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Towards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data

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Automated Reasoning for Systems Biology and Medicine

Part of the book series: Computational Biology ((COBO,volume 30))

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Abstract

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated datasets such as TCGA or METABRIC. These datasets will serve as references to compare new patient samples to in order to assign them to a predefined category (i.e. ‘patients associated with poor prognosis’). Some problems affecting the downstream analysis remain to be solved, the bottleneck is no longer data generation itself but the integration of the existing datasets with the present knowledge. Constraint-based modelling, that only requires the setting of a few parameters, became popular for the integration of high-throughput data in a metabolic context. Notably, context-specific building algorithms that extract a subnetwork from a reference network are largely used to study metabolic changes in various diseases. Reference networks are composed of canonical pathways while extracted subnetworks include only active pathways in the context of interest based on high-throughput data. Even though these algorithms can be part of automated pipelines, to be applied by clinicians, the model-building pipelines must be coupled to a standardized semi-automated analysis workflow based on machine learning approaches to avoid bias and reduce the cost of diagnostics.

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References

  1. Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8(5):e1002518

    Article  Google Scholar 

  2. Angione C, Lió P (2015) Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep 5:15147

    Article  Google Scholar 

  3. Asgari Y, Zabihinpour Z, Salehzadeh-Yazdi A, Schreiber F, Masoudi-Nejad A (2015) Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation. Genomics 105(5):275–281

    Article  Google Scholar 

  4. Askari BS, Krajinovic M (2010) Dihydrofolate reductase gene variations in susceptibility to disease and treatment outcomes. Curr Genomics 11(8):578–583

    Google Scholar 

  5. Aurich MK, Fleming RM, Thiele I (2017) A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines. PLoS Comput Biol 13(8):e1005698

    Article  Google Scholar 

  6. Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, Datsenko KA, Tomita M, Wanner BL, Mori H (2006) Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol 2(1)

    Google Scholar 

  7. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D et al (2012) The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603–607

    Article  Google Scholar 

  8. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, Ebright RY, Stewart ML, Ito D, Wang S et al (2013) An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154(5):1151–1161

    Article  Google Scholar 

  9. Becker SA, Palsson BO (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4(5):e1000082

    Article  MathSciNet  Google Scholar 

  10. Boehm JS, Golub TR (2015) An ecosystem of cancer cell line factories to support a cancer dependency map. Nat Rev Genet 16(7):373

    Article  Google Scholar 

  11. Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15(2):107

    Google Scholar 

  12. Clarke C, Doolan P, Barron N, Meleady P, O’Sullivan F, Gammell P, Melville M, Leonard M, Clynes M (2011) Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity. J Biotechnol 155(3):350–359

    Article  Google Scholar 

  13. Conde M, do Rosario P, Sauter T, Pfau T (2016) Constraint based modeling going multicellular. Front Mol Biosci 3:3

    Google Scholar 

  14. Consortium GP et al (2010) A map of human genome variation from population-scale sequencing. Nature 467(7319):1061–1073

    Google Scholar 

  15. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y et al (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403):346–352

    Article  Google Scholar 

  16. Diener C, Resendis-Antonio O (2016) Personalized prediction of proliferation rates and metabolic liabilities in cancer biopsies. Front Physiol 7

    Google Scholar 

  17. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BØ (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci 104(6):1777–1782

    Article  Google Scholar 

  18. Estévez SR, Nikoloski Z (2015) Context-specific metabolic model extraction based on regularized least squares optimization. PloS One 10(7):e0131875

    Article  Google Scholar 

  19. Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7(1):501

    Article  Google Scholar 

  20. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A et al (2010) Cosmic: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res 39(suppl\(\_\)1):D945–D950

    Article  Google Scholar 

  21. Franke RM, Scherkenbach LA, Sparreboom A (2009) Pharmacogenetics of the organic anion transporting polypeptide 1A2

    Article  Google Scholar 

  22. Frejno M, Chiozzi RZ, Wilhelm M, Koch H, Zheng R, Klaeger S, Ruprecht B, Meng C, Kramer K, Jarzab A et al (2017) Pharmacoproteomic characterisation of human colon and rectal cancer. Mol Syst Biol 13(11):951

