Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. Howeve...
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| Vydáno v: | Metabolites Ročník 9; číslo 4; s. 76 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
MDPI AG
18.04.2019
MDPI |
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| ISSN: | 2218-1989, 2218-1989 |
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| Abstract | The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized. |
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| AbstractList | The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized. The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized. |
| Author | Beale, David J. Paten, Amy M. Swarup, Sanjay Schirra, Horst J. Wishart, David Pinu, Farhana R. Kouremenos, Konstantinos |
| AuthorAffiliation | 7 Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia; h.schirra@uq.edu.au 1 The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand 8 Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; dwishart@ualberta.ca 5 Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia 9 Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada 4 Trajan Scientific and Medical, Ringwood, VIC 3134, Australia; kkouremenos@trajanscimed.com 6 Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore; sanjay@nus.edu.sg 2 Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia; david.beale@csiro.au 3 Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, A |
| AuthorAffiliation_xml | – name: 6 Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore; sanjay@nus.edu.sg – name: 9 Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada – name: 2 Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia; david.beale@csiro.au – name: 3 Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia; amy.paten@csiro.au – name: 8 Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; dwishart@ualberta.ca – name: 7 Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia; h.schirra@uq.edu.au – name: 1 The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand – name: 5 Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia – name: 4 Trajan Scientific and Medical, Ringwood, VIC 3134, Australia; kkouremenos@trajanscimed.com |
| Author_xml | – sequence: 1 givenname: Farhana R. orcidid: 0000-0002-0180-9341 surname: Pinu fullname: Pinu, Farhana R. – sequence: 2 givenname: David J. orcidid: 0000-0002-9948-9197 surname: Beale fullname: Beale, David J. – sequence: 3 givenname: Amy M. orcidid: 0000-0003-0420-2155 surname: Paten fullname: Paten, Amy M. – sequence: 4 givenname: Konstantinos surname: Kouremenos fullname: Kouremenos, Konstantinos – sequence: 5 givenname: Sanjay surname: Swarup fullname: Swarup, Sanjay – sequence: 6 givenname: Horst J. orcidid: 0000-0002-7541-246X surname: Schirra fullname: Schirra, Horst J. – sequence: 7 givenname: David orcidid: 0000-0002-3207-2434 surname: Wishart fullname: Wishart, David |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31003499$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/1752-0509-3-82 10.1104/pp.16.01942 10.1016/j.copbio.2017.10.009 10.1016/j.watres.2015.10.029 10.1093/nar/gkw1058 10.1007/s11306-015-0823-6 10.1016/j.copbio.2017.11.013 10.3389/fphys.2010.00009 10.1093/bioinformatics/bty679 10.1093/bioinformatics/btq583 10.1371/journal.pcbi.1004321 10.1038/nrmicro2737 10.1093/nar/gky992 10.1371/journal.pone.0071060 10.1002/1097-0290(2000)71:4<286::AID-BIT1018>3.0.CO;2-R 10.1038/nature14238 10.1002/cem.775 10.1021/pr9006365 10.1016/j.copbio.2017.07.008 10.1093/jxb/erv271 10.1038/nrg1272 10.1007/s11306-014-0734-y 10.1515/jib-2011-160 10.1080/14789450.2018.1476143 10.1038/35036627 10.2144/000114414 10.1093/nar/gkm961 10.1002/0471250953.bi1413s53 10.1002/0470857897.ch8 10.1371/journal.pone.0049138 10.1002/prot.23159 10.1371/journal.pone.0006386 10.1038/nbt.3870 10.1377/hlthaff.2017.1427 10.1016/j.copbio.2018.01.009 10.1093/nar/gkp552 10.1104/pp.105.060525 10.1371/journal.pone.0071462 10.3389/fbioe.2015.00038 10.1016/j.jbiotec.2009.08.010 10.2174/1871524915666160510124150 10.1186/1471-2105-7-176 10.1186/1471-2164-14-893 10.1038/nbt1492 10.1093/nar/gkp875 10.1093/nar/gky310 10.1038/sdata.2016.18 10.1093/nar/gkx1132 10.1038/nature04768 10.1038/ncomms4114 10.1093/bioinformatics/btq594 10.1186/1471-2105-7-109 10.1016/j.physrep.2014.07.001 10.1002/gepi.21808 10.1093/bioinformatics/btg042 10.1186/s12859-018-2371-3 10.1016/j.cell.2005.04.020 10.1093/bfgp/elm027 10.1002/jimd.12008 10.1128/ecosalplus.esp-0006-2018 10.1136/bmj.k2179 10.1021/ac301269r 10.1101/374520 10.1038/nbt.2488 10.1371/journal.pone.0039960 10.1093/nar/gkt338 10.1038/srep32584 10.1128/mBio.00525-18 10.1016/j.cell.2012.02.009 10.1093/bioinformatics/15.1.72 10.1093/nar/gkv1060 10.1093/bioinformatics/btr661 10.1016/j.cell.2015.11.001 10.1038/msb.2010.18 10.1038/nrg2484 10.1016/0169-2607(92)90102-D 10.1038/nature18003 10.1016/j.biosystems.2011.04.003 10.1530/JME-18-0055 10.1093/bioinformatics/btr499 