Using co-evolution to understand the emergence of bacterial phenotype from proteome variation

项目概要/摘要：生物学中的一个基本问题是理解生物学的纲要是如何产生的。 生物体中的蛋白质（“蛋白质组”）相互作用以产生表型。尽管有相当大 定义和推断蛋白质-蛋白质相互作用的实验和计算进展 （PPI），目前还没有方法来推断蛋白质相互作用的层次结构：即蛋白质如何相互作用， 创造复合体、途径和表型。这项提议以细菌为模型， 将基因组序列转换为蛋白质相互作用网络层次的统计方法。关键 这种方法是一种进步，即变化的分量通常作为噪声（含有< 0.01%）被丢弃。 方差）实际上确实包含关于PPI的生物学重要信息。初步结果表明， 我们的统计方法可能是一个有效的多尺度框架来描述涌现的生物功能 由蛋白质的“部分列表”产生。我们称这种方法为分层谱相关分析 进化信号（SCALES）;我们建议的主要目的是测试实验的有效性， 我们方法的稳健性。关于有效性，我们将联合收割机高通量分子遗传学与 计算来测试SCALES是否可以使用P准确地推断未表征蛋白质的功能。 铜绿假单胞菌作为模型系统。关于鲁棒性，我们将测试我们的结果是否对 用于测量共变异的基因组特征。 展望实验室的未来，SCALES通常可用于理解分层 跨不同生物系统的架构，从蛋白质到细胞再到生态系统。所以我们 我相信，这一建议将作为一个关键的出发点，探讨重要的概念，核心的重点， 后基因组时代，即创造新的数学框架，通过它来转换高- 内容，复杂的数据被收集成有用的和可操作的生物学知识。 为了定义实验室的愿景，自然系统是生成过程的产物， 对进化过程的理解很差虽然被恰当地描述为随机变异和选择， 进化产生了非常有序的、低熵的生物系统， 功能，是强大的扰动，并有能力适应新的功能。因此 可以想象，定量地理解进化系统的设计架构，以及它们是如何形成的， 可能会为具有类似自然属性的系统提供新的工程理论基础;即， 与环境动态互动的能力。拉曼实验室的广阔视野是阐明 管理进化系统工作能力和保持适应性的组织原则。我们 希望在受组件变化和环境影响各种系统中解决这个问题 选择.在这样做的过程中，我们的最终希望是创建智能设计自适应系统的规则。

Project Summary/Abstract: A fundamental problem in biology is to understand how the compendium of proteins in an organism (the ‘proteome’) cooperatively interact to create phenotype. Despite considerable experimental and computational advances with respect to defining and inferring protein-protein interactions (PPIs), no method currently exists to infer a hierarchy of protein interactions: that is, how proteins interact to create complexes, pathways, and phenotype. This proposal uses bacteria as a model to develop a novel statistical method that transforms a genome sequence into a hierarchy of protein interaction networks. Key to this approach is the advance that components of variation typically discarded as noise (harboring < 0.01% variance) in fact do contain biologically important information regarding PPIs. Preliminary results illustrate that our statistical method may be an effective multi-scale framework to describe emergent biological function arising from a ‘parts-list’ of proteins. We call our approach Spectral Correlation Analysis of Layered Evolutionary Signals (SCALES); the main thrust of our proposal is testing the experimental validity and robustness of our approach. With respect to validity, we will combine high-throughput molecular genetics with computation to test whether SCALES can accurately infer functions of uncharacterized proteins using P. aeruginosa as a model system. With respect to robustness, we will test whether our results are robust to the genomic feature used for measuring co-variation. Looking to the future of the laboratory, SCALES may be generally useful for understanding hierarchical architectures across different biological systems, spanning proteins to cells to ecosystems. Therefore, we believe this proposal will serve as a critical launching point to explore important concepts central to the focus of the post-genomic era, namely creating novel mathematical frameworks by which to convert the torrent of high- content, complex data being collected into useful and actionable biological knowledge. For defining the vision of the laboratory, natural systems are products of a generative process that is poorly understood—the evolutionary process. Though properly described as random variation and selection, evolution generates remarkably ordered, low-entropy biological systems that execute high-performance functions, are robust to perturbation, and have the capacity to adapt to new functions. It is therefore conceivable that quantitatively understanding design architectures of evolved systems, and how they come to be, may yield a new theoretical foundation of engineering for systems with natural-like properties; namely, the ability to dynamically interact with the environment. The broad vision of the Raman Lab is to elucidate organizational principles that govern the ability of evolved systems to work as well as maintain fitness. We hope to address this problem in a variety of systems subject to component variation and environmental selection. In doing so, our ultimate hope is to create rubrics for designing adaptive systems intelligently.