IntBIO COLLABORATIVE RESEARCH: Integrating fossils, genomics, and machine learning to reveal drivers of Cretaceous innovations in flowering plants

IntBIO 协作研究：整合化石、基因组学和机器学习，揭示白垩纪开花植物创新的驱动因素

• 批准号: 2217116
• 负责人: Stephen Smith
• 金额: $ 103.11万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217116&HistoricalAwards=false
• 关键词: IntBIO; COLLABORATIVE; RESEARCH; Integrating; fossils

The Tree of Life is marked by short periods of rapid innovation where groups emerge with dramatically altered forms and diversify quickly. This has happened multiple times throughout geologic history including with the rise of birds, mammals, the transition of plants from water to land, and the origination of flowering plants. The rapid emergence and diversification of flowering plants in particular represents one of the most remarkable episodes in the history of life on earth. However, while fundamental to understanding the ecology and evolution of modern ecosystems, this episode remains unexplained and leads to one of the grand challenges in the biological sciences – determining what processes may be responsible for such rapid changes in form and function across the Tree of Life. A major impediment to addressing this question is the availability of data and methods for analyzing those data. The goal of this study is to use and develop new machine learning approaches to gathering data for both fossil and living plant species and to use these data to help develop new techniques. These techniques will help identify what contributed to the rapid change in plants that resulted in their dominance in the environment today. This project will train undergraduates, graduate students, and postdoctoral fellows in machine learning methods, evolutionary biology, and techniques for working with both fossil and living specimens. The project will also include resource development and training for middle schoolers, high schoolers, undergraduates, and the broader research community.This project aims to evaluate the evolutionary processes underlying the emergence of innovation, using flowering plants as a case study. Specifically, the project will examine Cretaceous radiations of flowering plants characterized by rapid evolution, a rich fossil record, and the origin of innovations and lineages of great ecological significance. The central goals of the proposed research are to (a) generate a large morphological dataset for flowering plants using novel machine learning methods, (b) develop new statistical methods for modeling evolution, and (c) use these advances in data collection and methods to identify the processes that led to the episodic and rapid emergence of novelty across the Tree of Life. Collectively, these new developments in machine learning techniques, morphological data collection, and analytical techniques for addressing evolutionary processes will be potentially transformative to several fields including the biological sciences, computational biology, and machine learning. The large scope and scale of this project, together with its highly integrative nature, creates the potential to address one of the most important standing questions in evolutionary biology.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.

生命之树的特点是快速创新的短暂时期，群体的出现形式发生了戏剧性的变化，并迅速多样化。在整个地质史上，这种情况已经发生过多次，包括鸟类、哺乳动物的崛起，植物从水到陆地的过渡，以及开花植物的起源。特别是开花植物的迅速出现和多样化，是地球生命史上最引人注目的事件之一。然而，尽管这是理解现代生态系统生态学和进化的基础，但这一事件仍然没有得到解释，并导致了生物科学中的重大挑战之一--确定哪些过程可能导致整个生命树的形式和功能发生如此迅速的变化。解决这一问题的一个主要障碍是缺乏数据和分析这些数据的方法。这项研究的目标是使用和开发新的机器学习方法来收集化石和活植物物种的数据，并使用这些数据来帮助开发新技术。这些技术将有助于确定是什么导致了植物的快速变化，导致它们在今天的环境中占据主导地位。这个项目将对本科生、研究生和博士后研究员进行机器学习方法、进化生物学以及处理化石和活标本的技术方面的培训。该项目还将包括针对初中生、高中生、本科生和更广泛的研究社区的资源开发和培训。该项目旨在以开花植物为例，评估创新涌现背后的进化过程。具体地说，该项目将研究白垩纪开花植物的辐射，其特征是快速进化，丰富的化石记录，以及具有重大生态意义的创新和谱系的起源。拟议研究的中心目标是：(A)使用新的机器学习方法生成开花植物的大型形态数据集，(B)开发新的统计方法来模拟进化，以及(C)利用这些数据收集和方法方面的进展来识别导致整个生命树上的新奇事物断断续续和迅速出现的过程。总的来说，机器学习技术、形态数据收集和用于处理进化过程的分析技术的这些新发展将潜在地改变包括生物科学、计算生物学和机器学习在内的几个领域。该项目的大范围和规模，加上其高度综合的性质，创造了解决进化生物学中最重要的长期问题之一的可能性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。