10.1038/nbt1094-994 10.1128/mSystems.00013-15 10.1186/s13059-017-1359-z 10.3389/fmolb.2019.00002 10.1093/nar/gkv1031 10.1016/j.coisb.2017.08.009 10.3389/fmicb.2013.00050 10.1111/febs.13128 10.1111/j.1365-313X.2004.02016.x 10.1021/acs.analchem.8b03205 10.1007/978-1-4939-6371-3_1 10.1186/1742-4682-2-18 10.1111/j.1365-313X.2007.03293.x 10.1016/j.cbpa.2006.06.025 10.1093/ndt/gfv364 10.1016/bs.adgen.2015.11.004 10.1016/j.scitotenv.2017.07.184 10.1016/j.ymeth.2009.03.016 10.1089/omi.2014.0062 10.1016/j.cell.2016.08.007 10.1007/978-3-319-47656-8_14 10.1007/s11306-013-0612-z 10.1016/j.scitotenv.2018.03.106 10.1007/s11306-006-0037-z 10.1016/j.pnmrs.2017.11.003 10.1021/cb500609p 10.1128/JB.00740-06 10.1093/bioinformatics/btq183 10.1021/pr060124w 10.3233/ISB-2010-0415 10.1038/nbt.4072 10.1371/journal.ppat.1004786 10.1104/pp.105.060459 10.1093/database/bax002 10.1371/journal.pcbi.1004085 10.1016/j.micres.2015.01.003 10.1038/nbt.3790 10.1186/s13059-015-0841-8 10.1016/j.cels.2016.04.004 10.1002/cem.724 10.1146/annurev.genom.9.081307.164359 10.1186/1752-0509-7-64 10.1021/acssynbio.8b00140 10.1371/journal.pone.0021318 10.1093/jnci/djq306 10.1038/nbt.1614 10.1128/mBio.00204-15 10.1093/comnet/cnu016 10.1002/bit.22592 10.1371/journal.pone.0039860 10.1016/j.cell.2018.08.065 10.1049/iet-syb:20070020 10.1038/npp.2017.221 10.1093/nar/gks1195 10.1093/nar/gkv1217 10.1146/annurev-biochem-061516-044757 10.1007/978-3-319-46326-1_10 10.1371/journal.pone.0008365 10.1016/j.coisb.2017.08.007 10.1093/nar/gkx1089 10.1128/aem.60.10.3724-3731.1994 10.1016/j.jbiotec.2004.04.038 10.1038/srep40863 10.1093/nar/gky092 10.1093/nar/gkv1145 10.3390/ijms18081652 10.1038/s41540-018-0063-2 10.1007/s11306-018-1449-2 10.15252/msb.20178126 10.1371/journal.pcbi.1003580 10.1002/cem.1388 10.1016/j.drudis.2016.11.020 10.1093/bioinformatics/btg015 10.1038/nplants.2015.99 10.1186/1752-0509-7-S6-S1 10.1101/108597 10.1093/bioinformatics/18.3.405 |
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| References | Fondi (ref_90) 2015; 171 ref_136 ref_138 Tokimatsu (ref_122) 2005; 138 Lewis (ref_79) 2012; 10 ref_130 Nishino (ref_67) 2009; 144 ref_98 ref_135 ref_134 Breitling (ref_1) 2010; 1 Dihazi (ref_140) 2018; 15 Nayfach (ref_147) 2016; 166 Theriot (ref_86) 2014; 5 Thiele (ref_12) 2013; 31 Orth (ref_58) 2010; 28 Otero (ref_10) 2010; 105 Zampieri (ref_21) 2017; 6 Wierling (ref_54) 2007; 6 Nassar (ref_17) 2017; 22 Valdes (ref_155) 2018; 361 ref_127 Mardis (ref_13) 2008; 9 ref_129 Marcu (ref_99) 2017; 45 Xia (ref_119) 2013; 41 Bashiardes (ref_156) 2018; 51 ref_25 Hagemann (ref_5) 2018; 49 Biswapriya (ref_92) 2019; 62 Eriksson (ref_48) 2007; 52 Usadel (ref_124) 2005; 138 Lin (ref_123) 2011; 8 Wilkinson (ref_149) 2016; 3 Zeevi (ref_148) 2015; 163 Kaever (ref_126) 2015; 11 ref_28 Dopazo (ref_133) 2011; 27 Cisek (ref_4) 2016; 31 ref_26 Mori (ref_69) 2004; 37 Ara (ref_158) 2015; 3 Kirwan (ref_45) 2012; 84 Barabasi (ref_151) 2004; 5 Junker (ref_137) 2006; 7 Trygg (ref_42) 2002; 16 Wishart (ref_102) 2018; 46 Cho (ref_3) 2006; 10 Beale (ref_18) 2018; 14 ref_71 Tomita (ref_63) 1999; 15 Hastings (ref_89) 2019; 6 Rantalainen (ref_46) 2006; 5 Varma (ref_62) 1994; 12 Ogura (ref_157) 2015; 10 Wishart (ref_56) 2005; 5 Yizhak (ref_20) 2010; 26 Chen (ref_23) 2012; 148 Ma (ref_78) 2017; 7 Jozefczuk (ref_88) 2010; 6 Tabb (ref_145) 2010; 9 Kikuchi (ref_52) 2018; 104 Hucka (ref_72) 2003; 19 Benson (ref_103) 2013; 41 Hastings (ref_105) 2016; 44 Jeong (ref_150) 2000; 407 Mertins (ref_31) 2016; 534 Wheeler (ref_131) 2014; 38 Beale (ref_22) 2016; 88 Wilhelm (ref_146) 2009; 48 Varma (ref_59) 1994; 60 Brunk (ref_80) 2018; 36 ref_83 Ludwig (ref_16) 2018; 14 MacPherson (ref_101) 2017; 2017 Xu (ref_7) 2018; 53 Marygold (ref_95) 2016; 1478 Aderem (ref_139) 2005; 121 Reinke (ref_47) 2018; 90 Trygg (ref_44) 2011; 25 ref_87 ref_143 ref_85 ref_84 Krajewski (ref_159) 2015; 66 Trygg (ref_43) 2003; 17 Broadhurst (ref_33) 2006; 2 Zhang (ref_100) 2017; 173 Phillips (ref_164) 2018; 37 Price (ref_154) 2017; 35 Ishii (ref_64) 2004; 113 Kanehisa (ref_109) 2002; 247 Fabregat (ref_110) 2018; 46 Droste (ref_132) 2011; 105 Sahoo (ref_75) 2015; 282 Hultman (ref_40) 2015; 521 Beale (ref_51) 2018; 631–632 Caspi (ref_111) 2010; 38 Bai (ref_141) 2017; 35 Kornberg (ref_11) 2017; Volume 86 Lee (ref_61) 1992; 38 Voss (ref_57) 2003; 3 Bauer (ref_81) 2018; 4 Gilchrist (ref_9) 2006; 441 Noronha (ref_82) 2019; 47 Nishino (ref_66) 2010; 2010 Hou (ref_34) 2014; 10 Shapiro (ref_55) 2003; 19 Vizcaino (ref_108) 2016; 44 Herrgard (ref_6) 2008; 26 Howe (ref_96) 2016; 44 ref_162 ref_68 Joyce (ref_70) 2006; 188 Green (ref_121) 2013; 30 Kale (ref_106) 2016; 53 Diamandis (ref_161) 2010; 102 Friedmann (ref_27) 2016; Volume 93 Brunk (ref_19) 2016; 2 Kamburov (ref_120) 2011; 27 ref_115 Kimes (ref_39) 2013; 4 Yurkovich (ref_165) 2018; 51 ref_117 ref_116 ref_118 Chong (ref_53) 2018; 46 Xia (ref_114) 2010; 26 Shi (ref_36) 2012; 80 ref_32 LaFramboise (ref_14) 2009; 37 Sajed (ref_97) 2016; 44 Meyer (ref_160) 2015; 1 Beale (ref_50) 2017; 609 ref_113 Sinha (ref_144) 2015; 16 ref_112 Fahy (ref_107) 2018; 35 Trivedi (ref_163) 2017; 3 Nakayama (ref_65) 2005; 2 Coscia (ref_30) 2018; 175 Cooling (ref_73) 2008; 2 Padula (ref_29) 2016; 60 Arenas (ref_153) 2014; 2 Karnovsky (ref_128) 2012; 28 Grav (ref_8) 2018; 7 Haas (ref_93) 2017; 6 Ren (ref_35) 2015; 11 ref_104 Wanders (ref_91) 2019; 42 Shin (ref_24) 2014; 18 Thimm (ref_125) 2004; 37 Kuo (ref_142) 2002; 18 ref_41 ref_2 Schilling (ref_60) 2000; 71 Mallick (ref_38) 2017; 18 ref_49 Bult (ref_94) 2008; 36 Nagasaki (ref_74) 2010; 10 Janneth (ref_76) 2017; 17 Knecht (ref_77) 2016; 6 Wang (ref_15) 2009; 10 Cattaneo (ref_37) 2018; 43 Boccaletti (ref_152) 2014; 544 |
