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

知识是如何在基础科学、生物技术和工业的交叉点上产生的？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相结合。基因驱动是基因工程，使得它们以大于典型孟德尔百分之五十的频率优先遗传。在20世纪60年代开始的基因驱动的历史和概念演变期间，有许多创新和出版物。沿着，基因驱动科学发展了相当大的公众关注，新科学家的爆炸，以及主题讨论方式的变化。现在是时候看看这个新的科学组织使用系统的方法来描述系统，使知识在这个科学领域的增长。该项目将在基因工程科学的知识进步方面开辟新天地，以及我们如何通过分析语言，社区和社交媒体来理解“基因驱动”。实际上，这项研究将推动多个领域的发展，并通过会议、博客、GitHub和学术出版物公开传播结果，使广大受众能够更深入地了解知识和复杂系统。该项目将记录出版模式、合作关系、社交媒体影响力，然后将这些因素联合收割机组合起来，将知识系统表征为信号检测算法，以预测更大的CRISPR-Cas9科学的未来轨迹。计算分析的结果将提供基因驱动技术中发现的科学知识的结构，动力学和进化的深入和完整的表征。此外，该项目还将分析公众舆论如何通过社交媒体和新闻平台影响基因工程技术的进展。此外，对话语的多个层面进行时间序列分析将能够对协作关系进行历时连接映射，并跟踪语言的变化和变化，突出可能出现歧义的语言。因此，改进和创造更有凝聚力的科学语言。总的来说，描述科学复杂性新爆发期间科学知识的结构、动态和演变可以揭示可能导致以下结果的因素：（1）改善科学交流，（2）减少科学进步，（3）新知识，（4）新的合作关系。因此，描述当前的技术，方法和社会背景，可以影响科学知识。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分享。该奖项反映了NSF的法定使命，并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。

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.