Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

中文翻译：

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实验数据仓库：用于生物学实验数据存储，共享和可视化的基于Web的软件工具

尽管合成生物学的最新进展使我们能够比以往更有效地生产生物学设计，但我们预测这些设计最终结果的能力仍处于萌芽状态。预测模型需要对大量高质量数据进行参数化和测试，而这通常是不可用的。在这里，我们介绍了实验数据仓库（EDD），这是一种在线工具，被设计为实验数据和元数据的存储库。EDD提供了一种方便的方式来上传各种数据类型，可视化这些数据并以标准化方式导出它们，以与预测算法配合使用。在本文中，我们描述了EDD并展示了其在三种不同用例中的效用：存储特征化的合成生物学部分，利用蛋白质组学数据来提高生物燃料产量，