Literature DB >> 19840862

Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli.

Adam M Feist1, Daniel C Zielinski, Jeffrey D Orth, Jan Schellenberger, Markus J Herrgard, Bernhard Ø Palsson.   

Abstract

Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the production potential for the bacterial production organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite production to growth rate. Designs were examined for 11 unique central metabolism and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess maximum yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theoretical maximum potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for production strains that can be experimentally implemented, characterizes the potential for E. coli to produce native compounds, and outlines a strain design pipeline that can be utilized to design production strains for additional organisms. 2009 Elsevier Inc. All rights reserved.

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Year:  2009        PMID: 19840862      PMCID: PMC3125152          DOI: 10.1016/j.ymben.2009.10.003

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


  48 in total

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Authors:  J E Bailey
Journal:  Science       Date:  1991-06-21       Impact factor: 47.728

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Authors:  Priti Pharkya; Anthony P Burgard; Costas D Maranas
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Review 3.  Biotechnological production of amino acids and derivatives: current status and prospects.

Authors:  Wolfgang Leuchtenberger; Klaus Huthmacher; Karlheinz Drauz
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Review 4.  Systems biotechnology for strain improvement.

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Review 5.  Towards multidimensional genome annotation.

Authors:  Jennifer L Reed; Iman Famili; Ines Thiele; Bernhard O Palsson
Journal:  Nat Rev Genet       Date:  2006-02       Impact factor: 53.242

6.  In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

Authors:  Stephen S Fong; Anthony P Burgard; Christopher D Herring; Eric M Knight; Frederick R Blattner; Costas D Maranas; Bernhard O Palsson
Journal:  Biotechnol Bioeng       Date:  2005-09-05       Impact factor: 4.530

7.  Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation.

Authors:  Sang Jun Lee; Dong-Yup Lee; Tae Yong Kim; Byung Hun Kim; Jinwon Lee; Sang Yup Lee
Journal:  Appl Environ Microbiol       Date:  2005-12       Impact factor: 4.792

8.  Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes.

Authors:  Stephen S Fong; Annik Nanchen; Bernhard O Palsson; Uwe Sauer
Journal:  J Biol Chem       Date:  2005-11-30       Impact factor: 5.157

9.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110.

Authors:  A Varma; B O Palsson
Journal:  Appl Environ Microbiol       Date:  1994-10       Impact factor: 4.792

10.  Evolutionary programming as a platform for in silico metabolic engineering.

Authors:  Kiran Raosaheb Patil; Isabel Rocha; Jochen Förster; Jens Nielsen
Journal:  BMC Bioinformatics       Date:  2005-12-23       Impact factor: 3.169

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  72 in total

1.  Experimental evolution of a facultative thermophile from a mesophilic ancestor.

Authors:  Ian K Blaby; Benjamin J Lyons; Ewa Wroclawska-Hughes; Grier C F Phillips; Tyler P Pyle; Stephen G Chamberlin; Steven A Benner; Thomas J Lyons; Valérie de Crécy-Lagard; Eudes de Crécy
Journal:  Appl Environ Microbiol       Date:  2011-10-21       Impact factor: 4.792

2.  Functional integration of a metabolic network model and expression data without arbitrary thresholding.

Authors:  Paul A Jensen; Jason A Papin
Journal:  Bioinformatics       Date:  2010-12-20       Impact factor: 6.937

Review 3.  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

4.  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

Review 5.  Towards genome-scale signalling network reconstructions.

Authors:  Daniel R Hyduke; Bernhard Ø Palsson
Journal:  Nat Rev Genet       Date:  2010-04       Impact factor: 53.242

Review 6.  Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.

Authors:  Nathan E Lewis; Harish Nagarajan; Bernhard O Palsson
Journal:  Nat Rev Microbiol       Date:  2012-02-27       Impact factor: 60.633

Review 7.  The biomass objective function.

Authors:  Adam M Feist; Bernhard O Palsson
Journal:  Curr Opin Microbiol       Date:  2010-04-27       Impact factor: 7.934

8.  What is flux balance analysis?

Authors:  Jeffrey D Orth; Ines Thiele; Bernhard Ø Palsson
Journal:  Nat Biotechnol       Date:  2010-03       Impact factor: 54.908

9.  Computationally efficient flux variability analysis.

Authors:  Steinn Gudmundsson; Ines Thiele
Journal:  BMC Bioinformatics       Date:  2010-09-29       Impact factor: 3.169

10.  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

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