Machine and deep learning meet genome-scale metabolic modeling

PLoS Computational Biology

Volumn 15, Issue 7, 2019, Pages

  Zampieri, Guido ^a   Vijayakumar, Supreeta ^a   Yaneske, Elisabeth ^a   Angione, Claudio ^a  

^a TEESSIDE UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP LEARNING; GENES; METABOLISM;

'OMICS'; BIOLOGICAL PHENOTYPES; COMPUTATIONAL APPROACH; CONSTRAINT-BASED; GENOME-SCALE METABOLIC MODELING; LARGE-SCALES; MACHINE-LEARNING; METABOLIC MODELLING; SCALE RELATIONSHIP; STATISTICS LEARNING;

MOLECULAR BIOLOGY;

AGNOSTIC; CONCEPTUAL FRAMEWORK; DEEP LEARNING; HUMAN; REVIEW; BIOLOGY; GENOME; GENOTYPE; MACHINE LEARNING; METABOLISM; PHENOTYPE; PROCEDURES;

COMPUTATIONAL BIOLOGY; DEEP LEARNING; GENOME; GENOTYPE; MACHINE LEARNING; METABOLIC NETWORKS AND PATHWAYS; PHENOTYPE;

EID: 85069783975     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1007084     Document Type: Review

References (119)

