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A Conceptual Modeling Approach To Improve Human Genome Understanding

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Handbook of Conceptual Modeling

Abstract

Information systems cannot be designed nor programmed without prior elicitation of the knowledge they need to know. Representing this knowledge in an explicit form is the main application of a conceptualmodel. By allowing for a minor paradigm shift, one can imagine the human body as an information system; highly complex and built of biological molecules, rather than man-made hardware, but an information system nonetheless. It is this paradigm shift that allows for exciting possibilities. Just as acquiring the source-code of a man-made system allows for post-production modifications and easy software maintenance, the same could very well apply to the human body: essentially, the act of debugging life itself. Acquiring the source-code to the human information system begins with the first step in any information system development: the creation of a comprehensive, correct conceptual model of the human genome.

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References

  1. Olivé A (2007) Conceptual modeling of information systems. 1st edn. Springer, Berlin

    MATH  Google Scholar 

  2. Gerstein MB, Bruce C, Rozowsky JS, Zheng D, Du J, Korbel JO, Emanuelsson O, Zhang ZD, Weissman S, Snyder M (2007) What is a gene, post-ENCODE? History and updated definition. Genome Res, 17:669–681

    Article  Google Scholar 

  3. Pearson H (2006) Genetics: What is a gene? Nature 441(7092):398–402

    Article  Google Scholar 

  4. Chikofsky EJ, Cross II, JI (1990) Reverse engineering and design recovery: a taxonomy. IEEE Software 7(1):13–18

    Google Scholar 

  5. Canfora G, Di Penta M (2008) Frontiers of reverse engineering: a conceptual model. Proceedings of frontiers of software maintenance (FoSM 2008), pp 38–47 (2008)

    Google Scholar 

  6. Human Genome Project Information, http://www.ornl.gov/sci/techresources/Human_Genome/

  7. Collins FS, Morgan M, Patrinos A (2003) The human genome project: lessons from large-scale biology. Science 300:286–290

    Article  Google Scholar 

  8. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG., Nadeau J, McKusick VA, Zinder N et al (2001) The sequence of the human genome. Science 291:1304–1351

    Article  Google Scholar 

  9. Edwards AV, White MY, Cordwell SJ (2008) The role of proteomics in clinical cardiovascular biomarker discovery. Mol Cell Proteomics 7:1824–1837

    Article  Google Scholar 

  10. Giovane A, Balestrieri A, Napoli C (2008) New insights into cardiovascular and lipid metabolomics. J Cell Biochem, 105:648–654

    Article  Google Scholar 

  11. Blakemore AI, Froguel P (2008) Is obesity our genetic legacy? J Clin Endocrinol Metabol, 93(11-1):51–56

    Article  Google Scholar 

  12. Chen X, Hess S (2008) Adipose proteome analysis: focus on mediators of insulin resistance. Expet Rev Proteonomics 5:827–839

    Article  Google Scholar 

  13. Pietilainen KH, Sysi-Aho M, Rissanen A, Seppanen-Laakso T, Yki-Jarvinen H, Kaprio J, Oresic M (2007) Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects: a monozygotic twin study. PLoS ONE 2:18–32

    Article  Google Scholar 

  14. Bougneres P, Valleron AJ (2008) Causes of early-onset type 1 diabetes: toward data-driven environmental approaches. J Exp Med 105:2953–2957

    Article  Google Scholar 

  15. Frayling TM (2007) Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet 8:657–662

    Article  Google Scholar 

  16. Orešic M, Simell S, Sysi-Aho M, NLnt-Salonen K, SeppLnen-Laakso T, Parikka V, Katajamaa M, Hekkala A, Mattila I, Keskinen P, Yetukuri L, Reinikainen A, Lehde J, Suortti T, Hakalax J, Simell T, Hyty H, Veijola R, Ilonen J, Lahesmaa R, Knip M, Simell O (2008) Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J Exp Med 205:2975–2984

