EAGER: Shared Principles of Adaptive Learning - anticipatory behavior in Halobactetrium salinarum

EAGER：适应性学习的共享原则 - Halobactetrium salinarum 的预期行为

基本信息

批准号：
1237267
负责人：
Nitin Baliga
金额：
$ 30万
依托单位：
Institute for Systems Biology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-01 至 2014-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1237267&HistoricalAwards=false
关键词：
EAGER Shared Principles Adaptive Learning

项目摘要

Intellectual merit. Biological systems use memory of previous experiences to anticipate future environmental changes. In the literature this is called "anticipatory behavior". These experiments will be performed in Halobacterium salinarum, a tractable model organism that has adapted to a complex and dynamically changing natural environment. Individual environmental factors such as oxygen, temperature, pH do not occur in isolation but rather, in a temporally coupled and non-random manner. This project seeks to understand the rate at which microorganisms evolve novel anticipatory behavior as they encounter a new environment. This understanding will have significant implications on wide-ranging issues including predicting the consequences of climate change on microbial communities in the oceans, as well as strategies to prevent pathogens from evading the immune system.Broader impacts. The objective is to significantly expand the already highly successful "Networks in Biology" high school educational module by incorporating statistical modeling and model inference lessons. It will be reinforced that models are as good as the data they are based upon, and model predictions must be assessed with rigorous statistics in order to avoid misinterpretation. These educational activities will be designed and implemented through collaborations between senior scientists, postdoctoral fellows, high school student interns from disadvantaged backgrounds, and educators, such as high school teachers and curriculum developers.

智力优点。生物系统利用以前的经验的记忆来预测未来的环境变化。在文献中，这称为“预期行为”。这些实验将在Salinarum中进行，这是一种适合复杂且动态变化的自然环境的可进行的模型生物。单个环境因素（例如氧，温度，pH）不是分离出来的，而是以时间耦合和非随机方式发生。该项目试图了解微生物在遇到新环境时发展新颖的预期行为的速度。这种理解将对广泛的问题产生重大影响，包括预测气候变化对海洋中微生物群落的后果，以及防止病原体逃避免疫系统的策略。目的是通过结合统计建模和模型推理课程来显着扩大已经非常成功的“生物学”高中教育模块的“网络”。将加强模型与基于数据一样好，并且必须使用严格的统计数据来评估模型预测，以避免误解。这些教育活动将通过高级科学家，博士后研究员，来自弱势背景的高中生实习生以及高中教师和课程开发人员等教育者之间的合作设计和实施。