NSF Postdoctoral Fellowship in Biology FY 2021: Establishing parametric variability as a driver of plasticity across genotypes with diverse life histories ...

2021 财年 NSF 生物学博士后奖学金：将参数变异性建立为具有不同生命史的基因型可塑性的驱动因素......

• 批准号: 2109790
• 负责人: Renee Dale
• 金额: $ 21.6万
• 依托单位: Dale, Renee
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2109790&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2021. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Renee Dale is "Establishing parametric variability as a driver of plasticity across genotypes with diverse life histories through a novel mathematical modeling framework". The host institution for the fellowship is the Donald Danforth Plant Science Center and the sponsoring scientists are Dr. Ivan Baxter and Dr. Shankar Mukherji.Changes in climate increase the frequency and severity of extreme environmental conditions. Organisms with the ability to be flexible (“plasticity”) can rapidly adapt to these changes. Plants need to be especially flexible since they cannot move if their environment suddenly changes. Identifying the mechanisms that allow flexibility is increasingly important. Due to the amount of information needed to understand plasticity throughout the plant life cycle, models and computational approaches are needed. A new method is proposed to identify ways that plants are flexible using hundreds of plants coming from diverse environments. It is hypothesized that plants from extreme environments will be more flexible, with more modes of growth. Modeling will be used to identify different modes of growth and understand the underlying mechanisms. The results could be used in plant breeding programs to improve crop resilience and the approach can be used to understand the growth of other species. The proposed research will provide the PI with training and development opportunities in mathematics (biological stochasticity), biology (quantitative genetics, ecology, and plant physiology), and computation (Python coding and high-performance computing). The impact of this work will be broadened through the development and dissemination of an educational video game, intended to improve diversity and inclusion of under-represented individuals. The video game will introduce these concepts to high school students in a low-stress way, circumventing the negative social perceptions of math.A novel mathematical modeling framework will be developed to identify processes driving plasticity across hundreds of plant genotypes with diverse eco-evolutionary backgrounds. A family of mathematical models of plant physiology and growth processes will be applied. Model parameters will be estimated using Bayesian parameter estimation, providing parametric distributions within and between genotypes. This will provide a conceptual framework to understand plasticity by integrating mathematical, computational, and biological theory. Processes driving plasticity have agricultural significance, as the ability of crops to adapt and respond to increasing environmental stress is paramount. Model parameters will be mapped to genomic loci, which could be used in breeding programs to test model predictions that such loci will confer plasticity, and to improve crop plants.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.

该行动为2021财年的NSF植物基因组博士后生物学研究奖学金提供资金。该研究金支持研究员在东道实验室的研究和培训计划，研究员还提出了扩大生物学参与的计划。Renee Dale的研究和培训计划的标题是“通过新颖的数学建模框架，建立参数变异性作为具有不同生活史的基因型可塑性的驱动因素”。该研究金的主办机构是唐纳德·丹佛斯植物科学中心，赞助科学家是伊万·巴克斯特博士和尚卡尔·穆克吉博士。具有灵活性（“可塑性”）的生物体可以迅速适应这些变化。植物需要特别灵活，因为如果环境突然改变，它们不能移动。确定允许灵活性的机制越来越重要。由于需要大量的信息来了解整个植物生命周期的可塑性，模型和计算方法是必要的。提出了一种新的方法来识别植物的方式是灵活的，使用数百种植物来自不同的环境。据推测，来自极端环境的植物将更加灵活，具有更多的生长模式。建模将被用来识别不同的增长模式，并了解潜在的机制。研究结果可用于植物育种计划，以提高作物的弹性，该方法可用于了解其他物种的生长。拟议的研究将为PI提供数学（生物随机性），生物学（定量遗传学，生态学和植物生理学）和计算（Python编码和高性能计算）的培训和发展机会。将通过开发和传播教育视频游戏扩大这项工作的影响，旨在改善代表性不足的个人的多样性和包容性。该游戏将以一种低压力的方式向高中生介绍这些概念，规避社会对数学的负面看法。一个新颖的数学建模框架将被开发出来，以识别数百种具有不同生态进化背景的植物基因型的可塑性驱动过程。将应用植物生理学和生长过程的数学模型。将使用贝叶斯参数估计法估计模型参数，提供基因型内和基因型间的参数分布。这将提供一个概念框架来理解可塑性，通过整合数学，计算和生物学理论。驱动可塑性的过程具有农业意义，因为作物适应和应对日益增加的环境压力的能力至关重要。模型参数将被映射到基因组位点，这可以在育种计划中使用，以测试模型预测，这些位点将赋予可塑性，并改善作物plants.This奖项反映了NSF的法定使命，并已被认为是值得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的支持。