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

项目摘要/摘要：生物学中的一个基本问题是如何理解 生物体中的蛋白质(“蛋白质组”)相互作用，共同创造表型。尽管有相当大的 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：即蛋白质如何与 创造复合体、路径和表型。这项提议使用细菌作为模型来开发一种新的 将基因组序列转换为蛋白质相互作用网络层次结构的统计方法。关键是 这种方法是变异分量通常作为噪声丢弃的进步(隐藏&lt；0.01% 方差)实际上包含关于PPI的生物学上重要的信息。初步结果表明， 我们的统计方法可能是描述突发生物功能的一个有效的多尺度框架 产生于蛋白质的“部分清单”。我们称我们的方法为分层谱相关分析 进化信号(尺度)；我们建议的主旨是测试实验的有效性和 我们方法的健壮性。在有效性方面，我们将把高通量分子遗传学与 计算以检验标尺是否能用P。 铜绿假单胞菌作为模型系统。关于稳健性，我们将测试我们的结果是否对 用于测量协变的基因组特征。 展望实验室的未来，标度通常对理解分层结构很有用 跨越不同生物系统的体系结构，从蛋白质到细胞再到生态系统。因此，我们 我相信这项提案将成为一个关键的起点，以探索关注的核心重要概念 后基因组时代，也就是创造新的数学框架，用来将高基因组的洪流 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，自然系统是一个生成性过程的产物，即 人们对进化过程知之甚少。尽管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，这些系统执行高性能 函数，对扰动是健壮的，并且有能力适应新的函数。因此，它是 可以想象，定量地理解演进系统的设计体系结构，以及它们是如何实现的 可能会为具有类自然性质的系统产生新的工程理论基础；即 能够与环境动态互动。拉曼实验室的宏伟愿景是澄清 管理进化系统工作和保持健康的能力的组织原则。我们 希望在受组件变化和环境影响的各种系统中解决这一问题 选择。通过这样做，我们的最终希望是创建用于智能设计自适应系统的Rubrics。

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.