Literature DB >> 22367118

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

Nathan E Lewis1, Harish Nagarajan, Bernhard O Palsson.   

Abstract

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.

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Year:  2012        PMID: 22367118      PMCID: PMC3536058          DOI: 10.1038/nrmicro2737

Source DB:  PubMed          Journal:  Nat Rev Microbiol        ISSN: 1740-1526            Impact factor:   60.633


  139 in total

1.  Metabolic network structure determines key aspects of functionality and regulation.

Authors:  Jörg Stelling; Steffen Klamt; Katja Bettenbrock; Stefan Schuster; Ernst Dieter Gilles
Journal:  Nature       Date:  2002-11-14       Impact factor: 49.962

2.  Energy balance for analysis of complex metabolic networks.

Authors:  Daniel A Beard; Shou-dan Liang; Hong Qian
Journal:  Biophys J       Date:  2002-07       Impact factor: 4.033

3.  Integrating high-throughput and computational data elucidates bacterial networks.

Authors:  Markus W Covert; Eric M Knight; Jennifer L Reed; Markus J Herrgard; Bernhard O Palsson
Journal:  Nature       Date:  2004-05-06       Impact factor: 49.962

4.  Combinatorial complexity of pathway analysis in metabolic networks.

Authors:  Steffen Klamt; Jörg Stelling
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

5.  DESHARKY: automatic design of metabolic pathways for optimal cell growth.

Authors:  Guillermo Rodrigo; Javier Carrera; Kristala Jones Prather; Alfonso Jaramillo
Journal:  Bioinformatics       Date:  2008-09-05       Impact factor: 6.937

6.  Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol.

Authors:  Harry Yim; Robert Haselbeck; Wei Niu; Catherine Pujol-Baxley; Anthony Burgard; Jeff Boldt; Julia Khandurina; John D Trawick; Robin E Osterhout; Rosary Stephen; Jazell Estadilla; Sy Teisan; H Brett Schreyer; Stefan Andrae; Tae Hoon Yang; Sang Yup Lee; Mark J Burk; Stephen Van Dien
Journal:  Nat Chem Biol       Date:  2011-05-22       Impact factor: 15.040

7.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

Authors:  J S Edwards; R U Ibarra; B O Palsson
Journal:  Nat Biotechnol       Date:  2001-02       Impact factor: 54.908

8.  Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.

Authors:  Nathan E Lewis; Kim K Hixson; Tom M Conrad; Joshua A Lerman; Pep Charusanti; Ashoka D Polpitiya; Joshua N Adkins; Gunnar Schramm; Samuel O Purvine; Daniel Lopez-Ferrer; Karl K Weitz; Roland Eils; Rainer König; Richard D Smith; Bernhard Ø Palsson
Journal:  Mol Syst Biol       Date:  2010-07       Impact factor: 11.429

9.  Analysis of complex metabolic behavior through pathway decomposition.

Authors:  Kuhn Ip; Caroline Colijn; Desmond S Lun
Journal:  BMC Syst Biol       Date:  2011-06-03

10.  A genome-scale computational study of the interplay between transcriptional regulation and metabolism.

Authors:  Tomer Shlomi; Yariv Eisenberg; Roded Sharan; Eytan Ruppin
Journal:  Mol Syst Biol       Date:  2007-04-17       Impact factor: 11.429

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  286 in total

Review 1.  Engineering ecosystems and synthetic ecologies.

Authors:  Michael T Mee; Harris H Wang
Journal:  Mol Biosyst       Date:  2012-10

2.  Inference and Prediction of Metabolic Network Fluxes.

Authors:  Zoran Nikoloski; Richard Perez-Storey; Lee J Sweetlove
Journal:  Plant Physiol       Date:  2015-09-21       Impact factor: 8.340

Review 3.  Systems strategies for developing industrial microbial strains.

Authors:  Sang Yup Lee; Hyun Uk Kim
Journal:  Nat Biotechnol       Date:  2015-10       Impact factor: 54.908

Review 4.  In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories.

Authors:  Paulo Maia; Miguel Rocha; Isabel Rocha
Journal:  Microbiol Mol Biol Rev       Date:  2015-11-25       Impact factor: 11.056

5.  A Markov chain model for N-linked protein glycosylation--towards a low-parameter tool for model-driven glycoengineering.

Authors:  Philipp N Spahn; Anders H Hansen; Henning G Hansen; Johnny Arnsdorf; Helene F Kildegaard; Nathan E Lewis
Journal:  Metab Eng       Date:  2015-10-29       Impact factor: 9.783

6.  A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data.

Authors:  Narayanan Sadagopan; Yiping Wang; Brandon E Barker; Kieran Smallbone; Christopher R Myers; Hongwei Xi; Jason W Locasale; Zhenglong Gu
Journal:  Comput Biol Chem       Date:  2015-09-01       Impact factor: 2.877

7.  A vector library for silencing central carbon metabolism genes with antisense RNAs in Escherichia coli.

Authors:  Nobutaka Nakashima; Satoshi Ohno; Katsunori Yoshikawa; Hiroshi Shimizu; Tomohiro Tamura
Journal:  Appl Environ Microbiol       Date:  2013-11-08       Impact factor: 4.792

Review 8.  Integrative approaches for finding modular structure in biological networks.

Authors:  Koyel Mitra; Anne-Ruxandra Carvunis; Sanath Kumar Ramesh; Trey Ideker
Journal:  Nat Rev Genet       Date:  2013-10       Impact factor: 53.242

9.  Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma.

Authors:  Francesco Gatto; Intawat Nookaew; Jens Nielsen
Journal:  Proc Natl Acad Sci U S A       Date:  2014-02-18       Impact factor: 11.205

10.  Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production.

Authors:  Anne Arnold; Zoran Nikoloski
Journal:  Plant Physiol       Date:  2014-05-07       Impact factor: 8.340

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