| References_xml | – ident: ref_135 doi: 10.1186/1752-0509-3-82 – volume: 173 start-page: 2041 year: 2017 ident: ref_100 article-title: Genome-wide prediction of metabolic enzymes, pathways, and gene clusters in plants publication-title: Plant Physiol. doi: 10.1104/pp.16.01942 – volume: 53 start-page: 12 year: 2018 ident: ref_7 article-title: Production of chemicals using dynamic control of metabolic fluxes publication-title: Curr. Opin. Biotechnol. doi: 10.1016/j.copbio.2017.10.009 – volume: 88 start-page: 346 year: 2016 ident: ref_22 article-title: An ‘omics’ approach towards the characterisation of laboratory scale anaerobic digesters treating municipal sewage sludge publication-title: Water Res. doi: 10.1016/j.watres.2015.10.029 – volume: 45 start-page: D440 year: 2017 ident: ref_99 article-title: YMDB 2.0: A significantly expanded version of the yeast metabolome database publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkw1058 – volume: 37 start-page: 83 year: 2004 ident: ref_69 article-title: From the sequence to cell modeling: Comprehensive functional genomics in Escherichia coli publication-title: J. Biochem. Mol. Biol. – volume: 11 start-page: 1492 year: 2015 ident: ref_35 article-title: Computational and statistical analysis of metabolomics data publication-title: Metabolomics doi: 10.1007/s11306-015-0823-6 – volume: 51 start-page: 57 year: 2018 ident: ref_156 article-title: Towards utilization of the human genome and microbiome for personalized nutrition publication-title: Curr. Opin. Biotechnol. doi: 10.1016/j.copbio.2017.11.013 – volume: 5 start-page: 139 year: 2005 ident: ref_56 article-title: Dynamic cellular automata: An alternative approach to cellular simulation publication-title: Silico Biol. – volume: 1 start-page: 9 year: 2010 ident: ref_1 article-title: What is systems biology? publication-title: Front. Physiol. doi: 10.3389/fphys.2010.00009 – volume: 35 start-page: 685 year: 2018 ident: ref_107 article-title: LipidFinder on LIPID MAPS: Peak filtering, MS searching and statistical analysis for lipidomics publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty679 – volume: 26 start-page: 2995 year: 2010 ident: ref_114 article-title: OmicsAnalyzer: A Cytoscape plug-in suite for modeling omics data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq583 – ident: ref_116 doi: 10.1371/journal.pcbi.1004321 – volume: 3 start-page: 294 year: 2017 ident: ref_163 article-title: Metabolomics for the masses: The future of metabolomics in a personalized world publication-title: New Horizons Transl. Med. – volume: 10 start-page: 291 year: 2012 ident: ref_79 article-title: Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods publication-title: Nat. Rev. Microbiol. doi: 10.1038/nrmicro2737 – volume: 47 start-page: D614 year: 2019 ident: ref_82 article-title: The Virtual Metabolic Human database: Integrating human and gut microbiome metabolism with nutrition and disease publication-title: Nucleic Acids Res doi: 10.1093/nar/gky992 – ident: ref_68 doi: 10.1371/journal.pone.0071060 – volume: 71 start-page: 286 year: 2000 ident: ref_60 article-title: Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems publication-title: Biotechnol. Bioeng. doi: 10.1002/1097-0290(2000)71:4<286::AID-BIT1018>3.0.CO;2-R – volume: 521 start-page: 208 year: 2015 ident: ref_40 article-title: Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes publication-title: Nature doi: 10.1038/nature14238 – volume: 17 start-page: 53 year: 2003 ident: ref_43 article-title: O2-PLS, a two-block (X–Y) latent variable regression (LVR) method with an integral OSC filter publication-title: J. Chemom. doi: 10.1002/cem.775 – volume: 9 start-page: 761 year: 2010 ident: ref_145 article-title: Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry publication-title: J. Proteome Res. doi: 10.1021/pr9006365 – volume: 49 start-page: 94 year: 2018 ident: ref_5 article-title: Systems and synthetic biology for the biotechnological application of cyanobacteria publication-title: Curr. Opin. Biotechnol. doi: 10.1016/j.copbio.2017.07.008 – volume: 66 start-page: 5417 year: 2015 ident: ref_159 article-title: Towards recommendations for metadata and data handling in plant phenotyping publication-title: J. Exp. Bot. doi: 10.1093/jxb/erv271 – volume: 5 start-page: 101 year: 2004 ident: ref_151 article-title: Network biology: Understanding the cell’s functional organization publication-title: Nat. Rev. Genet. doi: 10.1038/nrg1272 – volume: 11 start-page: 764 year: 2015 ident: ref_126 article-title: MarVis-Pathway: Integrative and exploratory pathway analysis of non-targeted metabolomics data publication-title: Metabolomics doi: 10.1007/s11306-014-0734-y – volume: 8 start-page: 160 year: 2011 ident: ref_123 article-title: MADMAX - Management and analysis database for multiple ~omics experiments publication-title: J. Integr. Bioinform. doi: 10.1515/jib-2011-160 – volume: 15 start-page: 463 year: 2018 ident: ref_140 article-title: Integrative omics - from data to biology publication-title: Expert Rev. Proteom. doi: 10.1080/14789450.2018.1476143 – volume: 407 start-page: 651 year: 2000 ident: ref_150 article-title: The large-scale organization of metabolic networks publication-title: Nature doi: 10.1038/35036627 – volume: 60 start-page: 229 year: 2016 ident: ref_29 article-title: Analysis of formalin-fixed, paraffin-embedded (FFPE) tissue via proteomic techniques and misconceptions of antigen retrieval publication-title: BioTechniques doi: 10.2144/000114414 – volume: 36 start-page: D724 year: 2008 ident: ref_94 article-title: The Mouse Genome Database (MGD): Mouse biology and model systems publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkm961 – volume: 53 start-page: 14.13.1 year: 2016 ident: ref_106 article-title: Metabolights: An open-access database repository for metabolomics data publication-title: Curr. Protoc. Bioinform. doi: 10.1002/0471250953.bi1413s53 – volume: 247 start-page: 91 year: 2002 ident: ref_109 article-title: The KEGG database publication-title: Novartis Found. Symp. doi: 10.1002/0470857897.ch8 – ident: ref_84 doi: 10.1371/journal.pone.0049138 – volume: 80 start-page: 61 year: 2012 ident: ref_36 article-title: Protein stability and in vivo concentration of missense mutations in phenylalanine hydroxylase publication-title: Proteins doi: 10.1002/prot.23159 – ident: ref_87 doi: 10.1371/journal.pone.0006386 – volume: 35 start-page: 747 year: 2017 ident: ref_154 article-title: A wellness study of 108 individuals using personal, dense, dynamic data clouds publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3870 – volume: 37 start-page: 710 year: 2018 ident: ref_164 article-title: Genetic Test Availability And Spending: Where Are We Now? Where Are We Going? publication-title: Health Aff. (Proj. Hope) doi: 10.1377/hlthaff.2017.1427 – volume: 51 start-page: 130 year: 2018 ident: ref_165 article-title: Quantitative -omic data empowers bottom-up systems biology publication-title: Curr. Opin. Biotechnol. doi: 10.1016/j.copbio.2018.01.009 – volume: 37 start-page: 4181 year: 2009 ident: ref_14 article-title: Single nucleotide polymorphism arrays: A decade of biological, computational and technological advances publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkp552 – volume: 138 start-page: 1289 year: 2005 ident: ref_122 article-title: KaPPA-view: A web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps publication-title: Plant Physiol. doi: 10.1104/pp.105.060525 – ident: ref_143 doi: 10.1371/journal.pone.0071462 – volume: 3 start-page: 38 year: 2015 ident: ref_158 article-title: Metabolonote: A Wiki-Based Database for Managing Hierarchical Metadata of Metabolome Analyses publication-title: Front. Bioeng. Biotechnol. doi: 10.3389/fbioe.2015.00038 – volume: 144 start-page: 212 year: 2009 ident: ref_67 article-title: In silico modeling and metabolome analysis of long-stored erythrocytes to improve blood storage methods publication-title: J. Biotechnol. doi: 10.1016/j.jbiotec.2009.08.010 – volume: 17 start-page: 72 year: 2017 ident: ref_76 article-title: Understanding the metabolic consequences of human arylsulfatase a deficiency through a computational systems biology study publication-title: Cent. Nerv. Syst. Agents Med. Chem. doi: 10.2174/1871524915666160510124150 – ident: ref_117 doi: 10.1186/1471-2105-7-176 – ident: ref_49 doi: 10.1186/1471-2164-14-893 – volume: 26 start-page: 1155 year: 2008 ident: ref_6 article-title: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology publication-title: Nat. Biotechnol. doi: 10.1038/nbt1492 – volume: 38 start-page: D473 year: 2010 ident: ref_111 article-title: The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkp875 – volume: 46 start-page: W486 year: 2018 ident: ref_53 article-title: MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky310 – volume: 3 start-page: 160018 year: 2016 ident: ref_149 article-title: The FAIR Guiding Principles for scientific data management and stewardship publication-title: Sci. Data doi: 10.1038/sdata.2016.18 – ident: ref_28 – volume: 46 start-page: D649 year: 2018 ident: ref_110 article-title: The Reactome Pathway Knowledgebase publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1132 – volume: 441 