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MODULUS: Data-Driven Structured Population Modeling for Prediction of Complex Photosynthetic Phenotypes

MODULUS：数据驱动的结构化群体模型，用于预测复杂的光合表型

基本信息

批准号：
2054085
负责人：
David Bortz
金额：
$ 78.7万
依托单位：
University of Colorado at Boulder
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：
2021-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054085&HistoricalAwards=false
关键词：
MODULUS Data Driven Structured Population

项目摘要

Populations can be divided based on the properties of individual members, including features such as size and age. Mathematical models describing how features are spread across a population are called “structured population models” and are widespread in the biological sciences. In this project investigators will develop a novel algorithm and software to automatically create structured population models. Mathematical models will be used to study photosynthesis in bacteria with the goal of improving photosynthetic efficiency. This work could potentially lead to substantial improvements in bioreactors, water treatment, and biofuel production. As part of the Broader Impact activities investigators will provide training opportunities for postdoctoral, graduate and undergraduate students. Outreach activities for high school students and the larger community will also be conducted through their institution’s Summer Multicultural Access to Research Training (SMART) program and in partnership with the CU Society for Women in Engineering(SWE) organizatRecent advances in sparse-regression based data-driven modeling allow for the creation of mathematical models directly from data, reducing reliance on conventional single candidate model creation, simulation, and validation methodologies. A central premise of this research is that the applied framework will yield novel structured models, leading to biological insight into population-driven adaptations that impact the use of light energy for the conversion of CO2 into biomolecules critical for life on earth. The investigators will extend the data-driven framework to create a rigorous methodology for the discovery of structured population models in photosynthetic cyanobacteria. Advances in computational tools and models will inform the development of experimental studies to investigate the relationship between population structure and complex photosynthetic phenotypes yielding maximal bacterial growth. Experimental studies will leverage mutant strains and use long-term time-lapse imaging and carboxysome tracking to investigate hypotheses. Research-driven Broader Impact activities will include interdisciplinary training of students and outreach to high school students and the broader community through institutional and professional society (SWE) partnerships.This award is co-funded by the Systems and Synthetic Biology program in the Division of Molecular and Cellular Biosciences and the Mathematical Biology program in the Division of Mathematical 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.

种群可以根据个体成员的属性进行划分，包括大小和年龄等特征。描述特征如何在群体中传播的数学模型被称为“结构化群体模型”，并且在生物科学中广泛存在。在这个项目中，研究人员将开发一种新的算法和软件来自动创建结构化的人口模型。数学模型将用于研究细菌的光合作用，目的是提高光合效率。这项工作可能会导致生物反应器，水处理和生物燃料生产的重大改进。作为更广泛影响活动的一部分，调查人员将为博士后、研究生和本科生提供培训机会。针对高中生和更大社区的外联活动也将通过他们机构的夏季多元文化研究培训（SMART）计划并与CU工程妇女协会（SWE）组织合作进行。基于稀疏回归的数据驱动建模的最新进展允许直接从数据创建数学模型，减少对传统单一候选模型创建的依赖，模拟和验证方法。这项研究的一个中心前提是，应用框架将产生新的结构化模型，从而使生物学深入了解人口驱动的适应性，这些适应性影响光能的使用，将二氧化碳转化为对地球上生命至关重要的生物分子。研究人员将扩展数据驱动的框架，以创建一个严格的方法，用于发现光合蓝藻中的结构化种群模型。计算工具和模型的进步将为实验研究的发展提供信息，以调查种群结构与产生最大细菌生长的复杂光合表型之间的关系。实验研究将利用突变菌株，并使用长期的延时成像和羧基追踪来研究假设。研究驱动的更广泛的影响活动将包括学生的跨学科培训和推广到高中生和更广泛的社区通过机构和专业社会（SWE）的伙伴关系。由分子和细胞生物科学部的系统和合成生物学计划以及数学科学部的数学生物学计划资助。该奖项反映了NSF的法定使命，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。