1. Joyce AR, Palsson BØ, The model organism as a system: integrating 'omics' data sets. Nature reviews Molecular cell biology. 2006;7(3):198. doi: 10.1038/nrm1857 pmid: 16496022
2. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D, Methods of integrating data to uncover genotype–phenotype interactions. Nature Reviews Genetics. 2015;16(2):85. doi: 10.1038/nrg3868 pmid: 25582081
3. Macaulay IC, Ponting CP, Voet T, Single-cell multiomics: multiple measurements from single cells. Trends in Genetics. 2017;33(2):155–168. doi: 10.1016/j.tig.2016.12.003 pmid: 28089370
4. Libbrecht MW, Noble WS, Machine learning applications in genetics and genomics. Nature Reviews Genetics. 2015;16(6):321. doi: 10.1038/nrg3920 pmid: 25948244
5. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface. 2018;15(141):20170387.
6. Zhang Y, Rajapakse JC, Machine learning in bioinformatics. Vol 4. Hoboken, NJ: John Wiley & Sons; 2009.
7. Leung MK, Delong A, Alipanahi B, Frey BJ, Machine learning in genomic medicine: a review of computational problems and data sets. Proceedings of the IEEE. 2016;104(1):176–197.
8. Angermueller C, Pärnamaa T, Parts L, Stegle O, Deep learning for computational biology. Molecular systems biology. 2016;12(7):878. doi: 10.15252/msb.20156651 pmid: 27474269
9. Min S, Lee B, Yoon S, Deep learning in bioinformatics. Briefings in bioinformatics. 2017;18(5):851–869. doi: 10.1093/bib/bbw068 pmid: 27473064
10. Bordbar A, Monk JM, King ZA, Palsson BO, Constraint-based models predict metabolic and associated cellular functions. Nature Reviews Genetics. 2014;15(2):107. doi: 10.1038/nrg3643 pmid: 24430943
11. Durot M, Bourguignon PY, Schachter V, Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS microbiology reviews. 2008;33(1):164–190. doi: 10.1111/j.1574-6976.2008.00146.x pmid: 19067749
12. de Oliveira Dal'Molin CG, Nielsen LK, Plant genome-scale metabolic reconstruction and modelling. Current opinion in biotechnology. 2013;24(2):271–277. doi: 10.1016/j.copbio.2012.08.007 pmid: 22947602
13. Geng J, Nielsen J, In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models. Current Opinion in Systems Biology. 2017;2:29–38.
14. Monk J, Nogales J, Palsson BO, Optimizing genome-scale network reconstructions. Nature biotechnology. 2014;32(5):447. doi: 10.1038/nbt.2870 pmid: 24811519
15. Yilmaz LS, Walhout AJ, Metabolic network modeling with model organisms. Current opinion in chemical biology. 2017;36:32–39. doi: 10.1016/j.cbpa.2016.12.025 pmid: 28088694
16. Cuperlovic-Culf M, Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites. 2018;8(1):4.
17. Vijayakumar S, Conway M, Lió P, Angione C, Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Briefings in bioinformatics. 2017;19(6):1218–1235.
18. Heino J, Tunyan K, Calvetti D, Somersalo E, Bayesian flux balance analysis applied to a skeletal muscle metabolic model. Journal of theoretical biology. 2007;248(1):91–110. doi: 10.1016/j.jtbi.2007.04.002 pmid: 17568615
19. Machado D, Herrgård MJ, Co-evolution of strain design methods based on flux balance and elementary mode analysis. Metabolic Engineering Communications. 2015;2:85–92.
20. Angione C, Lió P, Predictive analytics of environmental adaptability in multi-omic network models. Scientific reports. 2015;5:15147. doi: 10.1038/srep15147 pmid: 26482106
21. Ruppin E, Papin JA, De Figueiredo LF, Schuster S, Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. Current opinion in biotechnology. 2010;21(4):502–510. doi: 10.1016/j.copbio.2010.07.002 pmid: 20692823
22. Angione C, Conway M, Lió P, Multiplex methods provide effective integration of multi-omic data in genome-scale models. BMC bioinformatics. 2016;17(4):83.
23. Spahn PN, Hansen AH, Hansen HG, Arnsdorf J, Kildegaard HF, Lewis NE, A Markov chain model for N-linked protein glycosylation–towards a low-parameter tool for model-driven glycoengineering. Metabolic engineering. 2016;33:52–66. doi: 10.1016/j.ymben.2015.10.007 pmid: 26537759
24. Pierobon M, Sakkaff Z, Catlett JL, Buan NR, Mutual information upper bound of molecular communication based on cell metabolism. In: Signal Processing Advances in Wireless Communications (SPAWC), 2016 IEEE 17th International Workshop on. New York: IEEE; 2016. p. 1–6.