start-page: 173 year: 2006 ident: ref_9 article-title: Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4 publication-title: Nature doi: 10.1038/nature04768 – volume: 5 start-page: 3114 year: 2014 ident: ref_86 article-title: Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection publication-title: Nat. Commun. doi: 10.1038/ncomms4114 – volume: 27 start-page: 137 year: 2011 ident: ref_133 article-title: Paintomics: A web based tool for the joint visualization of transcriptomics and metabolomics data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq594 – volume: 7 start-page: 1 year: 2006 ident: ref_137 article-title: VANTED: A system for advanced data analysis and visualization in the context of biological networks publication-title: BMC Bioinform. doi: 10.1186/1471-2105-7-109 – volume: 544 start-page: 1 year: 2014 ident: ref_152 article-title: The structure and dynamics of multilayer networks publication-title: Phys. Rep. doi: 10.1016/j.physrep.2014.07.001 – volume: 38 start-page: 402 year: 2014 ident: ref_131 article-title: Poly-omic prediction of complex traits: OmicKriging publication-title: Genetic Epidemiol. doi: 10.1002/gepi.21808 – volume: 19 start-page: 677 year: 2003 ident: ref_55 article-title: Cellerator: Extending a computer algebra system to include biochemical arrows for signal transduction simulations publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg042 – ident: ref_130 doi: 10.1186/s12859-018-2371-3 – volume: 121 start-page: 511 year: 2005 ident: ref_139 article-title: Systems biology: Its practice and challenges publication-title: Cell doi: 10.1016/j.cell.2005.04.020 – volume: 6 start-page: 240 year: 2007 ident: ref_54 article-title: Resources, standards and tools for systems biology publication-title: Brief. Funct. Genom. doi: 10.1093/bfgp/elm027 – volume: 42 start-page: 197 year: 2019 ident: ref_91 article-title: Translational Metabolism: A multidisciplinary approach towards precision diagnosis of inborn errors of metabolism in the omics era publication-title: J. Inherit. Metab. Dis. doi: 10.1002/jimd.12008 – ident: ref_98 doi: 10.1128/ecosalplus.esp-0006-2018 – volume: 361 start-page: k2179 year: 2018 ident: ref_155 article-title: Role of the gut microbiota in nutrition and health publication-title: BMJ (Clin. Res. Ed.) doi: 10.1136/bmj.k2179 – volume: 84 start-page: 7064 year: 2012 ident: ref_45 article-title: Building multivariate systems biology models publication-title: Anal. Chem. doi: 10.1021/ac301269r – ident: ref_25 doi: 10.1101/374520 – volume: 31 start-page: 419 year: 2013 ident: ref_12 article-title: A community-driven global reconstruction of human metabolism publication-title: Nat. Biotechnol. doi: 10.1038/nbt.2488 – ident: ref_113 doi: 10.1371/journal.pone.0039960 – volume: 41 start-page: W63 year: 2013 ident: ref_119 article-title: INMEX--a web-based tool for integrative meta-analysis of expression data publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt338 – volume: 6 start-page: 32584 year: 2016 ident: ref_77 article-title: Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls publication-title: Sci. Rep. doi: 10.1038/srep32584 – ident: ref_26 doi: 10.1128/mBio.00525-18 – volume: 148 start-page: 1293 year: 2012 ident: ref_23 article-title: Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes publication-title: Cell doi: 10.1016/j.cell.2012.02.009 – volume: 15 start-page: 72 year: 1999 ident: ref_63 article-title: E-CELL: Software environment for whole-cell simulation publication-title: Bioinformatics doi: 10.1093/bioinformatics/15.1.72 – volume: 44 start-page: D495 year: 2016 ident: ref_97 article-title: ECMDB 2.0: A richer resource for understanding the biochemistry of E. coli publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv1060 – volume: 28 start-page: 373 year: 2012 ident: ref_128 article-title: Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr661 – volume: 163 start-page: 1079 year: 2015 ident: ref_148 article-title: Personalized nutrition by prediction of glycemic responses publication-title: Cell doi: 10.1016/j.cell.2015.11.001 – volume: 6 start-page: 364 year: 2010 ident: ref_88 article-title: Metabolomic and transcriptomic stress response of Escherichia coli publication-title: Mol. Syst. Biol. doi: 10.1038/msb.2010.18 – volume: 10 start-page: 57 year: 2009 ident: ref_15 article-title: RNA-Seq: A revolutionary tool for transcriptomics publication-title: Nat. Rev. Genet. doi: 10.1038/nrg2484 – volume: 38 start-page: 195 year: 1992 ident: ref_61 article-title: A Macintosh software package for simulation of human red blood cell metabolism publication-title: Comput. Methods Programs Biomed. doi: 10.1016/0169-2607(92)90102-D – volume: 534 start-page: 55 year: 2016 ident: ref_31 article-title: Proteogenomics connects somatic mutations to signalling in breast cancer publication-title: Nature doi: 10.1038/nature18003 – volume: 105 start-page: 154 year: 2011 ident: ref_132 article-title: Visualizing