Genome scale models of yeast: towards standardized evaluation and consistent omic integration

被引：52
作者：
Sanchez, Benjamin J. [1 ,2 ]
Nielsen, Jens [1 ,2 ,3 ]
机构：
[1] Chalmers Univ Technol, Dept Biol & Biol Engn, SE-41296 Gothenburg, Sweden
[2] Chalmers Univ Technol, Novo Nordisk Fdn Ctr Biosustainabil, SE-41296 Gothenburg, Sweden
[3] Tech Univ Denmark, Novo Nordisk Fdn Ctr Biosustainabil, DK-2970 Horsholm, Denmark
来源：
INTEGRATIVE BIOLOGY | 2015年 / 7卷 / 08期
关键词：
FLUX BALANCE ANALYSIS; CONDITION-DEPENDENT REGULATION; METABOLIC NETWORKS REVEALS; SACCHAROMYCES-CEREVISIAE; ETHANOL-PRODUCTION; EXPRESSION DATA; ESCHERICHIA-COLI; S; CEREVISIAE; LIPID-METABOLISM; SYSTEMS BIOLOGY;
D O I：
10.1039/c5ib00083a
中图分类号：
Q2 [细胞生物学];
学科分类号：
071009 ; 090102 ;
摘要：
Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.
引用
收藏
页码：846 / 858
页数：13
相关论文
共 163 条
[1]   Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production [J].
Agren, Rasmus ;
Otero, Jose Manuel ;
Nielsen, Jens .
JOURNAL OF INDUSTRIAL MICROBIOLOGY & BIOTECHNOLOGY, 2013, 40 (07) :735-747
[2]   Integration of gene expression data into genome-scale metabolic models [J].
Åkesson, M ;
Förster, J ;
Nielsen, J .
METABOLIC ENGINEERING, 2004, 6 (04) :285-293
[3]   Global organization of metabolic fluxes in the bacterium Escherichia coli [J].
Almaas, E ;
Kovács, B ;
Vicsek, T ;
Oltvai, ZN ;
Barabási, AL .
NATURE, 2004, 427 (6977) :839-843
[4]  
[Anonymous], FRONT BIOENG BIOTECH
[5]   Computing topological parameters of biological networks [J].
Assenov, Yassen ;
Ramirez, Fidel ;
Schelhorn, Sven-Eric ;
Lengauer, Thomas ;
Albrecht, Mario .
BIOINFORMATICS, 2008, 24 (02) :282-284
[6]  
Aung Hnin W, 2013, Ind Biotechnol (New Rochelle N Y), V9, P215
[7]  
Barker B., 2014, MOL NETWORKS
[8]  
Basler Georg, 2015, Methods Mol Biol, V1279, P183, DOI 10.1007/978-1-4939-2398-4_12
[9]   Context-specific metabolic networks are consistent with experiments [J].
Becker, Scott A. ;
Palsson, Bernhard O. .
PLOS COMPUTATIONAL BIOLOGY, 2008, 4 (05)
[10]   Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity [J].
Beg, Q. K. ;
Vazquez, A. ;
Ernst, J. ;
de Menezes, M. A. ;
Bar-Joseph, Z. ;
Barabasi, A.-L. ;
Oltvai, Z. N. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2007, 104 (31) :12663-12668
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