Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany.
Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.
FEBS J. 2022 Oct;289(19):5864-5874. doi: 10.1111/febs.16318. Epub 2021 Dec 26.
EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
EnzymeML 是一种基于 XML 的数据交换格式,通过描述反应条件、底物和产物浓度的时程、动力学模型和估计的动力学常数,支持酶学数据的全面文档记录。EnzymeML 基于系统生物学标记语言,通过实施 STRENDA 指南进行扩展。EnzymeML 文档作为一种容器,用于在实验平台、建模工具和数据库之间传输数据。EnzymeML 通过引入标准化的数据交换格式来支持科学界,使酶学数据根据 FAIR 数据原则具有可发现性、可访问性、互操作性和可重用性。Python 中的应用程序编程接口支持用于数据采集、数据分析和发布的软件工具的集成。通过从结构化电子表格或从 STRENDA DB 数据库条目创建 EnzymeML 文档、使用建模平台 COPASI 进行动力学建模以及上传到酶反应动力学数据库 SABIO-RK,证明了使用 EnzymeML 实现无缝数据流的可行性。