multi-omics data in metabolic networks with the software Omix—A case study publication-title: Biosystems doi: 10.1016/j.biosystems.2011.04.003 – volume: 62 start-page: R21 year: 2019 ident: ref_92 article-title: Integrated omics: Tools, advances and future approaches publication-title: J. Mol. Endocrinol. doi: 10.1530/JME-18-0055 – volume: 27 start-page: 2917 year: 2011 ident: ref_120 article-title: Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr499 – volume: 12 start-page: 994 year: 1994 ident: ref_62 article-title: Metabolic flux balancing: Basic concepts, scientific and practical use publication-title: Bio/Technology doi: 10.1038/nbt1094-994 – ident: ref_83 doi: 10.1128/mSystems.00013-15 – volume: 18 start-page: 228 year: 2017 ident: ref_38 article-title: Experimental design and quantitative analysis of microbial community multiomics publication-title: Genome Biol. doi: 10.1186/s13059-017-1359-z – volume: 6 start-page: 364 year: 2019 ident: ref_89 article-title: Multi-Omics and Genome-Scale Modeling Reveal a Metabolic Shift During C. elegans Aging publication-title: Front. Mol. Biosci. doi: 10.3389/fmolb.2019.00002 – volume: 44 start-page: D1214 year: 2016 ident: ref_105 article-title: ChEBI in 2016: Improved services and an expanding collection of metabolites publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv1031 – volume: 6 start-page: 37 year: 2017 ident: ref_93 article-title: Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology publication-title: Curr. Opin. Syst. Biol. doi: 10.1016/j.coisb.2017.08.009 – volume: 4 start-page: 50 year: 2013 ident: ref_39 article-title: Metagenomic analysis and metabolite profiling of deep-sea sediments from the Gulf of Mexico following the Deepwater Horizon oil spill publication-title: Front. Microbiol. doi: 10.3389/fmicb.2013.00050 – volume: 3 start-page: 367 year: 2003 ident: ref_57 article-title: Steady state analysis of metabolic pathways using Petri nets publication-title: Silico Biol. – volume: 282 start-page: 297 year: 2015 ident: ref_75 article-title: Modeling the effects of commonly used drugs on human metabolism publication-title: FEBS J. doi: 10.1111/febs.13128 – ident: ref_136 – volume: 37 start-page: 914 year: 2004 ident: ref_125 article-title: MAPMAN: A user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes publication-title: Plant J. Cell Mol. Biol. doi: 10.1111/j.1365-313X.2004.02016.x – volume: 90 start-page: 13400 year: 2018 ident: ref_47 article-title: OnPLS-based multi-block data integration: A multivariate approach to interrogating biological interactions in asthma publication-title: Anal. Chem. doi: 10.1021/acs.analchem.8b03205 – volume: 30 start-page: 523 year: 2013 ident: ref_121 article-title: Causal analysis approaches in Ingenuity Pathway Analysis publication-title: Bioinformatics – volume: 1478 start-page: 1 year: 2016 ident: ref_95 article-title: Using FlyBase, a database of Drosophila genes and genomes publication-title: Methods Mol. Biol. (Clifton, N.J.) doi: 10.1007/978-1-4939-6371-3_1 – volume: 2 start-page: 18 year: 2005 ident: ref_65 article-title: Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition publication-title: Theor. Biol. Med. Model. doi: 10.1186/1742-4682-2-18 – volume: 52 start-page: 1181 year: 2007 ident: ref_48 article-title: Data integration in plant biology: The O2PLS method for combined modeling of transcript and metabolite data publication-title: Plant J. doi: 10.1111/j.1365-313X.2007.03293.x – volume: 10 start-page: 294 year: 2006 ident: ref_3 article-title: The application of systems biology to drug discovery publication-title: Curr. Opin. Chem. Biol. doi: 10.1016/j.cbpa.2006.06.025 – volume: 31 start-page: 2003 year: 2016 ident: ref_4 article-title: The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease publication-title: Nephrol. Dial. Transplant. doi: 10.1093/ndt/gfv364 – volume: Volume 93 start-page: 147 year: 2016 ident: ref_27 article-title: Chapter Three—Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases publication-title: Advances in Genetics doi: 10.1016/bs.adgen.2015.11.004 – volume: 609 start-page: 842 year: 2017 ident: ref_50 article-title: A multi-omics based ecological analysis of coastal marine sediments from Gladstone, in Australia’s Central Queensland, and Heron Island, a nearby fringing platform reef publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2017.07.184 – volume: 48 start-page: 249 year: 2009 ident: ref_146 article-title: RNA-Seq—Quantitative measurement of expression through massively parallel RNA-sequencing publication-title: Methods doi: 10.1016/j.ymeth.2009.03.016 – volume: 18 start-page: 682 year: 2014 ident: ref_24 article-title: Novel Multivariate Methods for Integration of Genomics and Proteomics Data: Applications in a Kidney Transplant Rejection Study publication-title: OMICS J. Integr. Biol. doi: 10.1089/omi.2014.0062 – volume: 166 start-page: 1103 year: 2016 ident: ref_147 article-title: Toward accurate