CAREER: Quantitative Analysis of Endocrine Disrupting Chemicals

EDC对细胞信号传导和行为影响的实验和计算分析是解决这一具有挑战性问题的创新方法。迭代实验模型实验设计将能够发现特别受EDC处理影响的机制。此外，所提出的在计算机上生成信号数据集的方法将导致新的建模技术的发展和原始模型的改进，以处理暴露于EDC组合的复杂和多变量问题。最终目标是建立能够解释内质网信号在正常和疾病状态下的复杂作用的模型，这是解决妇女健康问题的重要组成部分。EDCs的影响是一个全球关注的问题，提出的系统生物学方法对于解决这个问题的复杂性至关重要。开发计算机模型来识别EDCs影响的细胞机制并预测暴露于组合的结果是防止EDCs有害影响的重要一步。此外，实验数据收集和模型开发和分析的概述框架基本上可以应用于任何生物网络，为雌激素信号传导对细胞行为的影响的未来研究提供基础。将非博士学位授予机构的学生纳入研究实验室，并向K-12学生提供服务，这将有助于培养下一代在数学和生物学方面受过训练的学生，并将使在实验室受过训练的学生和研究员成为有效的跨学科研究人员和教师。

0951613 - KreegerThe experimental and computational analysis of EDC impact on cellular signaling and behavior is an innovative approach to address this challenging problem. The iterative experiment model experiment design will enable the discovery of mechanisms that are specifically impacted by EDC treatment. Additionally, the proposed methods to generate signaling data sets in silico will lead to the development of new modeling techniques and refinement of the original model to handle the complicated and multivariate problem of exposure to EDC combinations. The ultimate goal is to develop models that can interpret the complicated role of ER signaling in both normal and disease states, which is an essential component of addressing women's health concerns. The effects of EDCs are a global concern and the proposed systems biology methods are essential to contend with the complexity of the problem. Development of in silico models to identify cellular mechanisms that EDCs impact and predict the result of exposure to combinations is an essential step towards preventing harmful effects from EDCs. Additionally, the outlined framework of experimental data collection and model development and analysis can be applied to essentially any biological network, providing the basis for future studies of the impact of estrogenic signaling on cellular behavior. The outlined proposal to incorporate students from non-PhD granting institutions into the research laboratory and to perform outreach to K-12 students will help to develop the next generation of students trained at the math-biology interface, and will equip students and fellows trained in the laboratory to be effective interdisciplinary researchers and teachers.