RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

PLoS Computational Biology

Volumn 14, Issue 10, 2018, Pages

  Wang, Hao ^a,b   Marcišauskas, Simonas ^a   Sánchez, Benjamín J ^a   Domenzain, Iván ^a   Hermansson, Daniel ^a   Agren, Rasmus ^a   Nielsen, Jens ^a,c   Kerkhoven, Eduard J ^a  

^a CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

^b UNIVERSITY OF GOTHENBURG   (Sweden)

^c Chalmers University of Technology   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIA; HTTP; METABOLISM; METABOLITES; REPAIR;

CASE-STUDIES; CONSTRAINT-BASED MODELING; CONSTRAINTS-BASED MODELING; CURATION; GENOME-SCALE METABOLIC MODELING; METABOLIC NETWORK RECONSTRUCTION; MODEL CONSTRAINTS; MODELING RECONSTRUCTION; S. COELICOLOR; STREPTOMYCES COELICOLOR;

MATLAB;

ANTIBIOTIC AGENT;

ARTICLE; AUTOMATION; BACTERIAL METABOLISM; CASE STUDY; CONTROLLED STUDY; GENE EDITING; GENETIC DATABASE; GENOME; GENOME-SCALE METABOLIC MODEL; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; METABOLISM; METACYC PATHWAY DATABASE; NONHUMAN; PERFORMANCE; SECONDARY METABOLISM; STREPTOMYCES COELICOLOR; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; GENETICS; PROCEDURES; SOFTWARE;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, GENETIC; GENE EDITING; METABOLIC NETWORKS AND PATHWAYS; MODELS, GENETIC; SOFTWARE; STREPTOMYCES COELICOLOR;

EID: 85055635316     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1006541     Document Type: Article

References (47)

