Collaborative Research: Statistical Modeling of Mechanosensing by Cell Surface Receptors

合作研究：细胞表面受体机械传感的统计模型

• 批准号: 1660477
• 负责人: Ying Hung
• 金额: $ 22万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660477&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Modeling; Mechanosensing

This is a collaborative team between Georgia Tech and Rutgers, which consists of two statisticians and one biomedical engineer. It can serve as a role model on how rigorous statistical methods are used to tackle important problems in biology. The proposed research will provide a better understanding of a complex biological signaling process called cell adhesion, which can pave the way to future clinical interventions. The proposed statistical approaches are readily applicable to a variety of scientific disciplines and will have immediate impact on accelerating discoveries in numerous fields involving complex experiments. This research will facilitate a new mode of intellectual interaction between statistics and cell biology. Some outreach programs are proposed for educating the next generation of mathematical biologists and biometricians. The team is committed to creating a diverse environment in their laboratories in terms of race, gender and national origin. The research will also provide an excellent opportunity to recruit students from underrepresented groups to participate in projects at the interface between biology and statistics. Cells use their surface receptors to sense the environment by engaging ligands on neighboring cells or in the extracellular matrix. This research focuses on understanding how receptor-ligand engagement induces cellular response, which is critical to unraveling many disease pathologies and can provide the groundwork for clinical intervention. With a few exceptions, the mechanisms behind signaling initiation remain elusive for most receptors. The innovation of this project is in combining the single-molecule experiments with statistical modeling to extract new readouts required for understanding the complex signaling processes. New frameworks based on novel modifications to Gaussian process (GP) models and new regime-switching models are proposed to quantify the memory effect in receptor/ligand binding. To rigorously quantify effects of different putative triggering parameters on cell signaling, a new varying-coefficient Cox model is proposed. The proposed statistical models will be validated with experimental data from the lab and modified if warranted. The proposed studies are significant because the sophisticated statistical modeling will greatly empower the understanding of the impact of mechanical forces on two biologically important and clinically relevant receptors in the human body: the platelet glycoprotein Ib and the T cell receptor. From the statistical point of view, the proposed GP model for binary data provides an analogy to the standard GP models with interpolation property, which can potentially lead to further advances in spatial statistics. The new regime-switching models borrow strength across different time series, which can have significant impacts on longitudinal study. The new Cox model allows the effects of covariates to vary over time and incorporates the between subject variation. It can open up new avenues for studying problems in various fields involving survival or failure analysis, and energize both theoretical and applied research.

这是格鲁吉亚理工学院和罗格斯大学之间的一个合作团队，由两名统计学家和一名生物医学工程师组成。它可以作为一个榜样，说明如何严格的统计方法来解决生物学中的重要问题。这项研究将更好地了解称为细胞粘附的复杂生物信号传导过程，这可以为未来的临床干预铺平道路。所提出的统计方法很容易适用于各种科学学科，并将对加速涉及复杂实验的许多领域的发现产生直接影响。这项研究将促进统计学和细胞生物学之间的智能互动的新模式。一些推广计划提出了教育下一代的数学生物学家和生物统计学家。该团队致力于在他们的实验室中创造一个种族，性别和民族血统方面的多元化环境。这项研究还将提供一个极好的机会，从代表性不足的群体中招募学生参加生物学和统计学之间的接口项目。细胞使用其表面受体通过接合邻近细胞或细胞外基质中的配体来感知环境。这项研究的重点是了解受体-配体结合如何诱导细胞反应，这对揭示许多疾病病理至关重要，并可以为临床干预提供基础。除了少数例外，大多数受体的信号启动机制仍然难以捉摸。该项目的创新之处在于将单分子实验与统计建模相结合，以提取理解复杂信号传导过程所需的新读数。新的框架的基础上提出了新的修改高斯过程（GP）模型和新的政权切换模型来量化的记忆效应受体/配体结合。为了严格量化不同假定触发参数对细胞信号传导的影响，提出了一种新的变系数考克斯模型。将使用实验室的实验数据对拟定的统计模型进行验证，并在必要时进行修改。所提出的研究是重要的，因为复杂的统计建模将大大增强对机械力对人体中两种生物学重要和临床相关受体的影响的理解：血小板糖蛋白Ib和T细胞受体。从统计的角度来看，所提出的二进制数据GP模型提供了与具有插值属性的标准GP模型的类比，这可能会导致空间统计学的进一步发展。新的机制转换模型在不同的时间序列上借用了力量，这可能对纵向研究产生重大影响。新的考克斯模型允许协变量的影响随时间变化，并纳入受试者之间的变异。它可以为研究涉及生存或失败分析的各个领域的问题开辟新的途径，并激励理论和应用研究。

项目成果

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• DOI: 10.1214/18-sts677
• 发表时间: 2019
• 期刊: Statistical Science
• 影响因子: 5.7
• 作者: Stein, Michael L.;Hung, Ying
• 通讯作者: Hung, Ying

INDEEDopt: a deep learning-based ReaxFF parameterization framework

• DOI: 10.1038/s41524-021-00534-4
• 发表时间: 2021-05
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: M. Sengul;Yao Song;Nadire Nayir;Yawei Gao;Ying Hung;Tirthankar Dasgupta;A. V. van Duin