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Characterizing complex phenotypes in metabolism: An 'omics'-driven systems approach.

机译：表征代谢中的复杂表型：“组学”驱动的系统方法。

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摘要

The advent of high-throughput technologies has resulted in an explosion of molecular data. A major challenge is found in interpreting and understanding these different types of data sets at a phenotypic level. Systems biology has capitalized on these technologies by consolidating various types of biological information into structured networks for their analysis and computation. The bottom-up systems biology approach, in particular, has been crucial in providing mechanistic foundations for systems-level modeling in microorganisms, and its extension to eukaryotic metabolism has made it possible to elucidate complex phenotypes in a systematic manner. The work presented in this dissertation describes the integrative use of high-throughput data and genome-scale network reconstructions to characterize complex phenotypes of eukaryotic metabolism. First, the genome-scale reconstructions of yeast and human metabolism are discussed, which provide the contextual basis in which "omics" data is analyzed. Previously developed constraint-based modeling approaches were refined to analyze "omics" data sets, in particular for transcriptomic and metabolomic data. Finally, example applications are presented in the evaluation of physiological and perturbed metabolic states of yeast and human cellular systems. The studies discussed herein are: (1) analyzing drug response phenotypes of human metabolism; (2) evaluating genetic and environmentally perturbed processes in yeast ammonium assimilation; and (3) characterizing the pluripotent phenotype of embryonic stem cell metabolism. The work described in this dissertation represents advancement towards integrating bottom-up and data-driven approaches to understanding broader "omics"-to-phenotype relationships.
机译：高通量技术的出现导致分子数据的爆炸式增长。在表型水平上解释和理解这些不同类型的数据集是一项重大挑战。系统生物学已通过将各种类型的生物信息整合到结构化网络中进行分析和计算来利用这些技术。自下而上的系统生物学方法尤其在为微生物进行系统级建模提供机制基础方面至关重要，并且其扩展到真核生物代谢已使系统地阐明复杂表型成为可能。本文介绍的工作描述了高通量数据和基因组规模网络重构的综合利用，以表征真核代谢的复杂表型。首先，讨论了酵母和人类新陈代谢的基因组规模的重建，这为分析“组学”数据提供了背景基础。改进了以前开发的基于约束的建模方法，以分析“组学”数据集，特别是对于转录组和代谢组学数据。最后，在评估酵母和人类细胞系统的生理状态和扰动的代谢状态中提供了示例应用程序。本文讨论的研究是：（1）分析人类代谢的药物反应表型； （2）评估酵母铵同化过程中的遗传和环境扰动过程； （3）表征胚胎干细胞代谢的多能表型。本文所描述的工作代表了自下而上和数据驱动方法的集成的进步，以理解更广泛的“组学”与表型之间的关系。

著录项

  • 作者

    Mo, Monica L.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Biology Systematic.;Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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