    Article  Google Scholar 

  17. Li J, Li X, Su H, Chen H, Galbraith DW (2006) A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana. Bioinformatics 22:2037

    Article  Google Scholar 

  18. Human Genome Research Institute, Glossary of genetic terms. http://www.genome.gov/glossary/

  19. Vignal A, Milan D, SanCristobal M, Eggen A (2002) A review on SNP and other types of molecular markers and their use in animal genetics. Genetics 34:275

    Google Scholar 

  20. Zhao Z, Fu YX, Hewett-Emmett D, Boerwinkle E (2003) Investigating single nucleotide polymorphism (SNP) density in the human genome and its implications for molecular evolution. Gene 312:207–213

    Article  Google Scholar 

  21. Nebert DW, Vesell, ES (2004) Advances in pharmacogenomics and individualized drug therapy: exciting challenges that lie ahead. Eur J Pharmacol 500:267–280

    Article  Google Scholar 

  22. International HapMap Consortium (2003) The International HapMap Project. Nature 426:789–96

    Article  Google Scholar 

  23. The HapMap project, http://www.hapmap.org

  24. International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437: 1229–1320

    Article  Google Scholar 

  25. International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–862

    Article  Google Scholar 

  26. The ENCODE Project Consortium (2004) The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 306:636–640

    Article  Google Scholar 

  27. The ENCODE (ENCyclopedia Of DNA Elements) Project, http://genome.ucsc.edu/ENCODE

  28. The Encode Project Consortium (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447:799–816

    Article  Google Scholar 

  29. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences, 106:9362–9369

    Article  Google Scholar 

  30. Dewan A, Liu M, Hartman S, Zhang SS, Liu DT, Zhao C, Tam PO, Chan WM, Lam DS, Snyder M, Barnstable C, Pang CP, Hoh J (2006) HTRA1 promoter polymorphism in wet age-related macular degeneration. Science 314:989–992

    Article  Google Scholar 

  31. Zhu J, Wiener MC, Zhang C, Fridman A, Minch E, Lum PY, Sachs JR, Schadt EE (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3:e69

    Article  MathSciNet  Google Scholar 

  32. Orešic M, Vidal-Puig A, Hanninen V (2006) Metabolomic approaches to phenotype characterization and applications to complex diseases. Expet Rev Mol Diagnos, 6:575e85

    Google Scholar 

  33. van Ommen B (2004) Nutrigenomics: exploiting systems biology in the nutrition and health arenas. Nutrition 20:48

    Google Scholar 

  34. Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh MC, Berden JA, Brindle KM, Kell DB, Rowland JJ, Westerhoff HV, van Dam K, Oliver SG (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol, 19: 45–50

    Article  Google Scholar 

  35. Gieger C, Geistlinger L, Altmaier E, Hrab de Angelis M, Kronenberg F, Meitinger T, Mewes HW, Wichmann HE, Weinberger KM, Adamski J, Illig T, Suhre K (2008) Genetics meets metabolomics: a genomewide association study of metabolite profiles in human serum. PLoS Genet, 4:e1000282

    Article  Google Scholar 

  36. Cascante M, Boros LG, Comin-Anduix B, de Atauri P, Centelles JJ, Lee PW-N (2002) Metabolic control analysis in drug discovery and disease. Nat Biotechnol, 20: 243e9

    Article  Google Scholar 

  37. Collins F, Green E, Guttmacher A, Guyer M (2003) A vision for the future of genomics research. Nature 422:835e47

    Google Scholar 

  38. Fenn J, Mann M, Meng C, Wong S, Whitehouse C (1989) Electrospray ionization for mass spectrometry of large biomolecules. Science 246:64e71

    Article  Google Scholar 

  39. Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, Stott Parker D, Poldrack RA, Evans C, Freimer NB (2009) Phenomics: the systematic study of phenotypes on a genome-wide scale. Neurosci 164:30–42

