Literature DB >> 20430689

The biomass objective function.

Adam M Feist1, Bernhard O Palsson.   

Abstract

Flux balance analysis (FBA) is a mathematical approach for analyzing the flow of metabolites through a metabolic network. To computationally predict cell growth using FBA, one has to determine the biomass objective function that describes the rate at which all of the biomass precursors are made in the correct proportions. Here we review fundamental issues associated with its formulation and use to compute optimal growth states. Copyright 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID： 20430689      PMCID： PMC2912156          DOI： 10.1016/j.mib.2010.03.003

Source DB:  PubMed          Journal:  Curr Opin Microbiol        ISSN： 1369-5274            Impact factor:   7.934


  46 in total

1.  Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism.

Authors:  J M Savinell; B O Palsson
Journal:  J Theor Biol       Date:  1992-02-21       Impact factor: 2.691

Review 2.  The model organism as a system: integrating 'omics' data sets.

Authors:  Andrew R Joyce; Bernhard Ø Palsson
Journal:  Nat Rev Mol Cell Biol       Date:  2006-03       Impact factor: 94.444

3.  Network identification and flux quantification in the central metabolism of Saccharomyces cerevisiae under different conditions of glucose repression.

Authors:  A K Gombert; M Moreira dos Santos ; B Christensen; J Nielsen
Journal:  J Bacteriol       Date:  2001-02       Impact factor: 3.490

4.  Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model.

Authors:  Bas Teusink; Anne Wiersma; Douwe Molenaar; Christof Francke; Willem M de Vos; Roland J Siezen; Eddy J Smid
Journal:  J Biol Chem       Date:  2006-10-24       Impact factor: 5.157

5.  Branch point control by the phosphorylation state of isocitrate dehydrogenase. A quantitative examination of fluxes during a regulatory transition.

Authors:  K Walsh; D E Koshland
Journal:  J Biol Chem       Date:  1985-07-15       Impact factor: 5.157

6.  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

Review 7.  Reconstruction of biochemical networks in microorganisms.

Authors:  Adam M Feist; Markus J Herrgård; Ines Thiele; Jennie L Reed; Bernhard Ø Palsson
Journal:  Nat Rev Microbiol       Date:  2008-12-31       Impact factor: 60.633

Review 8.  Applications of genome-scale metabolic reconstructions.

Authors:  Matthew A Oberhardt; Bernhard Ø Palsson; Jason A Papin
Journal:  Mol Syst Biol       Date:  2009-11-03       Impact factor: 11.429

9.  Predicting biological system objectives de novo from internal state measurements.

Authors:  Erwin P Gianchandani; Matthew A Oberhardt; Anthony P Burgard; Costas D Maranas; Jason A Papin
Journal:  BMC Bioinformatics       Date:  2008-01-24       Impact factor: 3.169

10.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).

Authors:  Jennifer L Reed; Thuy D Vo; Christophe H Schilling; Bernhard O Palsson
Journal:  Genome Biol       Date:  2003-08-28       Impact factor: 13.583
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  198 in total

1.  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 2.  A metabolic network approach for the identification and prioritization of antimicrobial drug targets.

Authors:  Arvind K Chavali; Kevin M D'Auria; Erik L Hewlett; Richard D Pearson; Jason A Papin
Journal:  Trends Microbiol       Date:  2012-01-31       Impact factor: 17.079

3.  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

4.  Flux modules in metabolic networks.

Authors:  Arne C Müller; Alexander Bockmayr
Journal:  J Math Biol       Date:  2013-10-19       Impact factor: 2.259

5.  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

6.  Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

Authors:  Elias W Krumholz; Igor G L Libourel
Journal:  J Biol Chem       Date:  2015-06-03       Impact factor: 5.157

7.  Elimination of thermodynamically infeasible loops in steady-state metabolic models.

Authors:  Jan Schellenberger; Nathan E Lewis; Bernhard Ø Palsson
Journal:  Biophys J       Date:  2011-02-02       Impact factor: 4.033

8.  Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris.

Authors:  Cristal Zuñiga; Jennifer Levering; Maciek R Antoniewicz; Michael T Guarnieri; Michael J Betenbaugh; Karsten Zengler
Journal:  Plant Physiol       Date:  2017-09-26       Impact factor: 8.340

9.  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

10.  Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes.

Authors:  Jonathan M Monk; Anna Koza; Miguel A Campodonico; Daniel Machado; Jose Miguel Seoane; Bernhard O Palsson; Markus J Herrgård; Adam M Feist
Journal:  Cell Syst       Date:  2016-09-22       Impact factor: 10.304
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