A Computational Approach To Closing The Gap Between Tissue Structure And Function

描述（由申请人提供）：结构/功能关系是我们理解各个层面生物系统的基础，并且驱动了大多数（如果不是全部的话）检测、诊断和治疗疾病的技术。这个概念足够强大，激发了长达200多年的努力，以更精细的细节描述我们生物宇宙的组成部分，从基于结构相似性对生物进行分类的林奈分类系统开始，到微观尺度的描述，如几种生物的完整基因组，包括人类。然而，在将复杂的生物宇宙简化为无数微小的部分之后，我们遇到了新的复杂性形式：数据过载和维度诅咒。简单地说，我们已经把我们的生物机器拆开了，却无法再把它装回去——我们积累还原主义数据的能力已经超过了我们理解它的能力。因此，我们在结构/功能关系中遇到了一个缺口：我们已经积累了大量关于生物结构的详细信息，但我们无法将其组合在一起，以解释这些结构所执行的相应复杂的生物功能。我们提出了一种基于图论表示组织并通过分析底层图来学习其结构特性的新方法来解决这个问题。我们的长期目标是通过建立将组织结构与生物功能联系起来的定量特征来缩小这一差距。我们这个项目的直接目标是根据三种形态不同的组织（脑、乳腺、骨）的不同形态特征，定义三种定量不同的功能状态（健康、受损、患病）。然后测试提出将这些定量特征与这些组织中的特定生物活动联系起来的三个假设。该项目的成功完成将为定量联系组织的告密结构特性（例如，细胞分布、形态、接触）与特定疾病状态和组成这些组织的细胞的基本行为提供一个新的强大工具。这对进行基础研究以揭示组织结构和功能的指导原则的科学家和寻求快速准确地检测和诊断涉及组织结构改变的疾病的临床医生都很有用。公共卫生相关性：该项目的成功完成将提供一种新的、强大的工具，用于定量地将组织的结构特性（如细胞分布、形态、接触）与组成这些组织的细胞的特定疾病状态和基本行为联系起来。这对进行基础研究以揭示组织结构和功能的指导原则的科学家和寻求快速准确地检测和诊断涉及组织结构改变的疾病的临床医生都很有用。

DESCRIPTION (provided by applicant): The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. The concept is powerful enough to have inspired a 200+ year-long effort to describe the components of our biological universe in ever finer detail, beginning with the Linnean taxonomic system of cataloging organisms based on their structural similarities, and culminating with microscale descriptions such as the complete genomes of several organisms, including humans. However, having reduced the complex biological universe to a myriad of minute parts, we encounter new forms of complexity: data overload and curse of dimensionality. Simply put, we've taken our biological machine apart but can't put it back together again- our ability to accumulate reductionist data has outstripped our ability to understand it. Thus, we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological structures, we can't assemble it in a way that explains the correspondingly complex biological functions these structures perform. We propose a novel approach to this problem based on representing tissues using graph theory and learning its structural properties by analyzing the underlying graphs. Our long-range objective is to close this gap by establishing quantitative features that link tissue structure to biological function. Our immediate goals for this project are to define three quantitatively different functional states (healthy, damaged, diseased) of three morphologically distinct tissues (brain, breast, bone) based on their distinguishing morphological characteristics, then test three hypotheses that propose to link these quantitative features to specific biological activities in these tissues Successful completion of this project will provide a new and powerful tool for quantitatively linking telltale structural properties of tissues (e.g., cellular distribution, morphology, contact) with specific disease states and fundamental behaviors of the cells comprising these tissues. This will be useful both to scientists conducting basic research to uncover the guiding principles of tissue structure and function, and to clinicians seeking to quickly and accurately detect and diagnose diseases that involve alterations in tissue structure. PUBLIC HEALTH RELEVANCE: Successful completion of this project will provide a new and powerful tool for quantitatively linking telltale structural properties of tissues (e.g., cellular distribution, morphology, contact) with specific disease states and fundamental behaviors of the cells comprising these tissues. This will be useful both to scientists conducting basic research to uncover the guiding principles of tissue structure and function, and to clinicians seeking to quickly and accurately detect and diagnose diseases that involve alterations in tissue structure.