25. Bhaskar H, Hoyle DC, Singh S, Machine learning in bioinformatics: A brief survey and recommendations for practitioners. Computers in biology and medicine. 2006;36(10):1104–1125. doi: 10.1016/j.compbiomed.2005.09.002 pmid: 16226240
26. Tarca AL, Carey VJ, Chen Xw, Romero R, Drăghici S, Machine learning and its applications to biology. PLoS Comput Biol. 2007;3(6):e116. doi: 10.1371/journal.pcbi.0030116 pmid: 17604446
27. Zeng ISL, Lumley T, Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinformatics and Biology Insights. 2018;12:1177932218759292. doi: 10.1177/1177932218759292 pmid: 29497285
28. Cai Y, Gu H, Kenney T, Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization. Microbiome. 2017;5(1):110. doi: 10.1186/s40168-017-0323-1 pmid: 28859695
29. Xu R, Wunsch DC, Clustering algorithms in biomedical research: a review. IEEE Reviews in Biomedical Engineering. 2010;3:120–154. doi: 10.1109/RBME.2010.2083647 pmid: 22275205
30. Buescher JM, Driggers EM, Integration of omics: more than the sum of its parts. Cancer & metabolism. 2016;4(1):4.
31. Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC, Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in bioinformatics. 2016;17(4):628–641. doi: 10.1093/bib/bbv108 pmid: 26969681
32. Gligorijević V, Pržulj N, Methods for biological data integration: perspectives and challenges. Journal of the Royal Society Interface. 2015;12(112):20150571.
33. Hasin Y, Seldin M, Lusis A, Multi-omics approaches to disease. Genome biology. 2017;18(1):83. doi: 10.1186/s13059-017-1215-1 pmid: 28476144
34. Colomé-Tatché M, Theis F, Statistical single cell multi-omics integration. Current Opinion in Systems Biology. 2018;7:54–59.
35. Sun S, A survey of multi-view machine learning. Neural Computing and Applications. 2013;23(7–8):2031–2038.
36. Li Y, Wu FX, Ngom A, A review on machine learning principles for multi-view biological data integration. Briefings in bioinformatics. 2016;19(2):325–340.
37. Cavill R, Jennen D, Kleinjans J, Briedé JJ, Transcriptomic and metabolomic data integration. Briefings in bioinformatics. 2015;17(5):891–901. doi: 10.1093/bib/bbv090 pmid: 26467821
38. Wang X, Xing EP, Schaid DJ, Kernel methods for large-scale genomic data analysis. Briefings in bioinformatics. 2014;16(2):183–192. doi: 10.1093/bib/bbu024 pmid: 25053743
39. Zampieri M, Sauer U, Metabolomics-driven understanding of genotype-phenotype relations in model organisms. Current Opinion in Systems Biology. 2017;6:28–36.
40. Yugi K, Kuroda S, Metabolism as a signal generator across trans-omic networks at distinct time scales. Current Opinion in Systems Biology. 2017;8:59–66.
41. Sriyudthsak K, Shiraishi F, Hirai MY, Mathematical modeling and dynamic simulation of metabolic reaction systems using metabolome time series data. Frontiers in molecular biosciences. 2016;3:15. doi: 10.3389/fmolb.2016.00015 pmid: 27200361
42. Aretz I, Meierhofer D, Advantages and pitfalls of mass spectrometry based metabolome profiling in systems biology. International journal of molecular sciences. 2016;17(5):632.
43. Niedenführ S, Wiechert W, Nöh K, How to measure metabolic fluxes: a taxonomic guide for 13C fluxomics. Current opinion in biotechnology. 2015;34:82–90. doi: 10.1016/j.copbio.2014.12.003 pmid: 25531408
44. Edwards JS, Palsson BO, Systems properties of the Haemophilus influenzaeRd metabolic genotype. Journal of Biological Chemistry. 1999;274(25):17410–17416. doi: 10.1074/jbc.274.25.17410 pmid: 10364169
45. Edwards J, Palsson B, The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proceedings of the National Academy of Sciences. 2000;97(10):5528–5533.
46. Orth JD, Thiele I, Palsson BØ, What is flux balance analysis?Nature biotechnology. 2010;28(3):245. doi: 10.1038/nbt.1614 pmid: 20212490
47. Lewis NE, Nagarajan H, Palsson BO, Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology. 2012;10(4):291. doi: 10.1038/nrmicro2737 pmid: 22367118
48. O'Brien EJ, Monk JM, Palsson BO, Using genome-scale models to predict biological capabilities. Cell. 2015;161(5):971–987. doi: 10.1016/j.cell.2015.05.019 pmid: 26000478
49. Ebrahim A, Brunk E, Tan J, O'brien EJ, Kim D, Szubin R, et al. Multi-omic data integration enables discovery of hidden biological regularities. Nature communications. 2016;7:13091. doi: 10.1038/ncomms13091 pmid: 27782110
