权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: CDS&E: Elucidating Binding using Bayesian Inference to Integrate Multiple Data Sources

合作研究：CDS

基本信息

批准号：
1904822
负责人：
John Chodera
金额：
$ 25.5万
依托单位：
Sloan Kettering Institute For Cancer Research
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1904822&HistoricalAwards=false
关键词：
Collaborative Research CDS&E Elucidating Binding

项目摘要

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professors David Minh and John Chodera, and their groups at (respectively) the Illinois Institute of Technology and the Sloan Kettering Institute for Cancer Research, are developing statistical methods to study binding interactions between molecules. These interactions play critical roles in biology and materials technology. Full understanding of binding interactions can require integrating large amounts of data collected using multiple analytical instruments and experimental protocols. Existing statistical methods and software do not fully integrate data from multiple sources to produce useful knowledge. The Minh/Chodera team is pioneering the use of a new approach (a "Bayesian network") as a general framework for analyzing chemical measurement data from multiple instruments and protocols and for designing new experiments. The framework is usable for both small laboratory experiments and the massive datasets generated by automated instrumentation. The software (including a straightforward user interface) is utilized to teach the underlying principles in related courses, and will be made freely available online, along with tutorials and clear documentation. The Minh/Chodera team is developing chemometric methods and software for analyzing data related to binding. They are working to fuse data from diverse methods, including isothermal titration calorimetry (ITC), surface plasmon resonance (SPR), absorbance, fluorescence, and X-ray solution scattering. Key features of the software include automated parameter determination for physical binding models, and uncertainty propagation and quantification for model parameters. The research team also incorporates automated and principled model selection and hypothesis testing, and Bayesian experimental design to maximize acquisition of new information while minimizing cost. The software automatically constructs Bayesian networks that consider all sources of experimental error (e.g. dispensing, weighing, transfer, and measurement) for any experiment described by the Autoprotocol machine-readable standard. The software then performs Bayesian inference to weigh evidence for competing physical models, obtain credible intervals for thermodynamic and kinetic parameters, and propose new experiments. Robotic experiments, statistical inference, and Bayesian experimental design can be efficiently iterated to reduce model ambiguity and improve parameter precision. The team is using the software to advance knowledge of cooperativity between binding sites. A test application focuses on physiochemical properties that dictate site affinities and selectivities in human serum albumin.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.

在化学系化学测量与成像项目的支持下，伊利诺伊理工学院的David Minh教授和斯隆凯特琳癌症研究所的John Chodera教授及其团队正在开发统计方法来研究分子之间的结合相互作用。这些相互作用在生物学和材料技术中起着至关重要的作用。充分了解结合相互作用可能需要整合使用多种分析仪器和实验方案收集的大量数据。现有的统计方法和软件不能完全整合来自多个来源的数据来产生有用的知识。Minh/Chodera团队正在率先使用一种新方法（“贝叶斯网络”）作为分析来自多种仪器和协议的化学测量数据以及设计新实验的一般框架。该框架既可用于小型实验室实验，也可用于自动化仪器生成的大量数据集。该软件（包括一个简单的用户界面）用于在相关课程中教授基本原理，并将与教程和清晰的文档一起在线免费提供。Minh/Chodera团队正在开发化学计量学方法和软件，用于分析与结合相关的数据。他们正在努力融合来自不同方法的数据，包括等温滴定量热法（ITC）、表面等离子体共振（SPR）、吸光度、荧光和x射线溶液散射。该软件的主要功能包括物理绑定模型的自动参数确定，模型参数的不确定性传播和量化。研究团队还结合了自动化和原则性的模型选择和假设检验，以及贝叶斯实验设计，以最大限度地获取新信息，同时最小化成本。该软件自动构建贝叶斯网络，考虑所有实验误差的来源（例如分配，称重，转移和测量）的任何实验由Autoprotocol机器可读标准描述。然后，该软件执行贝叶斯推理来权衡相互竞争的物理模型的证据，获得热力学和动力学参数的可信区间，并提出新的实验。机器人实验、统计推断和贝叶斯实验设计可以有效地迭代，以减少模型模糊度，提高参数精度。该团队正在使用该软件来提高对结合位点之间协作性的认识。一个测试应用侧重于物理化学性质，决定了在人血清白蛋白的亲和力和选择性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。