Patrick F Suthers1,2,3, Thomas C Willis1,2
1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Metabolites
|July 26, 2024
概括
基因组规模代谢模型 (GEMs) 预测细胞行为,但往往忽视蛋白质成本和局限性. 资源分配模型 (RAMs) 整合了这些因素,以便在代谢工程中进行更准确的预测.
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:
- 固体测量基因组规模代谢模型 (GEMs) 被广泛使用,但存在局限性.
- GEMs不考虑蛋白质成本,酶动力学或蛋白质组限制,可能导致不准确的预测.
- 解决这些局限性的先前尝试包括分子拥挤的流量平衡分析.
研究的目的:
- 为资源分配模型 (RAMs) 提供全面的审查.
- 讨论从静态度模型到RAM框架的演变.
- 根据它们的实用性,数据需求,优势和局限性来比较不同的RAM框架.
主要方法:
- 关于代谢建模框架的现有文献的审查.
- 将RAM框架分为粗粒度和细粒度的方法.
- 分析数学框架以对比不同的RAM.
主要成果:
- RAM 将蛋白质组相关的局限性纳入GEM.
- RAM框架分为粗粒度和细粒度的类别.
- 该评论详细介绍了各种RAM的实用性,数据要求和局限性.
结论:
- 与传统的GEM相比,RAM为代谢建模提供了更加机械的方法.
- 了解不同RAM的优缺点对于选择适合研究的模型至关重要.
- 未来的RAM应用对推进代谢工程和合成生物学具有前途.
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