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

缩小组织结构和功能之间差距的计算方法

• 批准号: 7846877
• 负责人: BULENT YENER
• 金额: $ 44.47万
• 依托单位: RENSSELAER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7846877
• 关键词: Adhesions; Back; Basic Science; Biological; Biological Process; Breast; Cataloging; Catalogs; Cell Density; Cells; Characteristics; Clinical; Complex; Data; Descriptor; Detection; Diagnosis; Disease; Environment; Extracellular Matrix; Genome; Goals; Gold; Graph; Histocompatibility Testing; Human; Learning; Link; Methods; Mining; Modeling; Morphology; Organism; Property; Scientist; Structure; Structure-Activity Relationship; System; Techniques; Testing; Tissue Model; Tissues; Work; base; biological systems; bone; brain tissue; cancer diagnosis; cell behavior; clinically relevant; cytokine; design; disease diagnosis; functional status; improved; novel strategies; public health relevance; response; theories; tool

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.

描述(由申请人提供)：结构/功能关系是我们在所有层次上理解生物系统的基础，并推动大多数(如果不是全部)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，以更详细地描述我们生物宇宙的组成部分，从Linnean基于其结构相似性对有机体进行分类的分类系统开始，最后是微尺度的描述，如包括人类在内的几个有机体的完整基因组。然而，在将复杂的生物宇宙简化为无数微小的部分后，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，我们已经拆开了我们的生物机器，但无法将其重新组装起来--我们积累简化论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/功能关系上遇到了一个缺口：积累了大量关于生物结构的详细信息后，我们无法用一种方式来解释这些结构执行的相应复杂的生物功能。我们提出了一种新的方法来解决这一问题，该方法使用图论来表示组织，并通过分析潜在的图来学习其结构属性。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。我们这个项目的近期目标是根据三种不同形态的组织(脑、乳房、骨骼)的不同形态特征，定义三种不同的定量功能状态(健康、受损、患病)，然后测试三个假设，这些假设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。该项目的成功完成将提供一个新的强大的工具，用于将组织的结构属性(例如，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进行定量联系。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以及寻求快速准确地检测和诊断涉及组织结构变化的疾病的临床医生都是有用的。与公共卫生的相关性：该项目的成功完成将提供一种新的、强有力的工具，将组织的结构特征(如细胞分布、形态、接触)与构成这些组织的细胞的具体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以及寻求快速准确地检测和诊断涉及组织结构变化的疾病的临床医生都是有用的。