酶速率常数的自下而上的参数化:调和不一致的数据
Daniel C Zielinski1, Marta R A Matos2, James E de Bree1
1Department of Bioengineering, University of California, San Diego, CA, 92093, USA.
Metabolic engineering communications
|May 7, 2024
概括
本研究介绍了MASSef,这是一个计算工作流程,用于对酶动力学参数进行可靠的估计. 它协调不一致的数据并构建可扩展的代谢模型,利用现有的实验和机器学习数据.
科学领域:
- 代谢工程是代谢工程.
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 动态模型对于理解代谢系统和设计生产菌株至关重要.
- 合成酶对酶的动态模型 ("自下而上") 面临着诸如数据缺口,复杂机制和体外-体内相差差异等挑战.
研究的目的:
- 开发一个强大的计算工作流程,用于在详细的质量作用酶模型中估计动力参数.
- 创建一个软件包 (MASSef) 来处理各种动力参数和反应机制.
- 调和不一致的动力学数据,并使可扩展的,在生物体内一致的路径模型的构建成为可能.
主要方法:
- 开发了一种计算工作流程,用于可靠的动力参数估计,并考虑参数不确定性.
- 实现了MASSef软件包中的工作流,支持宏观和微观动力参数.
- 利用三个酶案例研究来证明数据协调和模型组装.
主要成果:
- MASSef成功地识别和协调了来自体外实验的不一致的动力学数据,以及体外和体外条件之间的不一致动力学数据.
- 参数化酶模块被有效地用于组装与体内行为一致的通路规模动态模型.
- 工作流显示了对详细的质量作用酶模型的动力参数估计的稳定性.
结论:
- MASSef 工作流提供了一种可靠的方法,可以在规模上对酶动态模型进行参数化.
- 这种方法有效地利用历史文献数据和机器学习估计,以克服动力建模的局限性.
- 能够为研究和菌株设计创建更准确和更具预测性的代谢模型.
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