EAGER: Technologies for the Reproducibility of Biological Computational Models

在过去的20年里，生物和生理系统的仿真模型在数量和复杂性上都在增加，它们是真正促进对这些复杂系统的理解的重要手段。 如果这些模型能够在研究小组之间方便而准确地共享，并且能够用更广泛的数据集进行建模和验证，那么这些模型的影响力将会增加。 华盛顿大学生物医学建模可再现系统中心旨在开发工具：1）构建可再现模型; 2）使用统一技术注释模型; 3）可再现地模拟，分析和可视化模型在线。 EARLY概念探索性研究资助（EAGER）奖的成果将为研究社区提供工具，使已发布的模型更易于理解，可视化和可重用。 研究小组将为各种讲习班和会议作出贡献，以突出工作重点，并对用户进行新工具和标准方面的培训。此外，该项目还包括一项在SoundBio举办下午科学活动的计划，SoundBio是一家专注于生物学的创客空间组织，其中包括高中和初中学生的动手和计算练习。EAGER奖将支持生物医学建模可再生系统中心的努力，以：1）开发一个智能注释编辑器，通过添加元数据以不引人注目的方式帮助用户注释模型;以及2）开发用于描述模拟实验的人类可读标准，这样，实验的精确条件可以通过多个平台和系统重新创建。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Simulation models of biological and physiological systems have been increasing in number and complexity over the last 20 years, and they are an important means to truly advance understanding of these complex systems. The impact of these models will be increased if they can be shared easily and accurately between research groups and so that they can both model and be validated with broader sets of data. The Center for Reproducible Systems for Biomedical Modeling at the University of Washington seeks to develop tools to: 1) build reproducible models; 2) to annotate models using a unified techniques; and 3) reproducibly simulate, analyze, and visualize models online. The outcomes of this EArly-concept Grant for Exploratory Research (EAGER) award will provide the research community with tools to make published models more understandable, visualizable, and reusable. The research team will contribute to various workshops and conferences to highlight the work and train users in the new tools and standards. In addition, the project includes a plan to host afternoon science activities at SoundBio, a maker-space organization that focuses on biology, that would include hands-on and computational exercises for high school and middle school students.This EAGER award will support efforts of the Center for Reproducible Systems for Biomedical Modeling to: 1) develop a smart annotation editor to assist users in an unobtrusive way with the annotation of models through the addition of metadata; and 2) develop a human-readable standard for describing simulation experiments, such that the precise conditions of the experiment could be recreated through multiple platforms and systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.