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:
- 金额:$ 30万
- 依托单位:
- 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:
- 关键词:
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.
知识价值。生物系统利用以往经验的记忆来预测未来的环境变化。在文献中,这被称为“预期行为”。这些实验将在盐盐杆菌中进行,盐盐杆菌是一种适应复杂和动态变化的自然环境的易处理的模式生物。个别环境因素,如氧气、温度、pH值,不是孤立地发生的,而是以一种时间耦合和非随机的方式发生的。该项目旨在了解微生物在遇到新环境时进化出新的预期行为的速度。这一认识将对广泛的问题产生重大影响,包括预测气候变化对海洋微生物群落的影响,以及防止病原体逃避免疫系统的策略。更广泛的影响。目标是通过整合统计建模和模型推理课程,显著扩展已经非常成功的“生物学网络”高中教育模块。我们将强调,模型与它们所依据的数据一样好,模型预测必须用严格的统计数据进行评估,以避免误解。这些教育活动将通过资深科学家、博士后、来自弱势背景的高中生实习生和教育工作者(如高中教师和课程开发人员)之间的合作来设计和实施。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Nitin Baliga其他文献
Nitin Baliga的其他文献
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{{ truncateString('Nitin Baliga', 18)}}的其他基金
A systems biology framework to uncover rules governing robustness of a microbial community
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2042948 - 财政年份:2021
- 资助金额:
$ 30万 - 项目类别:
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2050550 - 财政年份:2021
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$ 30万 - 项目类别:
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2105570 - 财政年份:2021
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$ 30万 - 项目类别:
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1616955 - 财政年份:2016
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$ 30万 - 项目类别:
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1565166 - 财政年份:2016
- 资助金额:
$ 30万 - 项目类别:
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Model-guided systems re-engineering of Chlamydomonas reinhardtii
模型引导的莱茵衣藻系统再造
- 批准号:
1606206 - 财政年份:2016
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Bilateral BBSRC-NSF/BIO: Identifying Mechanisms for Environmental Adaptation in Bacteria
双边 BBSRC-NSF/BIO:确定细菌环境适应机制
- 批准号:
1518261 - 财政年份:2015
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
Interplay of Transcriptional, Translational Regulatory Mechanisms and Kinetics of an Environmental Response
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- 批准号:
1330912 - 财政年份:2013
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
ABI Innovation: An approach to construct a systems-scale predictive model of a gene regulatory network complete with mechanisms at single nucleotide resolution
ABI Innovation:一种构建基因调控网络的系统规模预测模型的方法,该模型具有单核苷酸分辨率的机制
- 批准号:
1262637 - 财政年份:2013
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
Design and Implementation of Effective Solutions for Archiving and Processing Systems Biology Data: Research Integrated with an Ongoing High School Education Program.
归档和处理系统生物学数据的有效解决方案的设计和实施:研究与正在进行的高中教育计划相结合。
- 批准号:
0640950 - 财政年份:2007
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
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