Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis

Bioinformatics

Volumn 34, Issue 22, 2018, Pages 3882-3888

  Tian, Mingyuan ^a,b   Reed, Jennifer L ^a,b  

^a University of Wisconsin Madison   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOINFORMATICS; ERROR; ESCHERICHIA COLI; NONHUMAN; PREDICTION; QUANTITATIVE ANALYSIS; SACCHAROMYCES CEREVISIAE; ALGORITHM; BIOLOGICAL MODEL; METABOLIC FLUX ANALYSIS; METABOLISM; PROTEOMICS;

TRANSCRIPTOME;

ALGORITHMS; ESCHERICHIA COLI; METABOLIC FLUX ANALYSIS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; PROTEOMICS; SACCHAROMYCES CEREVISIAE; TRANSCRIPTOME;

EID: 85052975355     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bty445     Document Type: Article

References (34)

1. A kesson,M. et al. (2004) Integration of gene expression data into genome-scale metabolic models. Metab. Eng., 6, 285-293.
2. Becker,S.A. and Palsson,B.O. (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol., 4, e1000082.
3. van Berlo,R.J.P. et al. (2011) Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans. Comput. Biol. Bioinform., 8, 206-216.
4. Bosi,E. et al. (2016) Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc. Natl. Acad. Sci. USA, 113, E3801-E3809.
5. Burgard,A.P. et al. (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84, 647-657.
6. Chandrasekaran,S. and Price,N.D. (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA, 107, 17845-17850.
7. Chavali,A.K. et al. (2012) A metabolic network approach for the identification and prioritization of antimicrobial drug targets. Trends Microbiol., 20, 113-123.
8. Colijn,C. et al. (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol., 5, e1000489.
9. Cotten,C. and Reed,J.L. (2013) Constraint-based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering. Biotechnol. J., 8, 595-604.
10. Gerosa,L. et al. (2015) Pseudo-transition analysis identifies the key regulators of dynamic metabolic adaptations from steady-state data. Cell Syst., 1, 270-282.
11. Grant, M. and Boyd,S. (2008) Graph implementations for nonsmooth convex programs. In: Blondel, V. et al (eds), Recent Advances in Learning and Control, Lecture Notes in Control and Information Sciences. Springer Limited, Berlin, pp. 95-110.
12. Grant,M. and Boyd,S. (2014) CVX: Matlab Software for Disciplined Convex Programming, Version 2.1. http://cvxr.com/cvx.
13. Hackett,S.R. et al. (2016) Systems-level analysis of mechanisms regulating yeast metabolic flux. Science, 354, aaf2786.
14. Hamilton,J.J. and Reed,J.L. (2012) Identification of functional differences in metabolic networks using comparative genomics and constraint-based models. PLoS One, 7, e34670.
15. Hoppe,A. (2012) What mRNA abundances can tell us about metabolism. Metabolites, 2, 614-631.
16. Ishii,N. et al. (2007) Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science, 316, 593-597.
17. Jensen,P.A. and Papin,J.A. (2011) Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics, 27, 541-547.
18. Jouhten,P. et al. (2008) Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A. BMC Syst. Biol., 2, 60.
19. Kim,H.U. et al. (2013) Flux-coupled genes and their use in metabolic flux analysis. Biotechnol. J., 8, 1035-1042.
20. Kim,H.U. et al. (2010) Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol. Biosyst., 6, 339-348.
21. Kim,J. et al. (2011) Large-scale bi-level strain design approaches and mixed-integer programming solution techniques. PLoS One, 6, e24162.
22. Lee,D.-S. et al. (2009) Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J. Bacteriol., 191, 4015-4024.
23. Lehninger,A.L. et al. (2008) Lehninger Principles of Biochemistry. W.H. Freeman, New York.
24. Lewis,N.E. et al. (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol., 6, 390.
25. Long,M.R. and Reed,J.L. (2017) Improving flux predictions by integrating data from multiple strains. Bioinformatics, 33, 893-900.
26. Machado,D. and Herrgard,M. (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput. Biol., 10, e1003580.
27. Magnu sdo ttir,S. et al. (2017) Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol., 35, 81-89.
28. Mo,M.L. et al. (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst. Biol, 3, 37.
29. Nuccio,S.-P. and Bäumler,A.J. (2014) Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut. MBio, 5, e00929-14.
30. Orth,J.D. et al. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol., 7, 535.
31. Rintala,E. et al. (2009) Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae. BMC Genomics, 10, 461.
32. Shlomi,T. et al. (2008) Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol., 26, 1003-1010.
33. Tervo,C.J. and Reed,J.L. (2012) FOCAL: an experimental design tool for systematizing metabolic discoveries and model development. Genome Biol., 13, R116.
34. Wiebe,M.G. et al. (2008) Central carbon metabolism of Saccharomyces cerevisiae in anaerobic, oxygen-limited and fully aerobic steady-state conditions and following a shift to anaerobic conditions. FEMS Yeast Res., 8, 140-154.