50. Gottstein W, Olivier BG, Bruggeman FJ, Teusink B, Constraint-based stoichiometric modelling from single organisms to microbial communities. Journal of the Royal Society Interface. 2016;13(124):20160627.
51. Lewis NE, Abdel-Haleem AM, The evolution of genome-scale models of cancer metabolism. Frontiers in physiology. 2013;4:237. doi: 10.3389/fphys.2013.00237 pmid: 24027532
52. Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. Nature protocols. 2019;14(3):639–702. doi: 10.1038/s41596-018-0098-2 pmid: 30787451
53. Jensen K, Gudmundsson S, Herrgård MJ, Enhancing Metabolic Models with Genome-Scale Experimental Data. In: Systems Biology. New York: Springer; 2018. p. 337–350.
54. Thorleifsson SG, Thiele I, rBioNet: A COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics. 2011;27(14):2009–2010. doi: 10.1093/bioinformatics/btr308 pmid: 21596791
55. Machado D, Andrejev S, Tramontano M, Patil KR, Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Research. 2018;46:7542–7553. doi: 10.1093/nar/gky537 pmid: 30192979
56. Faria JP, Rocha M, Rocha I, Henry CS, Methods for automated genome-scale metabolic model reconstruction. Biochemical Society Transactions. 2018;46(4):931–936. doi: 10.1042/BST20170246 pmid: 30065105
57. Yang L, Yurkovich JT, Lloyd CJ, Ebrahim A, Saunders MA, Palsson BO, Principles of proteome allocation are revealed using proteomic data and genome-scale models. Scientific reports. 2016;6:36734. doi: 10.1038/srep36734 pmid: 27857205
58. Angione C, Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism. Bioinformatics. 2018;34(3):494–501. doi: 10.1093/bioinformatics/btx562 pmid: 28968777
59. Vivek-Ananth R, Samal A, Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. Biosystems. 2016;147:1–10. doi: 10.1016/j.biosystems.2016.06.001 pmid: 27287878
60. Töpfer N, Kleessen S, Nikoloski Z, Integration of metabolomics data into metabolic networks. Frontiers in plant science. 2015;6:49. doi: 10.3389/fpls.2015.00049 pmid: 25741348
61. Zur H, Ruppin E, Shlomi T, iMAT: an integrative metabolic analysis tool. Bioinformatics. 2010;26(24):3140–3142. doi: 10.1093/bioinformatics/btq602 pmid: 21081510
62. 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(5):e1002518. doi: 10.1371/journal.pcbi.1002518 pmid: 22615553
63. Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T, Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics. 2010;26(12):i255–i260. doi: 10.1093/bioinformatics/btq183 pmid: 20529914
64. Sánchez BJ, Zhang C, Nilsson A, Lahtvee PJ, Kerkhoven EJ, Nielsen J, Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Molecular systems biology. 2017;13(8):935. doi: 10.15252/msb.20167411 pmid: 28779005
65. Hyduke DR, Lewis NE, Palsson BØ, Analysis of omics data with genome-scale models of metabolism. Molecular BioSystems. 2013;9(2):167–174. doi: 10.1039/c2mb25453k pmid: 23247105
66. Fouladiha H, Marashi SA, Biomedical applications of cell-and tissue-specific metabolic network models. Journal of biomedical informatics. 2017;68:35–49. doi: 10.1016/j.jbi.2017.02.014 pmid: 28242343
67. Sridhara V, Meyer AG, Rai P, Barrick JE, Ravikumar P, Segrè D, et al. Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli. PLoS ONE. 2014;9(12):e114608. doi: 10.1371/journal.pone.0114608 pmid: 25502413
68. Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E, Metabolic Network Prediction of Drug Side Effects. Cell Systems. 2016;2(3):209–213. doi: 10.1016/j.cels.2016.03.001 pmid: 27135366
69. Yousoff SNM, Baharin A, Abdullah A. Differential Search Algorithm in Deep Neural Network for the Predictive Analysis of Xylitol Production in Escherichia Coli. In: Asian Simulation Conference. New York: Springer; 2017. p. 53–67.
70. Oyetunde T, Liu D, Martin HG, Tang YJ, Machine learning framework for assessment of microbial factory performance. PLoS ONE. 2019;14(1):e0210558. doi: 10.1371/journal.pone.0210558 pmid: 30645629
71. Folch-Fortuny A, Teusink B, Hoefsloot HC, Smilde AK, Ferrer A, Dynamic elementary mode modelling of non-steady state flux data. BMC systems biology. 2018;12(1):71. doi: 10.1186/s12918-018-0589-3 pmid: 29914483
72. DiMucci D, Kon M, Segre D, Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks. mSystems. 2018;3(5):e00181–18. doi: 10.1128/mSystems.00181-18 pmid: 30417106
