Summer Student Research on the Application of Boolean Analysis

项目总结/摘要 尽管50年来对人类结肠隐窝中干细胞群体的特征进行了广泛的研究， 仍然没有一个明确的定义细胞，维持结肠隐窝。我们试图回答这个问题 随着单细胞RNA测序（scRNA-seq）技术的兴起， 单个干细胞和祖细胞的转录组。我们有一种新的方法可以预测分化 使用大患者衍生基因无偏系统生物学观点和数学模型的层次结构 表达式数据集。我们有数学模型可以预测终末分化的细胞。的 我们使用的数学原理是基于布尔蕴涵逻辑，这种逻辑还没有普遍应用于 研究组织细胞群。布尔分析分配一个参数（例如基因的RNA水平），只有两个 价值观，即，高/低、1/0或正/负。应用布尔原理，可以确定 任何一对基因的表达水平之间的关系。布尔分析能够识别 人类组织和跨物种的基本基因表达关系。这个的主要目标 一个提议是使用布尔蕴涵关系来实现人类结肠组织的单细胞分析。基于 根据我们的初步数据，总体假设是布尔原理可以用于具体的 表征人结肠组织中的细胞类型群体。本科生参与这个干细胞 计算生物学研究可以使他们受益于生物医学领域的跨学科研究， 并推进他们的职业生涯。

Project Summary/Abstract Despite 50 years of extensive investigations to characterize the stem cell population in human colon crypts, we still do not have a clear definition of cells that maintain the colon crypts. We attempt to answer this question with emerging technology of single-cell RNA sequencing (scRNA-seq), which can provide insight into detailed transcriptomes of individual stem and progenitor cells. We have a new method that can predict differentiation hierarchy using unbiased systems biology perspective and mathematical models of large patient-derived gene expression datasets. We have mathematical models that can predict terminally differentiated cells. The mathematical principle we use is based on Boolean implication logic that has not been commonly applied to study tissue cell populations. Boolean analysis assigns a parameter (e.g. RNA level of a gene) with only two values, i.e., high/low, 1/0, or positive/negative. Applying the Boolean principle, it is possible to determine the relationship between the expression levels of any pair of genes. Boolean analysis enables identification of fundamental gene expression relationships in human tissues and across species. The primary goal of this proposal is to use Boolean implication relationships to enable single-cell analysis of human colon tissue. Based on our preliminary data, the overall hypothesis is that Boolean principles can be used to specifically characterize the population of cell types in human colon tissue. Undergraduate involvement in this stem cell and computational biology research can expose them to the benefits of interdisciplinary research in biomedical fields and advance their professional careers.