Genome-Scale 13C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae

系统生物学 合成生物学 代谢工程 代谢通量分析 代谢组学 计算生物学 模拟生物系统 生化工程 计算机科学 代谢网络 通量平衡分析 生物 数据科学 生物信息学 工程类 遗传学 基因 内分泌学 新陈代谢
作者
David Ando,Héctor García Martín
出处
期刊:Methods in molecular biology [Springer Science+Business Media]
卷期号:: 317-345 被引量:6
标识
DOI:10.1007/978-1-4939-8757-3_19
摘要

Synthetic biology is a rapidly developing field that pursues the application of engineering principles and development approaches to biological engineering. Synthetic biology is poised to change the way biology is practiced, and has important practical applications: for example, building genetically engineered organisms to produce biofuels, medicines, and other chemicals. Traditionally, synthetic biology has focused on manipulating a few genes (e.g., in a single pathway or genetic circuit), but its combination with systems biology holds the promise of creating new cellular architectures and constructing complex biological systems from the ground up. Enabling this merge of synthetic and systems biology will require greater predictive capability for modeling the behavior of cellular systems, and more comprehensive data sets for building and calibrating these models. The so-called “-omics” data sets can now be generated via high throughput techniques in the form of genomic, proteomic, transcriptomic, and metabolomic information on the engineered biological system. Of particular interest with respect to the engineering of microbes capable of producing biofuels and other chemicals economically and at scale are metabolomic datasets, and their insights into intracellular metabolic fluxes. Metabolic fluxes provide a rapid and easy to understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis and modeling using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This library allows the user to transform metabolomics data in the form of isotope labeling data from a 13C labeling experiment into a determination of cellular fluxes that can be used to develop genetic engineering strategies for metabolic engineering. The jQMM library presents a complete toolbox for performing a range of different tasks of interest in metabolic engineering. Various different types of flux analysis and modeling can be performed such as flux balance analysis, 13C metabolic flux analysis, and two-scale 13C metabolic flux analysis (2S-13C MFA). 2S-13C MFA is a novel method that determines genome-scale fluxes without the need of every single carbon transition in the metabolic network. In addition to several other capabilities, the jQMM library can make model based predictions for how various genetic engineering strategies can be incorporated toward bioengineering goals: it can predict the effects of reaction knockouts on metabolism using both the MoMA Minimization of metabolic adjustment (MoMA) and ROOM methodologies. In this chapter, we will illustrate the use of the jQMM library through a step-by-step demonstration of flux determination and knockout prediction in a complex eukaryotic model organism: Saccharomyces cerevisiae (S. cerevisiae). Included with this chapter is a digital Jupyter Notebook file that provides a computable appendix showing a self-contained example of jQMM usage, which can be changed to fit the user’s specific needs. As an open source software project, users can modify and extend the code base to make improvements at will, allowing them to share their development work and contribute back to the jQMM modeling community.
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