and quantitative comparative metagenomics publication-title: Cell doi: 10.1016/j.cell.2016.08.007 – ident: ref_32 doi: 10.1007/978-3-319-47656-8_14 – volume: 2010 start-page: 642420 year: 2010 ident: ref_66 article-title: A metabolic model of human erythrocytes: Practical application of the E-Cell Simulation Environment publication-title: J. Biomed. Biotechnol. – volume: 10 start-page: 589 year: 2014 ident: ref_34 article-title: Regularized projection pursuit for data with a small sample-to-variable ratio publication-title: Metabolomics doi: 10.1007/s11306-013-0612-z – volume: 631–632 start-page: 1328 year: 2018 ident: ref_51 article-title: Seasonal metabolic analysis of marine sediments collected from Moreton Bay in South East Queensland, Australia, using a multi-omics-based approach publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2018.03.106 – volume: 2 start-page: 171 year: 2006 ident: ref_33 article-title: Statistical strategies for avoiding false discoveries in metabolomics and related experiments publication-title: Metabolomics doi: 10.1007/s11306-006-0037-z – volume: 104 start-page: 56 year: 2018 ident: ref_52 article-title: Environmental metabolomics with data science for investigating ecosystem homeostasis publication-title: Prog. Nuclear Magn. Reson. Spectrosc. doi: 10.1016/j.pnmrs.2017.11.003 – volume: 10 start-page: 1908 year: 2015 ident: ref_157 article-title: Metabolic dynamics analysis by massive data integration: Application to tsunami-affected field soils in Japan publication-title: ACS Chem. Biol. doi: 10.1021/cb500609p – volume: 188 start-page: 8259 year: 2006 ident: ref_70 article-title: Experimental and computational assessment of conditionally essential genes in Escherichia coli publication-title: J. Bacteriol. doi: 10.1128/JB.00740-06 – volume: 26 start-page: i255 year: 2010 ident: ref_20 article-title: Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq183 – volume: 5 start-page: 2642 year: 2006 ident: ref_46 article-title: Statistically integrated metabonomic−proteomic studies on a human prostate cancer xenograft model in mice publication-title: J. Proteome Res. doi: 10.1021/pr060124w – volume: 10 start-page: 5 year: 2010 ident: ref_74 article-title: Cell Illustrator 4.0: A computational platform for systems biology publication-title: Silico Biol. doi: 10.3233/ISB-2010-0415 – volume: 36 start-page: 272 year: 2018 ident: ref_80 article-title: Recon3D enables a three-dimensional view of gene variation in human metabolism publication-title: Nat. Biotechnol. doi: 10.1038/nbt.4072 – ident: ref_2 doi: 10.1371/journal.ppat.1004786 – volume: 138 start-page: 1195 year: 2005 ident: ref_124 article-title: Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of coresponding genes, and comparison with known responses publication-title: Plant Physiol. doi: 10.1104/pp.105.060459 – volume: 2017 start-page: bax002 year: 2017 ident: ref_101 article-title: Outreach and online training services at the Saccharomyces Genome Database publication-title: Database doi: 10.1093/database/bax002 – ident: ref_134 doi: 10.1371/journal.pcbi.1004085 – volume: 171 start-page: 52 year: 2015 ident: ref_90 article-title: Multi -omics and metabolic modelling pipelines: Challenges and tools for systems microbiology publication-title: Microbiol. Res. doi: 10.1016/j.micres.2015.01.003 – volume: 35 start-page: 406 year: 2017 ident: ref_141 article-title: Discovering and linking public omics data sets using the Omics Discovery Index publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3790 – volume: 16 start-page: 276 year: 2015 ident: ref_144 article-title: The microbiome quality control project: Baseline study design and future directions publication-title: Genome Biol. doi: 10.1186/s13059-015-0841-8 – volume: 2 start-page: 335 year: 2016 ident: ref_19 article-title: Characterizing Strain Variation in Engineered E.coli Using a Multi-Omics-Based Workflow publication-title: Cell Syst. doi: 10.1016/j.cels.2016.04.004 – volume: 16 start-page: 283 year: 2002 ident: ref_42 article-title: O2-PLS for qualitative and quantitative analysis in multivariate calibration publication-title: J. Chemom. doi: 10.1002/cem.724 – volume: 9 start-page: 387 year: 2008 ident: ref_13 article-title: Next-Generation DNA Sequencing Methods publication-title: Annu. Rev. Genom. Hum. Genet. doi: 10.1146/annurev.genom.9.081307.164359 – ident: ref_41 doi: 10.1186/1752-0509-7-64 – volume: 7 start-page: 2148 year: 2018 ident: ref_8 article-title: Minimizing clonal variation during mammalian cell line engineering for improved systems biology data generation publication-title: ACS Synth. Biol. doi: 10.1021/acssynbio.8b00140 – ident: ref_115 doi: 10.1371/journal.pone.0021318 – volume: 102 start-page: 1462 year: 2010 ident: ref_161 article-title: Cancer biomarkers: Can we turn recent failures into success? publication-title: J. Natl. Cancer Inst. doi: 10.1093/jnci/djq306 – volume: 28 start-page: 245 year: 2010 ident: