Contextualizing Chaotic Metabolic Networks and Their Regulation

项目摘要/摘要 癌症新陈代谢是一个复杂的网络，对基本的化学反应和酶反应产生扰动； 然而，在过去的一个世纪里，人们对这一体系的理解基本上是一种简化论的方法。而当 以前，由于技术限制，这种方法是必要的，而现在的计算机时代是技术 技术进步使我们能够对单个细胞和细胞群体的生物学细节进行调查、建模和探索。 科学领域，如RNA生物学和新陈代谢，在最近几十年经历了巨大的进步 Rna-seq和基于质谱学的代谢组学的出现，但我们对上下文和提取的能力 这些庞大的数据集的全部范围继续滞后，往往导致只关注少数 数据集中的实体。这就有效地使“大数据”变成了“小数据”。这是有问题的，因为 实验通常是昂贵和耗时的，然而我们只使用了总数据的一小部分 由给定的实验产生的。对于我的提案的F99阶段，我将通过以下方式解决这些限制 Metaboverse的开发，这是一个建立在我们以前工作基础上的多组计算分析框架 对可定制和全球代谢网络表示中的组学数据集进行语境化。这 该框架将为探索复杂形式的癌症代谢调控奠定基础。 例如，我们将分析代谢网络经历分散和低幅度的能力 监管，其中，监管不是一两个组成部分充当核心监管行为者，而是 由分散的一组基因、蛋白质或代谢物执行的。这一框架和相关的监管 研究将彻底改变我们更全面地理解时间代谢变化和 基因-代谢物内部的协作性，以及确保我们获得最大数量的信息 根据我们的数据。在我的提案的K00阶段，我将与NCI指定的博士后导师一起工作 癌症中心或附属机构将补充我在机器学习和网络生物学方面的培训 开发模型，提高我们根据转录状态预测代谢状态的能力。这样做将允许 美国将利用癌症生物学中庞大的转录组学数据库更好地了解新陈代谢的作用 跨越不同种类的肿瘤细胞群。我的最终目标是成为一名终身教授，并参加 一个独立的、由NIH资助的研究实验室，专注于计算癌症新陈代谢研究 这就开发出了质疑这一新兴生物学领域的方法。

Project Summary/Abstract Cancer metabolism is a complex network of perturbations to essential chemical and enzymatic reactions; however, the past century has seen a largely reductionist approach to understanding this system. While previously this approach was necessary due to technological limitations, current computer age technological advances allow us to survey, model, and explore the biological details of individual cells and populations of cells. Scientific fields, such as RNA biology and metabolism, have experienced massive strides in recent decades with the advent of RNA-seq and mass spectrometry-based metabolomics, yet our ability to contextualize and extract the full extent of these enormous datasets continues to lag and often results in focusing on only a handful of entities from a dataset. This effectively causes “big data” to become “little data”. This is problematic as these experiments are often expensive and time-consuming to produce, yet we only use a fraction of the total data produced by a given experiment. For the F99 phase of my proposal, I will address these limitations by leading the development of Metaboverse, a multi-omic computational analysis framework built upon our previous work to contextualize -omics datasets within customizable and global metabolic network representations. This framework will lay the foundation allowing for the exploration of complex forms of metabolic regulation in cancer. For example, we will analyze the ability of metabolic networks to undergo dispersed and low-magnitude regulation, where, rather than one or two components acting as the core regulatory actors, regulation is performed by dispersed groups of genes, proteins, or metabolites. This framework and related regulatory research will revolutionize our ability to more holistically understand temporal metabolic shifts and gene-metabolite intra-cooperativity, as well as ensure we obtain the maximum amount of information from our data. For the K00 phase of my proposal, I will work with a postdoctoral mentor at an NCI-Designated Cancer Center or affiliated institution that will supplement my training in machine learning and network biology to develop models that improve our ability to predict metabolic state from transcriptomic state. Doing so will allow us to harness the vast transcriptomics databases in cancer biology to better understand the role of metabolism across heterogeneous tumor cell populations. My ultimate goal is to become a tenured professor and run an independent, NIH-funded research lab that focuses on computational cancer metabolism research and that develops methods for interrogating this emerging domain of biology.