Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods

被引:552
作者
Lewis, Nathan E. [1 ]
Nagarajan, Harish [2 ]
Palsson, Bernhard O. [1 ]
机构
[1] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Bioinformat & Syst Biol Grad Program, La Jolla, CA 92093 USA
基金
美国国家卫生研究院;
关键词
FLUX BALANCE ANALYSIS; ESCHERICHIA-COLI METABOLISM; HAEMOPHILUS-INFLUENZAE RD; NETWORK-BASED PREDICTION; PATHWAY ANALYSIS; OBJECTIVE FUNCTIONS; THERMODYNAMIC ANALYSIS; MICROBIAL-METABOLISM; KNOCKOUT STRATEGIES; GENOME ANNOTATION;
D O I
10.1038/nrmicro2737
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype-phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.
引用
收藏
页码:291 / 305
页数:15
相关论文
共 142 条
[1]   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
[2]   Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli [J].
Alper, H ;
Jin, YS ;
Moxley, JF ;
Stephanopoulos, G .
METABOLIC ENGINEERING, 2005, 7 (03) :155-164
[3]   Design and analysis of synthetic carbon fixation pathways [J].
Bar-Even, Arren ;
Noor, Elad ;
Lewis, Nathan E. ;
Milo, Ron .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2010, 107 (19) :8889-8894
[4]   Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation [J].
Barrett, Christian L. ;
Herrgard, Markus J. ;
Palsson, Bernhard .
BMC SYSTEMS BIOLOGY, 2009, 3
[5]   An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models [J].
Barua, Dipak ;
Kim, Joonhoon ;
Reed, Jennifer L. .
PLOS COMPUTATIONAL BIOLOGY, 2010, 6 (10)
[6]   Energy balance for analysis of complex metabolic networks [J].
Beard, DA ;
Liang, SC ;
Qian, H .
BIOPHYSICAL JOURNAL, 2002, 83 (01) :79-86
[7]   Context-specific metabolic networks are consistent with experiments [J].
Becker, Scott A. ;
Palsson, Bernhard O. .
PLOS COMPUTATIONAL BIOLOGY, 2008, 4 (05)
[8]   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
[9]   Flux balance analysis accounting for metabolite dilution [J].
Benyamini, Tomer ;
Folger, Ori ;
Ruppin, Eytan ;
Shlomi, Tomer .
GENOME BIOLOGY, 2010, 11 (04)
[10]   Differential Producibility Analysis (DPA) of Transcriptomic Data with Metabolic Networks: Deconstructing the Metabolic Response of M. tuberculosis [J].
Bonde, Bhushan K. ;
Beste, Dany J. V. ;
Laing, Emma ;
Kierzek, Andrzej M. ;
McFadden, Johnjoe .
PLOS COMPUTATIONAL BIOLOGY, 2011, 7 (06)