    Article  Google Scholar 

  23. Gibbs RA, Belmont JW, Hardenbol P, Willis TD, Yu F, Yang H, Ch’ang LY, Huang W, Liu B, Shen Y et al (2003) The international HapMap project. Nature 426(6968):789–796

    Article  Google Scholar 

  24. Goh WWB, Wang W, Wong L (2017) Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol

    Google Scholar 

  25. Guinney J, Dienstmann R, Wang X, De Reyniès A, Schlicker A, Soneson C, Marisa L, Roepman P, Nyamundanda G, Angelino P et al (2015) The consensus molecular subtypes of colorectal cancer. Nat Med 21(11):1350

    Article  Google Scholar 

  26. Halldorsson S, Rohatgi N, Magnusdottir M, Choudhary KS, Gudjonsson T, Knutsen E, Barkovskaya A, Hilmarsdottir B, Perander M, Mælandsmo GM et al (2017) Metabolic re-wiring of isogenic breast epithelial cell lines following epithelial to mesenchymal transition. Cancer Lett 396:117–129

    Article  Google Scholar 

  27. Hart T, Brown KR, Sircoulomb F, Rottapel R, Moffat J (2014) Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol Syst Biol 10(7):733

    Article  Google Scholar 

  28. Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S et al (2015) High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163(6):1515–1526

    Article  Google Scholar 

  29. Hartwell LH, Szankasi P, Roberts CJ, Murray AW, Friend SH (1997) Integrating genetic approaches into the discovery of anticancer drugs. Science 278(5340):1064–1068

    Article  Google Scholar 

  30. Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, Orellana CA, Baycin-Hizal D, Huang Y, Ley D et al (2016) A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism. Cell Syst 3(5):434–443

    Article  Google Scholar 

  31. Heinken A, Sahoo S, Fleming RM, Thiele I (2013) Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4(1):28–40

    Article  Google Scholar 

  32. Hillje AL, Beckmann E, Pavlou MA, Jaeger C, Pacheco MP, Sauter T, Schwamborn JC, Lewejohann L (2015) The neural stem cell fate determinant TRIM32 regulates complex behavioral traits. Front Cell Neurosci 9:75

    Article  Google Scholar 

  33. Ho YC, Pepyne DL (2002) Simple explanation of the no-free-lunch theorem and its implications. J Optim Theory Appl 115(3):549–570

    Article  MathSciNet  MATH  Google Scholar 

  34. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H et al (2016) A landscape of pharmacogenomic interactions in cancer. Cell 166(3):740–754

    Article  Google Scholar 

  35. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer, Berlin

    Google Scholar 

  36. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2009) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38(suppl\(\_\)1):D355–D360

    Article  Google Scholar 

  37. Klijn C, Durinck S, Stawiski EW, Haverty PM, Jiang Z, Liu H, Degenhardt J, Mayba O, Gnad F, Liu J et al (2015) A comprehensive transcriptional portrait of human cancer cell lines. Nat Biotechnol 33(3):306–312

    Article  Google Scholar 

  38. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V et al (2010) DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 39(suppl\(\_\)1):D1035–D1041

    Article  Google Scholar 

  39. Koike-Yusa H, Li Y, Tan EP, Velasco-Herrera MDC, Yusa K (2014) Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat Biotechnol 32(3):267

    Article  Google Scholar 

  40. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3(8):711

    Article  Google Scholar 

  41. Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comput Eng 160:3–24

    Google Scholar 

  42. Kubens BS, Niggemann B, Zänker KS (2001) Prevention of entrance into G2 cell cycle phase by mimosine decreases locomotion of cells from the tumor cell line SW480. Cancer Lett 162:S39–S47

    Article  Google Scholar 

  43. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6(1):343

    Article  Google Scholar 

  44. Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279(15):1200–1205

    Article  Google Scholar 

  45. Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R et al (2010) Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotechnol 28(12):1279

