Collaborative Research: From ecological dynamics to adaptive evolution during the origin of life

合作研究：从生态动力学到生命起源期间的适应性进化

• 批准号: 2218817
• 负责人: David Baum
• 金额: $ 112.65万
• 依托单位: University of Wisconsin-Madison
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218817&HistoricalAwards=false
• 关键词: Collaborative; Research; ecological; dynamics; adaptive

This study explores the idea that the earliest adaptive evolution operated on self-amplifying cycles of chemical reactions, in contrast to most prior studies, which have assumed that evolution requires elements that carry information in the form of nucleic acids (DNA or RNA). Understanding how the origin of life came about is important because it shapes our expectations about life on earth as it exists today and because it aids us in making predictions about whether and in what form life may exist elsewhere in the universe. This project combines theoretical and laboratory research to address the fundamental question of how mixtures of chemicals could self-organize into evolving systems, as must have occurred during the origin of life. The project tests the hypothesis that interactions among multiple such cycles can change over time, similar to the way in which natural ecosystems change as species invade or go extinct, and that such chemical ecological change can gradually morph into more familiar gene-based evolution. In addition to helping explain how life came to be, the work will likely yield new computational tools for ecological and biochemical research and has the potential to lead to new strategies for chemical engineering. Moreover, the work contributes to breaking down boundaries between the fields of evolutionary biology, ecology, computer science, and systems chemistry. The research will bring together and train a diverse team of researchers whose primary expertise is in biology, chemistry, physics, or computer science. Lastly, this project will produce a set of educational resources, designed to make this new perspective on the interplay between chemistry, ecology, and evolution accessible to students across these fields.The project entails three strands of theoretical research. First, the researchers will develop mathematical and computational tools for analyzing chemical or ecological networks and detecting adaptive evolutionary dynamics. Second, they will conduct analyses of digital ecosystems, composed of numerous digital species interacting in a virtual environment, to understand how patterns of species-species interaction and the spatial structure of the environment affect the capacity for ecological change to resemble evolution. Third, the team will use computer simulations of realistic chemical reaction networks to see if similar factors affect the emergence of adaptive behaviors in chemistry and ecology. These theoretical studies will be combined with laboratory experiments in which the chemical composition of complex mixtures that are driven out of equilibrium by periodic dilution using a flux of chemical food will be tracked over time using liquid-chromatography mass-spectrometry. Statistical analyses of the resulting time courses will be used to detect adaptive behaviors.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.

这项研究探索了这样一种观点，即最早的适应性进化是在化学反应的自我放大循环上进行的，而以前的大多数研究都假设进化需要携带核酸(DNA或RNA)形式的信息的元素。了解生命的起源是很重要的，因为它塑造了我们对地球上今天存在的生命的预期，也因为它帮助我们预测宇宙中其他地方是否存在生命，以及生命可能以什么形式存在。该项目结合了理论和实验室研究，以解决化学混合物如何自组织成进化系统的基本问题，这必须发生在生命起源期间。该项目测试了这样一个假设，即多个这样的周期之间的相互作用可以随着时间的推移而改变，类似于自然生态系统随着物种入侵或灭绝而改变的方式，并且这种化学生态变化可以逐渐演变为更熟悉的基于基因的进化。除了帮助解释生命是如何形成的，这项工作还可能为生态和生物化学研究提供新的计算工具，并有可能导致化学工程的新战略。此外，这项工作有助于打破进化生物学、生态学、计算机科学和系统化学领域之间的界限。这项研究将汇集和培训一支不同的研究团队，他们的主要专长是生物、化学、物理或计算机科学。最后，这个项目将产生一套教育资源，旨在使这些领域的学生能够从这个新的角度了解化学、生态和进化之间的相互作用。该项目需要三个理论研究。首先，研究人员将开发数学和计算工具，用于分析化学或生态网络，并检测适应性进化动力学。其次，他们将对数字生态系统进行分析，这些数字生态系统由许多数字物种在虚拟环境中相互作用组成，以了解物种-物种相互作用的模式和环境的空间结构如何影响生态变化类似进化的能力。第三，该团队将使用计算机模拟真实的化学反应网络，看看类似的因素是否会影响化学和生态学中适应行为的出现。这些理论研究将与实验室实验相结合，在实验室实验中，将使用液-色质谱跟踪复杂混合物的化学成分，这些混合物因使用化学食品的流量而被周期性稀释而失去平衡。对所产生的时间进程的统计分析将被用来检测适应性行为。这一奖项反映了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。

项目成果

Action functional gradient descent algorithm for estimating escape paths in stochastic chemical reaction networks

用于估计随机化学反应网络中逃逸路径的动作函数梯度下降算法

• DOI: 10.1103/physreve.107.034305
• 发表时间: 2023
• 期刊: Physical Review E
• 影响因子: 2.4
• 作者: Gagrani, Praful;Smith, Eric
• 通讯作者: Smith, Eric