Contextualizing Chaotic Metabolic Networks and Their Regulation

项目总结/摘要 癌症代谢是对基本化学和酶反应的扰动的复杂网络; 然而，在过去的世纪里，人们对这一体系的理解在很大程度上采取了简化论的方法。而 以前，由于技术限制，这种方法是必要的， 这些进展使我们能够调查、建模和探索单个细胞和细胞群体的生物学细节。 科学领域，如RNA生物学和代谢，在最近几十年经历了巨大的进步， RNA-seq和基于质谱的代谢组学的出现，但我们的能力， 这些庞大的数据集的全部范围仍然滞后，往往导致只关注少数几个 数据集的实体。这实际上导致了“大数据”变成了“小数据”。这是有问题的，因为这些 实验往往是昂贵和耗时的生产，但我们只使用了一小部分的总数据 由一个特定的实验产生。对于我的建议书的F99阶段，我将通过以下方式解决这些限制： Metaboverse的开发，这是一个基于我们以前工作的多组学计算分析框架 在可定制的和全球的代谢网络表示的背景下，组学数据集。这 该框架将为探索癌症中代谢调节的复杂形式奠定基础。 例如，我们将分析代谢网络进行分散和低幅度的能力， 监管，而不是一个或两个组成部分作为核心监管行为者，监管是 由分散的基因、蛋白质或代谢物组成。该框架和相关监管 研究将彻底改变我们更全面地理解时间代谢变化的能力， 基因代谢物内协同性，以及确保我们获得最大量的信息 from our data数据.对于我的建议的K 00阶段，我将与一位博士后导师在NCI指定的 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，提高我们从转录组状态预测代谢状态的能力。这样做将使 利用癌症生物学中庞大的转录组学数据库，更好地了解代谢的作用 在异质性肿瘤细胞群体中。我的最终目标是成为一名终身教授， 一个独立的，NIH资助的研究实验室，专注于计算癌症代谢研究 并开发出研究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.