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

智力上的优点。 生物系统利用对先前经历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”。 这些实验将在盐生盐杆菌（Halobacterium salinarum）中进行，盐生盐杆菌是一种易于处理的模式生物，已适应复杂且动态变化的自然环境。个别环境因素，如氧气，温度，pH值不会孤立地发生，而是以时间耦合和非随机的方式发生。 该项目旨在了解微生物在遇到新环境时进化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。 我们的目标是通过纳入统计建模和模型推理课程，显着扩大已经非常成功的“生物学网络”高中教育模块。它将强调，模型是一样好的数据，他们是基于，模型的预测必须进行评估，以严格的统计，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学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。

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.