    Article  Google Scholar 

  46. Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16(6):321–332

    Article  Google Scholar 

  47. Llaverias G, Danilo C, Mercier I, Daumer K, Capozza F, Williams TM, Sotgia F, Lisanti MP, Frank PG (2011) Role of cholesterol in the development and progression of breast cancer. Am J Pathol 178(1):402–412

    Article  Google Scholar 

  48. Long MR, Ong WK, Reed JL (2015) Computational methods in metabolic engineering for strain design. Curr Opin Biotechnol 34:135–141

    Article  Google Scholar 

  49. López-Agudelo VA, Baena A, Ramirez-Malule H, Ochoa S, Barrera LF, Ríos-Estepa R (2017) Metabolic adaptation of two in silico mutants of mycobacterium tuberculosis during infection. BMC Syst Biol 11(1):107

    Article  Google Scholar 

  50. Machado D, Herrgård M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10(4):e1003580

    Article  Google Scholar 

  51. Mahadevan R, Schilling C (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5(4):264–276

    Article  Google Scholar 

  52. Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J (2014) Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun 5:3083

    Article  Google Scholar 

  53. McCullagh P (1984) Generalized linear models. Eur J Oper Res 16(3):285–292

    Article  MathSciNet  MATH  Google Scholar 

  54. Mienda BS, Salihu R, Adamu A, Idris S (2018) Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets. Futur Microbiol 13(4):455–467

    Article  Google Scholar 

  55. Mitchell TM (1997) Machine learning. 1997, vol 45(37). McGraw Hill, Burr Ridge, pp 870–877

    Google Scholar 

  56. Nozawa T, Suzuki M, Takahashi K, Yabuuchi H, Maeda T, Tsuji A, Tamai I (2004) Involvement of estrone-3-sulfate transporters in proliferation of hormone-dependent breast cancer cells. J Pharmacol Exp Ther 311(3):1032–1037

    Article  Google Scholar 

  57. Nozawa T, Suzuki M, Yabuuchi H, Irokawa M, Tsuji A, Tamai I (2005) Suppression of cell proliferation by inhibition of estrone-3-sulfate transporter in estrogen-dependent breast cancer cells. Pharm Res 22(10):1634–1641

    Article  Google Scholar 

  58. Ohler U, Liao GC, Niemann H, Rubin GM (2002) Computational analysis of core promoters in the Drosophila genome. Genome Biol 3(12):research0087–1

    Article  Google Scholar 

  59. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28(3):245

    Article  Google Scholar 

  60. Pacheco MP, Sauter T (2018) The FASTCORE family: for the fast reconstruction of compact context-specific metabolic networks models. Metabolic network reconstruction and modeling. Springer, Berlin, pp 101–110

    Google Scholar 

  61. Pacheco MP, John E, Kaoma T, Heinäniemi M, Nicot N, Vallar L, Bueb JL, Sinkkonen L, Sauter T (2015) Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network. BMC Genomics 16(1):809

    Article  Google Scholar 

  62. Pacheco MP, Pfau T, Sauter T (2016) Benchmarking procedures for high-throughput context specific reconstruction algorithms. Front Physiol 6(410). https://doi.org/10.3389/fphys.2015.00410

  63. Pacheco MP, Bintener T, Ternes D, Kulms D, Haan S, Letellier E, Sauter T (subm) Identifying and targeting cancer-specific metabolism with network-based drug target prediction

    Google Scholar 

  64. Plaimas K, Mallm JP, Oswald M, Svara F, Sourjik V, Eils R, König R (2008) Machine learning based analyses on metabolic networks supports high-throughput knockout screens. BMC Syst Biol 2(1):67

    Article  Google Scholar 

  65. Plaimas K, Eils R, König R (2010) Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst Biol 4(1):56

    Article  Google Scholar 

  66. Raman K, Pratapa A, Mohite O, Balachandran S (2018) Computational prediction of synthetic lethals in genome-scale metabolic models using fast-SL. Metabolic network reconstruction and modeling. Springer, Berlin, pp 315–336

