Project 2

项目2

• 批准号: 8517246
• 负责人: AIMEE M DUDLEY
• 金额: $ 48.91万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8517246
• 关键词: Adopted; Age; Algorithms; Automobile Driving; Behavior; Biological; Biology; Blood Glucose; Carbon; Cell Death; Cell physiology; Cells; Cellular Structures; Characteristics; Chemicals; Communities; Complex; Congenital Abnormality; Coupled; Coupling; Cues; DNA Sequence; Data; Dependence; Development; Disease; Epigenetic Process; Equation; Ethanol; Exhibits; Extracellular Matrix; Fungal Genome; Gene Expression; Gene Expression Profile; Genetic; Genetic Transcription; Genotype; Glucose; Growth; Health; Image; Image Analysis; Individual; LacZ Genes; Libraries; Machine Learning; Measures; Mechanics; Metabolic; Methodology; Methods; Microbial Biofilms; Microscopy; Modeling; Molecular; Morphology; Noise; Nucleic Acids; Nucleotides; Nutrient; Onset of illness; Organ; Organism; Pathway interactions; Pattern; Phenotype; Population; Predisposition; Process; Property; RNA; Regulator Genes; Reporter; Saccharomyces cerevisiae; Series; Side; Signal Transduction; Source; Spatial Distribution; Structure; Surface; System; Systems Biology; Technology; Time; Tissues; Transcript; Update; Waste Products; Work; Yeast Model System; Yeasts; advanced system; angiogenesis; base; genetic analysis; genome sequencing; human disease; intercellular communication; interest; microorganism; molecular scale; particle; programs; promoter; quorum sensing; research study; response; spatiotemporal; technology development; trait; tumor growth

Project 2 Multiscale spatial and temporal dynamics of yeast colony development Introduction. In living systems, the characteristics of an individual, including traits such as susceptibility to disease or response to therapy, are determined by the coupling of processes that function at different scales of organization. For example, an individual's DNA sequence constrains the molecular networks that govern its cellular states and behaviors, which in turn determine the form and functions of multi-cellular structures. Microorganisms, including the yeast Saccharomyces cerevisiae, are traditionally used as models for investigating basic cellular processes at the unicellular level. However, unicellular organisms can form multi-cellular communities and differentiate into specialized structures to benefit the population. In some wild isolates of S. cerevisiae colonies (which start from a single cell and divide mitotically to become a structure of -10[8] cells) undergo a morphological transition characterized by complex patterns of "wrinkles" on the colony surface (Fig. 11). This trait is called the "fluffy" phenotype. Work by others has shown that fluffy yeast colonies possess many properties of microbial biofilms and are thus directly relevant to health and human disease. In fluffy colonies, cells are connected by an extracellular matrix and internal hollow channels, which may help exchange nutrients and waste products. While there is some evidence that this morphological development involves nutrient driven cell state transitions, cell-cell signaling, quorum sensing and cell death, the exact molecules and in many cases the pathways are largely unknown. The importance of the experimental system: This project seeks to understand how cells establish and maintain spatiotemporal patterns of cell state transitions to form multicellular structures. In our model (yeast "fluffy" colony formation) a single cell divides a undergoes a series of metabolic and functional transitions to reproducibly self organize into a complex structure of 10[8] cells. By advancing the conceptual, computational, and technical challeges below, we will develop a general methodology for analyzing complex traits that exhibit morphological phenotypes and can thus be applied to problems as diverse as physical birth defects during development or angiogenesis during tumor growth. The challenges: ¿ Conceptual challenges: Among the most fundamental problems in biology is the genotype-phenotype question: given the complete genome sequence of an individual, can we predict the traits that the individual will exhibit? The newly emerging field of systems genetics seeks to solve the genotype phenotype problem by applying the principles and technologies of systems biology to genetic analysis. A major limitation to this approach is that while the genotype side of the equation is data-rich (e.g., billions of nucleotides), traits are defined by an extremely limited set of values (e.g., a blood glucose level or age of disease onset). Recent methods, including new machine learning algorithms that we have helped develop, have attempted to overcome this data sparsity problem by using comprehensive ("omic") data, such as RNA expression levels. Unfortunately, such data are most informative about the molecular networks (to which they are more proximal) and many steps removed from the traits of interest, which can result from processes that span multiple biological scales (molecular networks, cells, tissues, and organs). To advance systems genetics in a way that is less confounded by this problem, we have chosen an organism and a trait, colony morphology in yeast, where imaging colony structure and spatial patterns of gene expression can be used to extract an extremely rich set of parameters that are directly relevant to the trait being studied.

项目2 酵母菌落发育的多尺度时空动态 介绍。 在生命系统中，个体的特征，包括对疾病的易感性或对治疗的反应等特征，是由在不同组织规模上发挥作用的过程的耦合决定的，例如，个体的 DNA 序列限制了控制分子网络。它的细胞状态和行为反过来决定了包括酿酒酵母在内的多细胞结构的形式和功能，传统上被用作研究单细胞水平的基本细胞过程的模型。单细胞生物可以形成多细胞群落并分化成特殊的结构，以造福于种群。在一些野生分离的酿酒酵母菌落中（从单个细胞开始，通过有丝分裂形成-10[8]个细胞的结构）。以菌落表面复杂的“皱纹”图案为特征的形态转变（图11）。 这种特性被称为“蓬松”表型。其他人的研究表明，蓬松的酵母菌落具有微生物生物膜的许多特性，因此与健康和人类疾病直接相关。在蓬松的菌落中，细胞通过细胞外基质和内部空心连接。虽然有一些证据表明这种形态发育涉及营养驱动的细胞状态转变、细胞间信号传导、群体感应和细胞死亡，但确切的分子以及在许多情况下的途径很大程度上是。未知。 实验系统的重要性：该项目旨在了解细胞如何建立和维持细胞状态转换的时空模式以形成多细胞结构，在我们的模型（酵母“蓬松”集落形成）中，单个细胞分裂并经历一系列代谢和代谢过程。通过推进以下概念、计算和技术挑战，我们将开发一种用于分析表现出形态表型和特征的复杂性状的通用方法。因此可以应用于多种问题，如发育过程中的身体出生缺陷或肿瘤生长过程中的血管生成。 挑战： ¿概念挑战：生物学中最基本的问题之一是基因型-表型问题：给定个体的完整基因组序列，我们能否预测个体将表现出的特征？新兴的系统遗传学领域致力于解决基因型表型问题。将系统生物学的原理和技术应用于遗传分析的问题。 这种方法的局限性在于，虽然等式的基因型方面数据丰富（例如，数十亿个核苷酸），但性状是由一组极其有限的值定义的（例如，血糖水平或发病年龄） 。 最近的方法，包括我们帮助开发的新机器学习算法，试图通过使用综合（“组学”）数据（例如 RNA 表达水平）来克服这种数据稀疏问题，不幸的是，此类数据提供了有关分子网络的信息最多。它们更接近），并且从感兴趣的特征中删除了许多步骤，这些特征可能是跨越多个生物尺度（分子网络、细胞、组织和器官）的过程的结果，以推进系统遗传学。 为了减少这个问题的困扰，我们选择了一种生物体和一种性状，即酵母菌落形态，其中对菌落结构和基因表达的空间模式进行成像可用于提取一组极其丰富的参数，这些参数与酵母菌落直接相关。正在研究的特质。