Literature DB >> 26000478

Using Genome-scale Models to Predict Biological Capabilities.

Edward J O'Brien1, Jonathan M Monk2, Bernhard O Palsson3.   

Abstract

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2015        PMID: 26000478      PMCID: PMC4451052          DOI: 10.1016/j.cell.2015.05.019

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   41.582


  93 in total

1.  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

2.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

Authors:  Jan Schellenberger; Richard Que; Ronan M T Fleming; Ines Thiele; Jeffrey D Orth; Adam M Feist; Daniel C Zielinski; Aarash Bordbar; Nathan E Lewis; Sorena Rahmanian; Joseph Kang; Daniel R Hyduke; Bernhard Ø Palsson
Journal:  Nat Protoc       Date:  2011-08-04       Impact factor: 13.491

3.  Reconciling a Salmonella enterica metabolic model with experimental data confirms that overexpression of the glyoxylate shunt can rescue a lethal ppc deletion mutant.

Authors:  Nicole L Fong; Joshua A Lerman; Irene Lam; Bernhard O Palsson; Pep Charusanti
Journal:  FEMS Microbiol Lett       Date:  2013-03-15       Impact factor: 2.742

4.  Long-term phenotypic evolution of bacteria.

Authors:  Germán Plata; Christopher S Henry; Dennis Vitkup
Journal:  Nature       Date:  2014-10-26       Impact factor: 49.962

5.  A whole-cell computational model predicts phenotype from genotype.

Authors:  Jonathan R Karr; Jayodita C Sanghvi; Derek N Macklin; Miriam V Gutschow; Jared M Jacobs; Benjamin Bolival; Nacyra Assad-Garcia; John I Glass; Markus W Covert
Journal:  Cell       Date:  2012-07-20       Impact factor: 41.582

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.  Drug off-target effects predicted using structural analysis in the context of a metabolic network model.

Authors:  Roger L Chang; Li Xie; Lei Xie; Philip E Bourne; Bernhard Ø Palsson
Journal:  PLoS Comput Biol       Date:  2010-09-23       Impact factor: 4.475

10.  Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path.

Authors:  Miguel A Campodonico; Barbara A Andrews; Juan A Asenjo; Bernhard O Palsson; Adam M Feist
Journal:  Metab Eng       Date:  2014-07-28       Impact factor: 9.783

View more
  205 in total

Review 1.  Metabolic network modeling with model organisms.

Authors:  L Safak Yilmaz; Albertha Jm Walhout
Journal:  Curr Opin Chem Biol       Date:  2017-01-12       Impact factor: 8.822

2.  Prediction of Cellular Burden with Host-Circuit Models.

Authors:  Evangelos-Marios Nikolados; Andrea Y Weiße; Diego A Oyarzún
Journal:  Methods Mol Biol       Date:  2021

3.  Thermodynamic Constraints Improve Metabolic Networks.

Authors:  Elias W Krumholz; Igor G L Libourel
Journal:  Biophys J       Date:  2017-08-08       Impact factor: 4.033

4.  Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling.

Authors:  Tomoaki Matsuura; Naoki Tanimura; Kazufumi Hosoda; Tetsuya Yomo; Yoshihiro Shimizu
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-06       Impact factor: 11.205

5.  Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

Authors:  Maria Masid; Meric Ataman; Vassily Hatzimanikatis
Journal:  Nat Commun       Date:  2020-06-04       Impact factor: 14.919

6.  Applications of a metabolic network model of mesenchymal stem cells for controlling cell proliferation and differentiation.

Authors:  Hamideh Fouladiha; Sayed-Amir Marashi; Mohammad Ali Shokrgozar; Mehdi Farokhi; Amir Atashi
Journal:  Cytotechnology       Date:  2017-10-04       Impact factor: 2.058

Review 7.  How to deal with parameters for whole-cell modelling.

Authors:  Ann C Babtie; Michael P H Stumpf
Journal:  J R Soc Interface       Date:  2017-08-02       Impact factor: 4.118

Review 8.  Metabolomics and Isotope Tracing.

Authors:  Cholsoon Jang; Li Chen; Joshua D Rabinowitz
Journal:  Cell       Date:  2018-05-03       Impact factor: 41.582

Review 9.  Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities.

Authors:  Xinyun Cao; Joshua J Hamilton; Ophelia S Venturelli
Journal:  Biochemistry       Date:  2018-11-20       Impact factor: 3.162

10.  Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis.

Authors:  Van Du T Tran; Sébastien Moretti; Alix T Coste; Sara Amorim-Vaz; Dominique Sanglard; Marco Pagni
Journal:  Bioinformatics       Date:  2019-07-01       Impact factor: 6.937

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.