    Google Scholar 

  67. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD (2004) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6(1):R2

    Article  Google Scholar 

  68. Schomburg I, Chang A, Hofmann O, Ebeling C, Ehrentreich F, Schomburg D (2002) BRENDA: a resource for enzyme data and metabolic information

    Google Scholar 

  69. Schultz A, Qutub AA (2016) Reconstruction of tissue-specific metabolic networks using CORDA. PLoS Comput Biol 12(3):e1004808

    Article  Google Scholar 

  70. zu Schwabedissen HEM, Tirona RG, Yip CS, Ho RH, Kim RB, (2008) Interplay between the nuclear receptor pregnane X receptor and the uptake transporter organic anion transporter polypeptide 1A2 selectively enhances estrogen effects in breast cancer. Cancer Res 68(22):9338–9347

    Google Scholar 

  71. Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E (2016) Metabolic network prediction of drug side effects. Cell Syst 2(3):209–213

    Article  Google Scholar 

  72. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelsen TS, Heckl D, Ebert BL, Root DE, Doench JG et al (2014) Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343(6166):84–87

    Article  Google Scholar 

  73. Shankavaram UT, Reinhold WC, Nishizuka S, Major S, Morita D, Chary KK, Reimers MA, Scherf U, Kahn A, Dolginow D et al (2007) Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study. Mol Cancer Ther 6(3):820–832

    Article  Google Scholar 

  74. Tarca AL, Carey VJ, Xw Chen, Romero R, Drăghici S (2007) Machine learning and its applications to biology. PLoS Comput Biol 3(6):e116

    Article  Google Scholar 

  75. Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31(5):419

    Article  Google Scholar 

  76. Vlassis N, Pacheco MP, Sauter T (2014) Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 10(1):e1003424. https://doi.org/10.1371/journal.pcbi.1003424

    Article  Google Scholar 

  77. Wang T, Wei JJ, Sabatini DM, Lander ES (2014) Genetic screens in human cells using the CRISPR-Cas9 system. Science 343(6166):80–84

    Article  Google Scholar 

  78. Wang T, Birsoy K, Hughes NW, Krupczak KM, Post Y, Wei JJ, Lander ES, Sabatini DM (2015) Identification and characterization of essential genes in the human genome. Science (New York) 350(6264):1096–1101. https://doi.org/10.1126/science.aac7041 (1011.1669)

    Article  Google Scholar 

  79. Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM, Network CGAR et al (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45(10):1113–1120

    Article  Google Scholar 

  80. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR et al (2012) Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41(D1):D955–D961

    Article  Google Scholar 

  81. Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E (2014) Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. ELIFE 3:e03641

    Google Scholar 

  82. Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E (2014) A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Mol Syst Biol 10(8):744

    Article  Google Scholar 

  83. Zampieri G, Coggins M, Valle G, Angione C (2017) A poly-omics machine-learning method to predict metabolite production in CHO cells

    Google Scholar 

  84. Zomorrodi AR, Islam MM, Maranas CD (2014) d-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS Synth Biol 3(4):247–257

    Article  Google Scholar 

  85. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc: Ser B (Stat Methodol) 67(2):301–320

    Article  MathSciNet  MATH  Google Scholar 

  86. Zur H, Ruppin E, Shlomi T (2010) iMAT: an integrative metabolic analysis tool. Bioinformatics 26(24):3140–3142

    Article  Google Scholar 

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  1. University of Luxembourg, Luxembourg City, Luxembourg

    Maria Pires Pacheco, Tamara Bintener & Thomas Sauter

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  1. Maria Pires Pacheco
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  1. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

    Pietro Liò

  2. School of Computing, Newcastle University, Newcastle, UK

    Paolo Zuliani

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Pacheco, M.P., Bintener, T., Sauter, T. (2019). Towards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data. In: Liò, P., Zuliani, P. (eds) Automated Reasoning for Systems Biology and Medicine. Computational Biology, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-17297-8_15

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