ref_58 article-title: What is flux balance analysis? publication-title: Nat. Biotechnol. doi: 10.1038/nbt.1614 – ident: ref_85 doi: 10.1128/mBio.00204-15 – volume: 2 start-page: 203 year: 2014 ident: ref_153 article-title: Multilayer networks publication-title: J. Complex Netw. doi: 10.1093/comnet/cnu016 – volume: 105 start-page: 439 year: 2010 ident: ref_10 article-title: Industrial Systems Biology publication-title: Biotechnol. Bioeng. doi: 10.1002/bit.22592 – ident: ref_127 doi: 10.1371/journal.pone.0039860 – volume: 175 start-page: 159 year: 2018 ident: ref_30 article-title: Multi-level Proteomics Identifies CT45 as a Chemosensitivity Mediator and Immunotherapy Target in Ovarian Cancer publication-title: Cell doi: 10.1016/j.cell.2018.08.065 – volume: 2 start-page: 73 year: 2008 ident: ref_73 article-title: Modelling biological modularity with CellML publication-title: IET Syst. Biol. doi: 10.1049/iet-syb:20070020 – volume: 43 start-page: 227 year: 2018 ident: ref_37 article-title: Integrating ‘omics’ approaches to prioritize new pathogenetic mechanisms for mental disorders publication-title: Neuropsychopharmacology doi: 10.1038/npp.2017.221 – volume: 41 start-page: D36 year: 2013 ident: ref_103 article-title: GenBank publication-title: Nucleic Acids Res. doi: 10.1093/nar/gks1195 – volume: 44 start-page: D774 year: 2016 ident: ref_96 article-title: WormBase 2016: Expanding to enable helminth genomic research publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv1217 – volume: Volume 86 start-page: 245 year: 2017 ident: ref_11 article-title: Systems Biology of Metabolism publication-title: Annual Review of Biochemistry doi: 10.1146/annurev-biochem-061516-044757 – ident: ref_112 doi: 10.1007/978-3-319-46326-1_10 – ident: ref_138 doi: 10.1371/journal.pone.0008365 – volume: 6 start-page: 28 year: 2017 ident: ref_21 article-title: Metabolomics-driven understanding of genotype-phenotype relations in model organisms publication-title: Curr. Opin. Syst. Biol. doi: 10.1016/j.coisb.2017.08.007 – volume: 46 start-page: D608 year: 2018 ident: ref_102 article-title: HMDB 4.0: The human metabolome database for 2018 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1089 – volume: 60 start-page: 3724 year: 1994 ident: ref_59 article-title: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 publication-title: Appl. Environ. Microbiol. doi: 10.1128/aem.60.10.3724-3731.1994 – volume: 113 start-page: 281 year: 2004 ident: ref_64 article-title: Toward large-scale modeling of the microbial cell for computer simulation publication-title: J. Biotechnol. doi: 10.1016/j.jbiotec.2004.04.038 – volume: 7 start-page: 40863 year: 2017 ident: ref_78 article-title: Reliable and efficient solution of genome-scale models of metabolism and macromolecular expression publication-title: Sci. Rep. doi: 10.1038/srep40863 – ident: ref_104 doi: 10.1093/nar/gky092 – volume: 44 start-page: D447 year: 2016 ident: ref_108 article-title: 2016 update of the PRIDE database and its related tools publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv1145 – ident: ref_162 doi: 10.3390/ijms18081652 – volume: 4 start-page: 27 year: 2018 ident: ref_81 article-title: From metagenomic data to personalized in silico microbiotas: Predicting dietary supplements for Crohn’s disease publication-title: NPJ Syst. Biol. Appl. doi: 10.1038/s41540-018-0063-2 – volume: 14 start-page: 152 year: 2018 ident: ref_18 article-title: Review of recent developments in GC–MS approaches to metabolomics-based research publication-title: Metabolomics doi: 10.1007/s11306-018-1449-2 – volume: 14 start-page: 23 year: 2018 ident: ref_16 article-title: Data-independent acquisition-based SWATH-MS for quantitative proteomics: A tutorial publication-title: Mol. Syst. Biol. doi: 10.15252/msb.20178126 – ident: ref_118 doi: 10.1371/journal.pcbi.1003580 – volume: 25 start-page: 441 year: 2011 ident: ref_44 article-title: OnPLS—A novel multiblock method for the modelling of predictive and orthogonal variation publication-title: J. Chemom. doi: 10.1002/cem.1388 – volume: 22 start-page: 463 year: 2017 ident: ref_17 article-title: UPLC–MS for metabolomics: A giant step forward in support of pharmaceutical research publication-title: Drug Discov. Today doi: 10.1016/j.drudis.2016.11.020 – volume: 19 start-page: 524 year: 2003 ident: ref_72 article-title: The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg015 – volume: 1 start-page: 15099 year: 2015 ident: ref_160 article-title: Encouraging metadata curation in the Diversity Seek initiative publication-title: Nature Plants doi: 10.1038/nplants.2015.99 – ident: ref_71 doi: 10.1186/1752-0509-7-S6-S1 – ident: ref_129 doi: 10.1101/108597 – volume: 18 start-page: 405 year: 2002 ident: ref_142 article-title: Analysis of matched mRNA measurements from two different microarray technologies publication-title: Bioinformatics doi: 10.1093/bioinformatics/18.3.405 |
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