1. 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.2009.203 pmid: 20057383
2. O’Brien EJ, Monk JM, Palsson BO, Using genome-scale models to predict biological capabilities. Cell. Elsevier Inc.; 2015;161: 971–987. doi: 10.1016/j.cell.2015.05.019 pmid: 26000478
3. Simeonidis E, Price ND, Genome-scale modeling for metabolic engineering. J Ind Microbiol Biotechnol. 2015;42: 327–38. doi: 10.1007/s10295-014-1576-3 pmid: 25578304
4. Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol. Nature Publishing Group; 2016;35: 81–89. doi: 10.1038/nbt.3703 pmid: 27893703
5. Mardinoglu A, Gatto F, Nielsen J, Genome-scale modeling of human metabolism—a systems biology approach. Biotechnol J. 2013;8: 985–996. doi: 10.1002/biot.201200275 pmid: 23613448
6. Nielsen J., Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. Cell Metab. Elsevier Inc.; 2017;25: 572–579. doi: 10.1016/j.cmet.2017.02.002 pmid: 28273479
7. Hwang K- S, Kim HU, Charusanti P, Palsson BØ, Lee SY, Systems biology and biotechnology of Streptomyces species for the production of secondary metabolites. Biotechnol Adv. Elsevier Inc.; 2014;32: 255–268. doi: 10.1016/j.biotechadv.2013.10.008 pmid: 24189093
8. Kim WJ, Kim HU, Lee SY, Current state and applications of microbial genome-scale metabolic models. Curr Opin Syst Biol. Elsevier Ltd; 2017;2: 10–18. doi: 10.1016/j.coisb.2017.03.001
9. 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: e1002980. doi: 10.1371/journal.pcbi.1002980 pmid: 23555215
10. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K, KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45: D353–D361. doi: 10.1093/nar/gkw1092 pmid: 27899662
11. Thompson RA, Dahal S, Garcia S, Nookaew I, Trinh CT, Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome. Biotechnol Biofuels. BioMed Central; 2016;9: 194. doi: 10.1186/s13068-016-0607-x pmid: 27602057
12. Hamilton JJ, Calixto Contreras M, Reed JL, Thermodynamics and H2 Transfer in a Methanogenic, Syntrophic Community. PLoS Comput Biol. 2015;11: 1–20. doi: 10.1371/journal.pcbi.1004364 pmid: 26147299
13. Shoaie S, Ghaffari P, Kovatcheva-Datchary P, Mardinoglu A, Sen P, Pujos-Guillot E, et al. Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome. Cell Metab. 2015;22: 320–331. doi: 10.1016/j.cmet.2015.07.001 pmid: 26244934
14. Levering J, Broddrick J, Dupont CL, Peers G, Beeri K, Mayers J, et al. Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom. PLoS One. 2016;11: 1–22. doi: 10.1371/journal.pone.0155038 pmid: 27152931
15. Chiappino-Pepe A, Tymoshenko S, Ataman M, Soldati-Favre D, Hatzimanikatis V, Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks. PLoS Comput Biol. 2017;13: 1–24. doi: 10.1371/journal.pcbi.1005397 pmid: 28333921
16. Sharma M, Shaikh N, Yadav S, Singh S, Garg P, Nowicki C, et al. A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets. Mol BioSyst. Royal Society of Chemistry; 2017;277: 38245–38253. doi: 10.1039/C6MB00823B pmid: 28367572
17. Tymoshenko S, Oppenheim RD, Agren R, Nielsen J, Soldati-Favre D, Hatzimanikatis V, Maranas CD, Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis. PLoS Comput Biol. 2015;11: e1004261. doi: 10.1371/journal.pcbi.1004261 pmid: 26001086
18. Ledesma-Amaro R, Kerkhoven EJ, Revuelta JL, Nielsen J, Genome scale metabolic modeling of the riboflavin overproducer Ashbya gossypii. Biotechnol Bioeng. 2014;111: 1191–1199. doi: 10.1002/bit.25167 pmid: 24374726
19. Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J, Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol. 2014;10: 1–13. doi: 10.1002/msb.145122 pmid: 24646661
20. Hur W, Ryu JY, Kim HU, Hong SW, Lee EB, Lee SY, et al. Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133. Sci Rep. Nature Publishing Group; 2017;7: 45557. doi: 10.1038/srep45557 pmid: 28367990
21. Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J, Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. Nature Publishing Group; 2014;5: 1–11. doi: 10.1038/ncomms4083 pmid: 24419221
22. Blais EM, Rawls KD, Dougherty B V, Li ZI, Kolling GL, Ye P, et al. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat Commun. Nature Publishing Group; 2017;8: 1–15. doi: 10.1038/s41467-016-0009-6 pmid: 28232747
23. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc. 2011;6: 1290–1307. doi: 10.1038/nprot.2011.308 pmid: 21886097
24. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgard MJ, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc. 2007;2: 727–38. doi: 10.1038/nprot.2007.99 pmid: 17406635
25. Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. arXiv. 2017; 1710.04038v2
26. Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2016;44: D471–D480. doi: 10.1093/nar/gkv1164 pmid: 26527732
27. Bentley S, Chater K, Cerdeño-Tárraga A- M, Challis GL, Thomson NR, James KD, et al. Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2). Nature. 2002;417: 141–147. doi: 10.1038/417141a pmid: 12000953
28. Wang H, Fewer DP, Holm L, Rouhiainen L, Sivonen K, Atlas of nonribosomal peptide and polyketide biosynthetic pathways reveals common occurrence of nonmodular enzymes. Proc Natl Acad Sci U S A. 2014;111: 9259–64. doi: 10.1073/pnas.1401734111 pmid: 24927540
29. Challis GL, Exploitation of the Streptomyces coelicolor A3(2) genome sequence for discovery of new natural products and biosynthetic pathways. J Ind Microbiol Biotechnol. 2014;41: 219–32. doi: 10.1007/s10295-013-1383-2 pmid: 24322202
30. Borodina I, Krabben P, Nielsen J, Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism. Genome Res. 2005;15: 820–9. doi: 10.1101/gr.3364705 pmid: 15930493
31. Alam MT, Merlo ME, Hodgson DA, Wellington EMH, Takano E, Breitling R, Metabolic modeling and analysis of the metabolic switch in Streptomyces coelicolor. BMC Genomics. 2010;11: 202. doi: 10.1186/1471-2164-11-202 pmid: 20338070
32. Kim M, Sang Yi J, Kim J, Kim J- N, Kim MW, Kim B- G, Reconstruction of a high-quality metabolic model enables the identification of gene overexpression targets for enhanced antibiotic production in Streptomyces coelicolor A3(2). Biotechnol J. 2014;9: 1185–94. doi: 10.1002/biot.201300539 pmid: 24623710
33. Bordel S, Agren R, Nielsen J, Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol. 2010;6: e1000859. doi: 10.1371/journal.pcbi.1000859 pmid: 20657658
34. Choi HS, Lee SY, Kim TY, Woo HM, In Silico Identification of Gene Amplification Targets for Improvement of Lycopene Production. Appl Environ Microbiol. 2010;76: 3097–3105. doi: 10.1128/AEM.00115-10 pmid: 20348305
35. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR, COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 2013;7: 74. doi: 10.1186/1752-0509-7-74 pmid: 23927696
36. Radivoyevitch T, Venkateswaran V. SBMLR: SBML-R Interface and Analysis Tools. 2015
37. Xu Z, Wang Y, Chater KF, Ou H, Xu HH, Deng Z, Drake HL, et al. Large-Scale Transposition Mutagenesis of Streptomyces coelicolor Identifies Hundreds of Genes Influencing Antibiotic Biosynthesis. Appl Environ Microbiol. 2017;83: e02889–16. doi: 10.1128/AEM.02889-16 pmid: 28062460
38. Pabinger S, Rader R, Agren R, Nielsen J, Trajanoski Z, MEMOSys: Bioinformatics platform for genome-scale metabolic models. BMC Syst Biol. 2011;5: 20. doi: 10.1186/1752-0509-5-20 pmid: 21276275
39. Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J, Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol. 2012;8. doi: 10.1371/journal.pcbi.1002518 pmid: 22615553
40. Sauls JT, Buescher JM, Assimilating genome-scale metabolic reconstructions with modelBorgifier. Bioinformatics. 2014;30: 1036–1038. doi: 10.1093/bioinformatics/btt747 pmid: 24371155
41. Moretti S, Martin O, Van Du Tran T, Bridge A, Morgat A, Pagni M, MetaNetX/MNXref–reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks. Nucleic Acids Res. 2016;44: D523–D526. doi: 10.1093/nar/gkv1117 pmid: 26527720
42. Lieven C, Beber ME, Olivier BG, Bergmann FT, Chauhan S, Correia K, et al. Memote: A community driven effort towards a standardized genome-scale metabolic model test suite. bioRxiv. 2018; 350991. doi: 10.1101/350991
43. Li W, Godzik A, Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22: 1658–1659. doi: 10.1093/bioinformatics/btl158 pmid: 16731699
44. Katoh K, Standley DM, MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30: 772–780. doi: 10.1093/molbev/mst010 pmid: 23329690
45. Finn RD, Clements J, Eddy SR, HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 2011;39: 29–37. doi: 10.1093/nar/gkr367 pmid: 21593126
46. Bornstein BJ, Keating SM, Jouraku A, Hucka M, LibSBML: An API library for SBML. Bioinformatics. 2008;24: 880–881. doi: 10.1093/bioinformatics/btn051 pmid: 18252737
47. Medema MH, Blin K, Cimermancic P, De Jager V, Zakrzewski P, Fischbach MA, et al. AntiSMASH: Rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 2011;39: 339–346. doi: 10.1093/nar/gkr466 pmid: 21672958