Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

PLoS Computational Biology

Volumn 13, Issue 3, 2017, Pages

  Biggs, Matthew B ^a   Papin, Jason A ^a  

^a University of Virginia   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIA; GENES; METABOLISM; MOLECULES;

ESSENTIAL GENE; FLUX BALANCE ANALYSIS; GENOME-SCALE METABOLIC NETWORK; MANAGING UNCERTAINTY; METABOLIC NETWORK; METABOLIC NETWORK RECONSTRUCTION; MODEL ORGANISMS; NETWORK STRUCTURES; SMALL MOLECULES; STREPTOCOCCUS SPECIES;

FORECASTING;

ARTICLE; CONTROLLED STUDY; ENSEMBLE FLUX BALANCE ANALYSIS; GENE MAPPING; MATHEMATICAL MODEL; METABOLISM; MOLECULAR INTERACTION; NONHUMAN; PREDICTION; PSEUDOMONAS AERUGINOSA; STREPTOCOCCUS; ALGORITHM; BACTERIUM; BIOLOGICAL MODEL; COMPUTER SIMULATION; METABOLIC FLUX ANALYSIS; METABOLOME; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SOFTWARE; STATISTICAL MODEL;

BACTERIAL PROTEIN;

ALGORITHMS; BACTERIA; BACTERIAL PROTEINS; COMPUTER SIMULATION; METABOLIC FLUX ANALYSIS; METABOLIC NETWORKS AND PATHWAYS; METABOLOME; MODELS, BIOLOGICAL; MODELS, STATISTICAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOFTWARE;

EID: 85016715344     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005413     Document Type: Article

References (34)

1. Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2010;38: D473–D479. doi: 10.1093/nar/gkp87519850718
2. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL, The large-scale organization of metabolic networks. Nature. 2000;407: 651–4. doi: 10.1038/3503662711034217
3. Oberhardt MA, Palsson BØ, Papin JA, Applications of genome-scale metabolic reconstructions. Mol Syst Biol. 2009;5:320–334. doi: 10.1038/msb.2009.7719888215
4. McCloskey D, Palsson BØ, Feist AM, Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol. 2013;9: 661. doi: 10.1038/msb.2013.1823632383
5. Thiele I, Palsson BØ, A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5: 93–121. doi: 10.1038/nprot.2009.20320057383
6. Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, et al. Systems approach to refining genome annotation. Proc Natl Acad Sci U S A. 2006;103: 17480–17484. doi: 10.1073/pnas.060336410317088549
7. Burgard AP, Pharkya P, Maranas CD, OptKnock: A Bilevel Programming Framework for Identifying Gene Knockout Strategies for Microbial Strain Optimization. Biotechnol Bioeng. 2003;84: 647–657. doi: 10.1002/bit.1080314595777
8. Bartell JA, Yen P, Varga JJ, Goldberg JB, Papin JA, Comparative metabolic systems analysis of pathogenic Burkholderia. J Bacteriol. 2014;196: 210–226. doi: 10.1128/JB.00997-1324163337
9. Stolyar S, Van Dien S, Hillesland KL, Pinel N, Lie TJ, Leigh JA, et al. Metabolic modeling of a mutualistic microbial community. Mol Syst Biol. 2007;3: 92. doi: 10.1038/msb410013117353934
10. Zomorrodi AR, Maranas CD, OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol. 2012;8: e1002363. doi: 10.1371/journal.pcbi.100236322319433
11. Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome. Nature. 2012;486: 207–214. doi: 10.1038/nature1123422699609
12. Lundberg DS, Lebeis SL, Paredes SH, Yourstone S, Gehring J, Malfatti S, et al. Defining the core Arabidopsis thaliana root microbiome. Nature. 2012;488: 86–90. doi: 10.1038/nature1123722859206
13. Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL, High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28: 977–982. doi: 10.1038/nbt.167220802497
14. Plata G, Fuhrer T, Hsiao T-L, Sauer U, Vitkup D, Global probabilistic annotation of metabolic networks enables enzyme discovery. Nat Chem Biol. 2012;8(10): 848–854. doi: 10.1038/nchembio.106322960854
15. Agren R, Liu L, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J, The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum. PLoS Comput Biol. 2013;9(3): e1002980. doi: 10.1371/journal.pcbi.100298023555215
16. Pitkänen E, Jouhten P, Hou J, Syed MF, Blomberg P, Kludas J, et al. Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species. PLoS Comput Biol. 2014;10: e1003465. doi: 10.1371/journal.pcbi.100346524516375
17. Thiele I, Vlassis N, Fleming RMT, FASTGAPFILL: efficient gap filling in metabolic networks. Bioinformatics. 2014;30: 2529–2531. doi: 10.1093/bioinformatics/btu32124812336
18. Latendresse M, Efficiently gap-filling reaction networks. BMC Bioinformatics. 2014;15: 225. doi: 10.1186/1471-2105-15-22524972703
19. Turner KH, Wessel AK, Palmer GC, Murray JL, Whiteley M, Essential genome of Pseudomonas aeruginosa in cystic fibrosis sputum. Proc Natl Acad Sci. 2015;112: 4110–4115. doi: 10.1073/pnas.141967711225775563
20. Plata G, Henry CS, Vitkup D, Long-term phenotypic evolution of bacteria. Nature. 2014;517(7534): 369–372. doi: 10.1038/nature1382725363780
21. Wishart DS, DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34: D668–D672. doi: 10.1093/nar/gkj06716381955
22. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10: 421. doi: 10.1186/1471-2105-10-42120003500
23. Kumar VS, Maranas CD, GrowMatch: An automated method for reconciling in silico/in vivo growth predictions. PLoS Comput Biol. 2009;5(3): e1000308. doi: 10.1371/journal.pcbi.100030819282964
24. Opitz D, Maclin R, Popular Ensemble Methods: An Empirical Study. J Artif Intell Res. 1999;11: 169–198.
25. Breiman L, Random Forests. Mach Learn. 2001;45: 5–32.
26. Tran LM, Rizk ML, Liao JC, Ensemble Modeling of Metabolic Networks. Biophys J. 2008;95: 5606–5617. doi: 10.1529/biophysj.108.13544218820235
27. Overbeek R, Begley T, Butler RM, Choudhuri J V, Chuang HY, Cohoon M, et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 2005;33: 5691–5702. doi: 10.1093/nar/gki86616214803
28. Dreyfuss JM, Zucker JD, Hood HM, Ocasio LR, Sachs MS, Galagan JE, Reconstruction and validation of a genome-scale metabolic model for the filamentous fungus Neurospora crassa using FARM. PLoS Comput Biol. 2013;9: e1003126. doi: 10.1371/journal.pcbi.100312623935467
29. Round JL, Mazmanian SK, The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol. 2009;9: 313–323. doi: 10.1038/nri251519343057
30. McCaughey LC, Ritchie ND, Douce GR, Evans TJ, Walker D, Efficacy of species-specific protein antibiotics in a murine model of acute Pseudomonas aeruginosa lung infection. Sci Rep. 2016;6: 30201. doi: 10.1038/srep3020127444885
31. Benedict MN, Mundy MB, Henry CS, Chia N, Price ND, Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. PLoS Comput Biol. 2014;10(10): e1003882. doi: 10.1371/journal.pcbi.100388225329157
32. Oberhardt MA, Puchałka J, Martins dos Santos VAP, Papin JA, Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS Comput Biol. 2011;7(3): e1001116. doi: 10.1371/journal.pcbi.100111621483480
33. Oberhardt MA, Goldberg JB, Hogardt M, Papin JA, Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection. J Bacteriol. 2010;192: 5534–5548. doi: 10.1128/JB.00900-1020709898
34. Oberhardt MA, Puchałka J, Fryer KE, Martins dos Santos VAP, Papin JA, Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol. 2008;190(8): 2790–2803. doi: 10.1128/JB.01583-0718192387