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

利用共同进化来了解蛋白质组变异中细菌表型的出现

• 批准号: 10684867
• 负责人: Arjun Raman
• 金额: $ 40.26万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684867
• 关键词: Address; Architecture; Bacteria; Big Data; Biological; Biological Models; Biological Process; Biology; Catalogs; Cells; Collection; Complex; Data; Ecosystem; Engineering; Entropy; Environment; Evolution; Expert Systems; Foundations; Future; Genetic Variation; Genome; Genomics; Genotype; Knowledge; Laboratories; Mathematics; Measures; Methods; Modeling; Molecular Genetics; Noise; Organism; Pathway interactions; Performance; Phenotype; Process; Property; Proteins; Proteome; Pseudomonas aeruginosa; Signal Transduction; Statistical Methods; System; Testing; Variant; Vision; Work; biological systems; cell behavior; complex data; design; fitness; genomic data; novel; protein protein interaction

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.

项目摘要/摘要：生物学的一个基本问题是了解如何了解 生物体中的蛋白质（“蛋白质组”）合作相互作用以创建表型。尽管很大 关于定义和推断蛋白质 - 蛋白质相互作用的实验和计算进步 （PPI），目前尚无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 创建复合物，途径和表型。该建议使用细菌作为模型来发展新颖 统计方法将基因组序列转化为蛋白质相互作用网络的层次结构。关键 这种方法是变化的组成部分通常被丢弃为噪声（藏有<0.01％ 差异）实际上确实包含有关PPI的生物学重要信息。初步结果说明了 我们的统计方法可能是描述新兴生物学功能的有效多尺度框架 由蛋白质的“零件列表”产生。我们称我们的方法光谱相关分析分析 进化信号（尺度）；我们提案的主要目的是测试实验有效性和 我们方法的鲁棒性。关于有效性，我们将将高通量分子遗传学与 计算以测试尺度是否可以使用P准确推断未表征的蛋白质的功能。 铜绿作为模型系统。关于鲁棒性，我们将测试我们的结果是否适合 用于测量共同变化的基因组特征。 展望实验室的未来，量表通常对于理解层次结构可能很有用 跨不同生物系统的体系结构，跨越细胞到生态系统。因此，我们 认为该建议将成为探索重要概念的关键启动点 后期的时代，即创建了新颖的数学框架，以转化高高的洪流 内容，将复杂的数据收集到有用且可操作的生物学知识中。 为了定义实验室的愿景，自然系统是通用过程的产物 理解不佳 - 进化过程。虽然正确地描述为随机变化和选择，但 Evolution生成非常有序的低渗透生物系统，可执行高性能 功能，对扰动是可靠的，并且具有适应新功能的能力。因此是 可以想象，可以定量了解进化系统的设计架构，以及它们如何达到 BE，可能会为具有天然特性的系统产生新的理论基础；即， 能够与环境动态互动的能力。拉曼实验室的广泛视野是阐明 管理发展系统工作和保持健身能力的组织原则。我们 希望在受组件变化和环境的各种系统中解决此问题 选择。这样一来，我们的最终希望是创建用于智能设计自适应系统的专栏。