从代谢组学时间进程推断代谢通量

Inferring Metabolic Flux from Time-Course Metabolomics.

作者信息

Campit Scott, Chandrasekaran Sriram

机构信息

Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA.

Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.

出版信息

Methods Mol Biol. 2020;2088:299-313. doi: 10.1007/978-1-0716-0159-4_13.

DOI:10.1007/978-1-0716-0159-4_13

PMID:31893379

Abstract

The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.

摘要

哺乳动物细胞的代谢活性会随时间动态变化，并与细胞分化和增殖等细胞过程中不断变化的代谢需求相关联。虽然诸如时间进程代谢组学和通量追踪等实验工具能够测量少数几条途径的动态变化，但它们无法推断整个网络水平的通量。为解决这一局限性，我们开发了动态通量活性（DFA）算法，这是一种基因组规模的建模方法，利用时间进程代谢组学来预测代谢状态转变期间的动态通量重新布线。本章提供了一个应用DFA来表征各种癌细胞系动态代谢活性的方案。