Morrell William C, Birkel Garrett W, Forrer Mark, Lopez Teresa, Backman Tyler W H, Dussault Michael, Petzold Christopher J, Baidoo Edward E K, Costello Zak, Ando David, Alonso-Gutierrez Jorge, George Kevin W, Mukhopadhyay Aindrila, Vaino Ian, Keasling Jay D, Adams Paul D, Hillson Nathan J, Garcia Martin Hector
DOE Joint BioEnergy Institute , Emeryville, California 94608, United States.
Biotechnology and Bioengineering and Biomass Science and Conversion Department, Sandia National Laboratories , Livermore, California 94550, United States.
ACS Synth Biol. 2017 Dec 15;6(12):2248-2259. doi: 10.1021/acssynbio.7b00204. Epub 2017 Sep 8.
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.
尽管合成生物学的最新进展使我们能够比以往更高效地生成生物设计，但我们预测这些设计最终结果的能力仍处于起步阶段。预测模型需要大量高质量数据来进行参数化和测试，而这些数据通常并不容易获得。在这里，我们展示了实验数据仓库（EDD），这是一个设计为实验数据和元数据存储库的在线工具。EDD提供了一种便捷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，以便与预测算法一起使用。在本文中，我们描述了EDD，并展示了它在三个不同用例中的效用：已表征合成生物学部件的存储、利用蛋白质组学数据提高生物燃料产量，以及使用细胞外代谢物浓度预测细胞内代谢通量。