73. Chien J, Larsen P, Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling. arXiv [Preprint]. 2017 [cited 2019 Feb 10]. Available from: https://arxiv.org/abs/1701.03220.
74. Segre D, DeLuna A, Church GM, Kishony R, Modular epistasis in yeast metabolism. Nature genetics. 2005;37(1):77. doi: 10.1038/ng1489 pmid: 15592468
75. 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. Nature biotechnology. 2017;35(1):81. doi: 10.1038/nbt.3703 pmid: 27893703
76. Barrett CL, Herrgard MJ, Palsson B, Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. BMC systems biology. 2009;3(1):30.
77. Folch-Fortuny A, Marques R, Isidro IA, Oliveira R, Ferrer A, Principal elementary mode analysis (PEMA). Molecular BioSystems. 2016;12(3):737–746. doi: 10.1039/c5mb00828j pmid: 26905301
78. Bhadra S, Blomberg P, Castillo S, Rousu J, Wren J, Principal metabolic flux mode analysis. Bioinformatics. 2018;1:9.
79. Plaimas K, Mallm JP, Oswald M, Svara F, Sourjik V, Eils R, et al. Machine learning based analyses on metabolic networks supports high-throughput knockout screens. BMC systems biology. 2008;2(1):67.
80. Szappanos B, Kovács K, Szamecz B, Honti F, Costanzo M, Baryshnikova A, et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature genetics. 2011;43(7):656. doi: 10.1038/ng.846 pmid: 21623372
81. Nandi S, Subramanian A, Sarkar RR, An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features. Molecular BioSystems. 2017;13(8):1584–1596. doi: 10.1039/c7mb00234c pmid: 28671706
82. Burgard AP, Nikolaev EV, Schilling CH, Maranas CD, Flux coupling analysis of genome-scale metabolic network reconstructions. Genome research. 2004;14(2):301–312. doi: 10.1101/gr.1926504 pmid: 14718379
83. Li L, Zhou X, Ching WK, Wang P, Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines. BMC bioinformatics. 2010;11(1):501.
84. Yaneske E, Angione C, The poly-omics of ageing through individual-based metabolic modelling. BMC bioinformatics. 2018;19(14):415.
85. Occhipinti A, Eyassu F, Rahman TJ, Rahman PK, Angione C, In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production. PeerJ. 2018;6:e6046. doi: 10.7717/peerj.6046 pmid: 30588397
86. Zampieri G, Coggins M, Valle G, Angione C. A poly-omics machine-learning method to predict metabolite production in CHO cells. In: Metabolomics, The 2nd International Electronic Conference on. Basel, Switzerland: MDPI AG; 2017. p. 4993.
87. Kim M, Rai N, Zorraquino V, Tagkopoulos I, Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nature communications. 2016;7:13090. doi: 10.1038/ncomms13090 pmid: 27713404
88. Samal SS, Radulescu O, Weber A, Fröhlich H, Linking metabolic network features to phenotypes using sparse group lasso. Bioinformatics. 2017;33(21):3445–3453. doi: 10.1093/bioinformatics/btx427 pmid: 29077809
89. Andreozzi S, Miskovic L, Hatzimanikatis V, iSCHRUNK–in silico approach to characterization and reduction of uncertainty in the kinetic models of genome-scale metabolic networks. Metabolic engineering. 2016;33:158–168. doi: 10.1016/j.ymben.2015.10.002 pmid: 26474788
90. Guo W, Xu Y, Feng X, DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing. arXiv [Preprint]. 2017 [cited 2019 Feb 10]. Available from: https://arxiv.org/abs/1705.03094.
91. Angione C, Pratanwanich N, Lió P, A hybrid of metabolic flux analysis and bayesian factor modeling for multiomic temporal pathway activation. ACS synthetic biology. 2015;4(8):880–889. doi: 10.1021/sb5003407 pmid: 25856685
92. Barsacchi M, Andres-Terre H, Lió P, GEESE: Metabolically driven latent space learning for gene expression data. bioRxiv [Preprint]. 2018 [cited 2019 Feb 10]. Available from: https://www.biorxiv.org/content/10.1101/365643v1.
93. Wu SG, Wang Y, Jiang W, Oyetunde T, Yao R, Zhang X, et al. Rapid prediction of bacterial heterotrophic fluxomics using machine learning and constraint programming. PLoS Comput Biol. 2016;12(4):e1004838. doi: 10.1371/journal.pcbi.1004838 pmid: 27092947
94. Brunk E, George KW, Alonso-Gutierrez J, Thompson M, Baidoo E, Wang G, et al. Characterizing strain variation in engineered E. coli using a multi-omics-based workflow. Cell systems. 2016;2(5):335–346. doi: 10.1016/j.cels.2016.04.004 pmid: 27211860
95. Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson OE, Palsson BO, Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Nature Communications. 2017;7:46249.