    Article  Google Scholar 

  40. Freimer N, Sabatti C (2003) The human phenome project. Nat Genet 34:15–21

    Article  Google Scholar 

  41. Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M, Cook L, Abbott R, Larson DE, Koboldt DC, Pohl C, Smith S, Hawkins A, Abbott S, Locke D, Hillier LW, Miner T, Fulton L, Magrini V, Wylie T, Glasscock J, Conyers J, Sander N, Shi X, Osborne JR, Minx P et al (2008) DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456:66–72

    Article  Google Scholar 

  42. The 1000 Genomes Project, http://www.1000genomes.org/

  43. The Human Microbiome, http://www.human-microbiome.org/

  44. The Copy Number Variation Project, http://www.sanger.ac.uk/humgen/cnv/

  45. The Cancer Genome Atlas, http://cancergenome.nih.gov/

  46. Kroemer HK, Meyer zu Schwabedissen HE (2010) A piece in the puzzle of personalized medicine. Clin Pharmacol Therapeut 87:19–20

    Article  Google Scholar 

  47. Mousses S, Kiefer J, von Hoff D, Trent J (2008) Using biointelligence to search the cancer genome: an epistemological perspective on knowledge recovery strategies to enable precision medical genomics. Oncogene 27(2):S58–66

    Article  Google Scholar 

  48. Potti A, Dressman HK, Bild A, Riedel RF, Chan G, Sayer R, Cragun J, Cottrill H, Kelley MJ, Petersen R, Harpole D, Marks J, Berchuck A, Ginsburg GS, Febbo P, Lancaster J, Nevins JR: Genomic signatures to guide the use of chemotherapeutics. Nat Med 12:12941300

    Google Scholar 

  49. Devlin B, Risch N (1995) A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29(2):311–322

    Article  Google Scholar 

  50. Nyholt DR, Chang-En Y, Visscher PM (2009) On Jim Watsons APOE status: genetic information is hard to hide. Eur J Hum Genet 17(2):147–150

    Article  Google Scholar 

  51. Maglott D, Ostell J, Pruitt KD, Tatusova T (2006) Entrez gene: gene-centered information at NCBI. Nucleic Acids Res 35:26–32

    Article  Google Scholar 

  52. Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NST, Cooper DN (2009) The Human gene mutation database: 2008 update. Genome Med 1:13

    Article  Google Scholar 

  53. Mooney SD, Altman RB (2003) MutDB: annotating human variation with functionally relevant data. Bioinformatics 19:1858–1860

    Article  Google Scholar 

  54. Szabo C, Masiello A, Ryan JF, The BIC Consortium, Brody LC (2000) The breast cancer information core: database design, structure, and scope. Hum Mutat 16:123–131

    Article  Google Scholar 

  55. Povey S, Lovering R, Bruford E, Wright M, Lush M, Wain H (2001) The HUGO gene nomenclature committee (HGNC). Hum Genet 109:678–680

    Article  Google Scholar 

  56. Gibbs RA, Belmont JW, Hardenbol P, Willis TD, Yu F et al (2003) The international HapMap project. Nature 426:789–796

    Article  Google Scholar 

  57. Stoesser G, Tuli MA, Lopez R, Sterk P (1999) The EMBL nucleotide sequence database. Nucleic Acids Res 27:18–24

    Article  Google Scholar 

  58. Okayama T, Tamura T, Gojobori T, Tateno Y, Ikeo K, Miyazaki S, Fukami-Kobayashi K, Sugawara H (1998) Formal design and implementation of an improved DDBJ DNA database with a new schema and object-oriented library. Bioinformatics 14(6):472

    Article  Google Scholar 

  59. Chen IMA, Markowitz V (1995) Modeling scientific experiments with an object data model. In: Proceedings of the SSDBM. IEEE Press, New York pp 391–400

    Google Scholar 

  60. Medigue C, Rechenmann F, Danchin A, Viari A (1999) Imagene, an integrated computer environment for sequence annotation and analysis. Bioinformatics 15(1):2

