Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

IF 3.7 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Metabolic Engineering Communications Pub Date : 2024-04-23 DOI:10.1016/j.mec.2024.e00234
Daniel C. Zielinski , Marta R.A. Matos , James E. de Bree , Kevin Glass , Nikolaus Sonnenschein , Bernhard O. Palsson
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Abstract

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of ‘bottom up’ parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard ‘macroscopic’ kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and ‘microscopic’ rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

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酶速率常数的自下而上参数化:调和不一致的数据
代谢动力学模型是研究复杂代谢系统和设计生产菌株的理想平台。鉴于可以从历史实验和机器学习估算工具中获得酶动力学数据,一种直接的建模方法是逐个酶收集动力学数据,直到达到所需的规模。然而,这种 "自下而上 "的动力学模型参数化一直很困难,原因有很多,包括动力学参数的差距、酶机制的复杂性、从不同来源获得的参数之间的不一致性以及体外-体内差异。在此,我们提出了一种计算工作流程,用于对详细的质量作用酶模型的动力学参数进行稳健估算,同时考虑到参数的不确定性。由此产生的软件包被称为 MASSef(质量作用化学计量模拟酶拟合软件包),可以处理标准的 "宏观 "动力学参数,包括 Km、kcat、Ki、Keq 和 nh,以及以质量作用反应和 "微观 "速率常数定义的各种反应机制。我们提供了三个酶案例研究,证明这种方法可以识别并调和体外实验中或体外与体内酶功能之间不一致的数据。我们进一步展示了参数化酶模块如何用于建立与体内行为一致的通路尺度动力学模型。这项工作利用大量历史文献数据和机器学习参数估计,实现了大规模酶动力学模型的稳健参数化,从而在酶动力学行为知识遗产的基础上更上一层楼。
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来源期刊
Metabolic Engineering Communications
Metabolic Engineering Communications Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
13.30
自引率
1.90%
发文量
22
审稿时长
18 weeks
期刊介绍: Metabolic Engineering Communications, a companion title to Metabolic Engineering (MBE), is devoted to publishing original research in the areas of metabolic engineering, synthetic biology, computational biology and systems biology for problems related to metabolism and the engineering of metabolism for the production of fuels, chemicals, and pharmaceuticals. The journal will carry articles on the design, construction, and analysis of biological systems ranging from pathway components to biological complexes and genomes (including genomic, analytical and bioinformatics methods) in suitable host cells to allow them to produce novel compounds of industrial and medical interest. Demonstrations of regulatory designs and synthetic circuits that alter the performance of biochemical pathways and cellular processes will also be presented. Metabolic Engineering Communications complements MBE by publishing articles that are either shorter than those published in the full journal, or which describe key elements of larger metabolic engineering efforts.
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