Collaborative Research: Accelerating Synthetic Biology Discovery & Exploration through Knowledge Integration

合作研究：加速合成生物学发现

• 批准号: 1939860
• 负责人: Eric Young
• 金额: $ 20.41万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939860&HistoricalAwards=false
• 关键词: Collaborative; Research; Accelerating; Synthetic; Biology

The scientific challenge for this project is to accelerate discovery and exploration of the synthetic biology design space. In particular, many parts used in synthetic biology come from or are initially tested in a simple bacteria, E. coli, but many potential applications in energy, agriculture, materials, and health require either different bacteria or higher level organisms (yeast for example). Currently, researchers use a trial-and-error approach because they cannot find reliable information about prior experiments with a given part of interest. This process simply cannot scale. Therefore, to achieve scale, a wide range of data must be harnessed to allow confidence to be determined about the likelihood of success. The quantity of data and the exponential increase in the publications generated by this field is creating a tipping point, but this data is not readily accessible to practitioners. To address this challenge, our multidisciplinary team of biological engineers, machine learning experts, data scientists, library scientists, and social scientists will build a knowledge system integrating disparate data and publication repositories in order to deliver effective and efficient access to collectively available information; doing so will enable expedited, knowledge-based synthetic biology design research.This project will develop an open and integrated synthetic biology knowledge system (SBKS) that leverages existing data repositories and publications to create a single interface that transforms the way researchers access this information. Access to up-to-date information in multiple, heterogeneous sources will be provided via a federated approach. New methods based on machine learning will be developed to automatically generate ontology annotations in order to create connections between data in various repositories and information extracted from publications. Provenance for each entity in SBKS will be tracked, and it will be utilized by new methods that are developed to assess bias and assign confidence scores to knowledge returned for each entity. An intuitive, natural-language-based interface and visualization functionality will be implemented for users to easily access and explore SBKS contents. Additionally, as ethics is necessarily a part of synthetic biology research, data from text sources related to ethical concerns in synthetic biology will also be incorporated to inform researchers about ethical debates relevant to their search queries. Finally, to test the SBKS API, a new genetic design tool, Kimera, will be developed that leverages the knowledge in SBKS to produce better designs. The proposed SBKS will accelerate discovery and innovation by enabling researchers to learn from others' past experiences and to maximize the productivity of valuable experimental time on testing designs that have a higher likelihood of working when transformed to a new organism. This research thus provides the potential for transformative research outcomes in the field of synthetic biology by leveraging data science to improve the field's epistemic culture. For more information please see https://synbioks.github.io.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by the HDR and the Division of Biological Infrastructure within the NSF Directorate of Directorate for Biological Sciences.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.

该项目的科学挑战是加快对合成生物学设计空间的发现和探索。 特别是，合成生物学中使用的许多部分来自或最初是在简单的细菌（大肠杆菌）中测试的，但是在能量，农业，材料和健康中的许多潜在应用都需要不同的细菌或更高水平的生物（例如酵母） 。目前，研究人员使用试验方法，因为他们找不到有关具有特定部分兴趣的先前实验的可靠信息。这个过程根本无法扩展。因此，为了达到规模，必须利用广泛的数据，以确定成功的可能性。该领域生成的出版物的数据数量和指数增加是在创建一个临界点，但是从业者不容易访问此数据。为了应对这一挑战，我们由生物工程师，机器学习专家，数据科学家，图书馆科学家和社会科学家组成的跨学科团队将建立一个集成不同的数据和出版存储库的知识系统，以便有效，有效地访问共同可用的信息；这样做将使基于知识的合成生物学设计研究能够开发一个开放和综合的合成生物学知识系统（SBK），该项目利用现有的数据存储库和出版物来创建一个单个界面，从而改变研究人员访问此信息的方式。将通过联合方法提供多个异构资源中的最新信息。将开发基于机器学习的新方法，以自动生成本体注释，以在各种存储库中的数据与从出版物中提取的信息之间建立连接。 将跟踪SBK中每个实体的出处，并将通过开发的新方法来使用它来评估偏见并将置信度分数分配给每个实体返回的知识。将实现一个直观的，自然的界面和可视化功能，以便用户轻松访问和探索SBK的内容。 此外，由于伦理学必然是合成生物学研究的一部分，因此还将纳入与合成生物学中与伦理问题有关的文本问题的数据，以告知研究人员与搜索查询相关的道德辩论。 最后，将开发一种新的遗传设计工具SBKS API，它将利用SBK中的知识来生成更好的设计。 拟议的SBK将通过使研究人员能够从他人过去的经验中学习，并最大程度地利用有价值的实验时间来测试设计时，这些实验时间的生产力最大化，而在转化为新的生物体时，他们有可能工作的可能性更高。 因此，这项研究通过利用数据科学改善该领域的认知文化，为合成生物学领域的变革性研究结果提供了潜力。有关更多信息，请参见https：//synbioks.github.io.io.这个项目是国家科学基金会利用数据革命（HDR）的大思想活动的一部分，并由HDR和HDR和生物基础设施共同支持NSF生物科学局局。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估的。

项目成果

Synthetic Biology Knowledge System

• DOI: 10.1021/acssynbio.1c00188
• 发表时间: 2021-08-13
• 期刊: ACS SYNTHETIC BIOLOGY
• 影响因子: 4.7
• 作者: Mante, Jeanet;Hao, Yikai;Myers, Chris J.
• 通讯作者: Myers, Chris J.