    Article  Google Scholar 

  61. Paton NW, Khan SA, Hayes A, Moussouni F, Brass A, Eilbeck K, Goble CA, Hubbard SJ, Oliver SG (2000) Conceptual modeling of genomic information. Bioinformatics, 16 (6) 548–557

    Article  Google Scholar 

  62. Pastor MA, Burriel V, Pastor O (2009) Conceptual modeling of human genome mutations: a dichotomy between what we have and what we should have. BIOSTEC Bioinformatics 2010, pp 160–166

    Google Scholar 

  63. Pastor O, Levin AM, Celma M, Casamayor JC, Eraso Schattka LE, Villanueva MJ, Perez-Alonso M (2010) Enforcing conceptual modeling to improve the understanding of the human genome. Proceedings of the IVth international conference on research challenges in information science RCIS 2010, Nice, France, IEEE Press, New York

    Google Scholar 

  64. Ashburner M, Ball CA, Blake JA (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–30

    Article  Google Scholar 

  65. Schwarz DF, Hädicke O, Erdmann J, Ziegler A, Bayer D, Möller S (2008) SNPtoGO: characterizing SNPs by enriched GO terms. Bioinformatics 24(1):146

    Article  Google Scholar 

  66. Coulet A, Smal-Tabbone M, Benlian P, Napoli A, Devignes M-D (2006) SNP-converter: an ontology-based solution to reconcile heterogeneous SNP descriptions for pharmacogenomic studies. Lect Notes Bioinform 4075:82–93

    Google Scholar 

  67. Fonseca F, Martin J (2005) Learning the differences between ontologies and conceptual schemas through ontology-driven information systems. J Assoc Inform Syst, 8:129–142

    Google Scholar 

  68. Aßmann U, Zschaler S, Wagner G (2006) Ontologies, meta-models, and the model-driven paradigm. In: Calero C, Ruiz F, Piattini M (eds) Ontologies for software engineering and software technology. Springer, Berlin

    Google Scholar 

  69. Gruber TR (1993) A translation approach to portable ontology specification. Knowl Acquis 5:199–220

    Article  Google Scholar 

  70. Guarino N (2006) Formal ontology in information systems (1998). In: Bennett B, Fellbaum C (eds) Proceedings of the fourth international conference (FOIS 2006), vol 150, IOS Press, Amsterdam

    Google Scholar 

  71. Booch, G, Rumbaugh, J, Jacobson, I (1999) The unified modelling language user guide. Addison-Wesley Professional. Pearson Education, Upper Saddle River

    Google Scholar 

  72. Spilianakis CG, Lalioti MD, Town T, Lee GR, Flavell RA (2005) Interchromosomal associations between alternatively expressed loci. Nature 435:637–645

    Article  Google Scholar 

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  1. Oscar Pastor, Ana M. Levin, Juan Carlos Casamayor, Matilde Celma & Matthijs Kroon

    Present address: Centro de Investigacion en Metodos de Produccion de Software, 46022, Valencia, Spain

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    1. Oscar Pastor
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    2. Ana M. Levin
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    3. Juan Carlos Casamayor
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    4. Matilde Celma
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    5. Matthijs Kroon
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    Correspondence to Oscar Pastor , Ana M. Levin , Juan Carlos Casamayor , Matilde Celma or Matthijs Kroon .

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    Editors and Affiliations

    1. , Department of Computer Science, Brigham Young University, Provo, 84602, USA

      David W. Embley

    2. Institut für Informatik, Christian-Albrechts-Universität zu Kiel, Olshausenstr. 40, Kiel, 24098, Germany

      Bernhard Thalheim

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    Pastor, O., Levin, A., Casamayor, J., Celma, M., Kroon, M. (2011). A Conceptual Modeling Approach To Improve Human Genome Understanding. In: Embley, D., Thalheim, B. (eds) Handbook of Conceptual Modeling. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15865-0_16

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