96. Heckmann D, Lloyd CJ, Mih N, Ha Y, Zielinski DC, Haiman ZB, et al. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nature Communications. 2018;9(1):5252. doi: 10.1038/s41467-018-07652-6 pmid: 30531987
97. Robinson JL, Nielsen J, Integrative analysis of human omics data using biomolecular networks. Molecular BioSystems. 2016;12(10):2953–2964. doi: 10.1039/c6mb00476h pmid: 27510223
98. Timmons JA, Szkop KJ, Gallagher IJ, Multiple sources of bias confound functional enrichment analysis of global-omics data. Genome biology. 2015;16(1):186.
99. Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE, Orth JD, et al. In silico method for modelling metabolism and gene product expression at genome scale. Nature communications. 2012;3:929. doi: 10.1038/ncomms1928 pmid: 22760628
100. Motamedian E, Mohammadi M, Shojaosadati SA, Heydari M, TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data. Bioinformatics. 2016;33(7):1057–1063.
101. Thiele I, Palsson BØ, A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. 2010;5(1):93. doi: 10.1038/nprot.2009.203 pmid: 20057383
102. Semmes OJ, The “omics” haystack: defining sources of sample bias in expression profiling. Clinical Chemistry. 2005;51(9):1571–1572. doi: 10.1373/clinchem.2005.053405 pmid: 16120944
103. Goh WWB, Wang W, Wong L, Why batch effects matter in omics data, and how to avoid them. Trends in biotechnology. 2017;35(6):498–507. doi: 10.1016/j.tibtech.2017.02.012 pmid: 28351613
104. Tummler K, Klipp E, The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions?Current Opinion in Systems Biology. 2017;8:1–6.
105. Edwards JS, Ibarra RU, Palsson BO, In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature biotechnology. 2001;19(2):125. doi: 10.1038/84379 pmid: 11175725
106. Joyce AR, Reed JL, White A, Edwards R, Osterman A, Baba T, et al. Experimental and computational assessment of conditionally essential genes in Escherichia coli. Journal of bacteriology. 2006;188(23):8259–8271. doi: 10.1128/JB.00740-06 pmid: 17012394
107. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular systems biology. 2010;6(1):390.
108. King ZA, O'Brien EJ, Feist AM, Palsson BO, Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion. Metabolic engineering. 2017;39:220–227. doi: 10.1016/j.ymben.2016.12.004 pmid: 27986597
109. Machado D, Herrgård M, Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol. 2014;10(4):e1003580. doi: 10.1371/journal.pcbi.1003580 pmid: 24762745
110. Feist AM, Palsson BØ, The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature biotechnology. 2008;26(6):659. doi: 10.1038/nbt1401 pmid: 18536691
111. Lay JO, JrLiyanage R, Borgmann S, Wilkins CL, Problems with the “omics”. TrAC Trends in Analytical Chemistry. 2006;25(11):1046–1056.
112. Sung J, Wang Y, Chandrasekaran S, Witten DM, Price ND, Molecular signatures from omics data: from chaos to consensus. Biotechnology journal. 2012;7(8):946–957. doi: 10.1002/biot.201100305 pmid: 22528809
113. Long MR, Ong WK, Reed JL, Computational methods in metabolic engineering for strain design. Current opinion in biotechnology. 2015;34:135–141. doi: 10.1016/j.copbio.2014.12.019 pmid: 25576846
114. Campbell K, Xia J, Nielsen J, The Impact of Systems Biology on Bioprocessing. Trends in Biotechnology. 2017;35(12):1156–1168. doi: 10.1016/j.tibtech.2017.08.011 pmid: 28987922
115. Bordbar A, Feist AM, Usaite-Black R, Woodcock J, Palsson BO, Famili I, A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC systems biology. 2011;5(1):180.
116. Damiani C, Maspero D, Di Filippo M, Colombo R, Pescini D, Graudenzi A, et al. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLoS Comput Biol. 2019;15(2):e1006733. doi: 10.1371/journal.pcbi.1006733 pmid: 30818329
117. Belgrave D, Henderson J, Simpson A, Buchan I, Bishop C, Custovic A, Disaggregating asthma: Big investigation versus big data. Journal of Allergy and Clinical Immunology. 2017;139(2):400–407. doi: 10.1016/j.jaci.2016.11.003 pmid: 27871876
118. Lipton ZC, The Mythos of Model Interpretability. Queue. 2018;16(3):30.
119. Bennett KP, Parrado-Hernández E, The interplay of optimization and machine learning research. Journal of Machine Learning Research. 2006;7(Jul):1265–1281.