Laboratory for Metabolic Systems Engineering, BioZone, Center for Applied Biosciences and Bioengineering, Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON M5T 3A1, Canada.
Database (Oxford). 2022 Oct 12;2022. doi: 10.1093/database/baac081.
The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz.
当前的动力学模型在模拟细胞表型行为方面的能力是有限的，因为细胞代谢是在包括酶调节在内的不同水平上进行调节的。小分子调节网络（SMRN）使细胞能够通过控制代谢途径中酶的活性来快速响应环境波动。然而，与代谢网络相比，SMRN 研究得还不够充分。导致人们对这个调节系统缺乏了解的主要原因是实验数据的稀疏性以及缺乏表示现有信息的标准框架。在本文中，我们介绍了 KinMod 数据库，该数据库包含了 9814 种生物的代谢和代谢调节网络的 200 多万个数据点。KinMod 数据库采用分层数据结构来：（i）表示通过体外实验获得的动力学信息与蛋白质之间的关系，重点是 SMRN；（ii）深入了解该调节网络的现有动力学参数和缺失的实验测量值；（iii）通过提供链接的组学数据的同质列表，为参数估计和准确的动力学模型构建提供便利，从而促进机器学习方法的应用。KinMod 数据库的分层本体允许灵活地探索数据属性，并在种内和种间研究代谢关系。识别缺失的实验值表明需要进行额外的实验来进行动力学参数估计。链接多组学数据并提供 SMRN 数据，鼓励开发用于预测缺失动力学参数的新的机器学习技术，并通过提供全面的可用动力学测量列表来促进细胞代谢的准确动力学模型构建。为了说明 KinMod 数据的价值，我们开发了六个分析来可视化属于代谢不同部分的数据类之间的关联。通过这些分析，我们证明了 KinMod 数据库为生物学家和工程师提供了一个独特的框架，用于检索、评估和比较物种的功能代谢，包括调节网络，并发现代谢调节的现有和缺失实验值的程度。数据库 URL：https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz。
