Doctoral Dissertation Research: Unveiling Conceptual Shifts and Novel Dynamics in Genetic Engineering Science: A Gene Drive Case Study

博士论文研究：揭示基因工程科学中的概念转变和新动态：基因驱动案例研究

• 批准号: 2122621
• 负责人: Manfred Laubichler
• 金额: $ 1.51万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122621&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Unveiling; Conceptual

How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing interest with a recent technological breakthrough and a new set of commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent. During the historical and conceptual evolution of gene drives beginning in the 1960s, there has been many innovations and publications. Along with that, gene drive science developed considerable public attention, explosion of new scientists, and variation in the way the topic is discussed. It is now time to look at this new organization of science using a systematic approach to characterize the system which has enabled knowledge to grow in this scientific field. This project will break new ground in how knowledge advances in genetic engineering science, and how we understand what a “gene drive” is through analysis of language, communities, and social media. In effect, this research will advance multiple fields and enable a deeper understanding of knowledge and complex systems by a wide audience through publicly available dissemination of results through conferences, blogs, GitHub, and scholarly publications. This project will document patterns of publication, collaborative relationships, social media influence, then combine those factors to characterize the knowledge system into a signal detection algorithm to predict the future trajectory of the larger CRISPR-Cas9 science. The results of computational analysis will provide an in-depth and complete characterization of the structure, dynamics, and evolution of scientific knowledge found in the gene drive technology. In addition, the project will analyze how the public opinion influences the progress of genetic engineering technologies through social media and news platforms. Further, time series analysis of the multiple layers of discourse will enable a diachronic connective mapping of collaborative relationships and track linguistic variation and change, highlighting where ambiguous language may appear. Thus, improving and creating more cohesive scientific language. Overall, depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore, characterizing the current technological, methodological, and social contexts that can influence scientific knowledge. Research results will reach conferences, blogs, GitHub, and be shared through both traditional and digital publications. Scholarly results will be available through different websites, and as much as possible will be shared for free.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.

基础科学、生物技术和工业之间的交叉点如何创造知识？基因驱动是一个有趣的例子，因为它们将长期的兴趣与最近的技术突破和一系列新的商业应用结合起来。它们优先遗传的频率高于典型的孟德尔百分之五十。在 20 世纪 60 年代开始的基因驱动的历史和概念演变过程中，出现了许多创新和出版物，基因驱动科学引起了公众的广泛关注。新的爆炸现在是时候使用系统的方法来研究这个新的科学组织，该系统使知识在这个科学领域得以增长。基因工程科学中的知识如何进步，以及我们如何通过对语言、社区和社交媒体的分析来理解什么是“基因驱动”。 实际上，这项研究将推动多个领域的发展，并通过以下方式加深对知识和复杂系统的理解。通过会议、博客等方式公开传播结果，从而吸引广泛的受众该项目将记录出版模式、协作关系、社交媒体影响，然后将这些因素结合起来，将知识系统特征化为信号检测算法，以预测更大的 CRISPR-Cas9 计算科学的未来轨迹。分析将对基因驱动技术中科学知识的结构、动态和演化提供深入、完整的描述。此外，该项目还将分析公众舆论如何通过社会媒体影响基因工程技术的进步。此外，时间序列分析。多层话语的分析将实现协作关系的历时连接映射，并跟踪语言变异和变化，突出显示可能出现歧义的语言，从而改进和创建更具凝聚力的科学语言，总体上描绘了结构、动态和演变。科学复杂性的新爆发期间的科学知识可以揭示导致以下因素的因素：（1）改善科学交流，（2）减少科学进步，（3）新知识，以及（4）新的合作关系。表征当前可以影响科学知识的技术、方法论和社会背景的研究成果将在会议、博客、GitHub 上发布，并通过传统和数字出版物进行共享。学术成果将通过不同的网站提供，并尽可能多地进行共享。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。