Advertisement

Systems Biology, Information Technology, and Cancer Research

  • Imme PetersenEmail author
  • Regine Kollek
  • Anne Brüninghaus
  • Martin Döring

Abstract

The plethora and heterogeneity of data on biological processes have caused a change in approaches to data handling and processing by using high-performance computing and informatics. Infrastructures based on information and communication technology (ICT) have been developed to facilitate data management, access, and sharing of data on biological structures and processes on which systems biology is based. Although such infrastructures are essential for research and collaboration, they are often not regarded as being part of knowledge production. In contrast to this, we hypothesize that ICT infrastructures are not mere service facilities to support research activities, but enable, and restrict doing systems research at the same time. Based on a case study in systems cancer research, we argue that the understanding and modeling of biological systems is profoundly shaped by ICT and their underlying conceptualizations. In addition, individual scientists and research institutions cede the responsibilities of the activities associated with standardization, integration, and management of data. From the perspective of the sociological Actor-Network-Theory, our analysis also showed that such ICT infrastructures will become new powerful actors for knowledge production and within the knowledge-producing community of systems biology. Individual scientists and research institutions often neglect the challenges related to standardization, integration, and management of data that complicates and sometimes impedes innovation and translation of new developments into practice. This implies that standardization and integration in systems biology are as important as data generation.

Keywords

Scientific practice ICT infrastructure Data management Integration Standardization Case study 

References

  1. Abu-Asab MS, Abu-Asab N, Loffredo CA, Clarke R, Amri H (2013) Identifying early events of gene expression in breast cancer with systems biology phylogenetics. Cytogenet Genome Res 139(3):206–214PubMedCentralPubMedCrossRefGoogle Scholar
  2. ACGT (2005) Annex 1—Description of Work, Proposal, unpublished workGoogle Scholar
  3. ACGT (2009) Demonstrator specifications, deliverable 13.4, unpublished workGoogle Scholar
  4. Akrich M (1992) The description of technical objects. In: Bijker W, Law J (eds) Shaping technology/building society: studies in sociotechnical change. MIT Press, Cambridge, pp 205–224Google Scholar
  5. Ankeny RA, Leonelli S (2011) What is so special about model organisms? Stud Hist Philos Sci A 42(2):313–323CrossRefGoogle Scholar
  6. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1(1):2PubMedCentralPubMedCrossRefGoogle Scholar
  7. Belliger A, Krieger D (2006) ANThology. Ein einführendes Handbuch zur Akteur–Netzwerk-Theorie. transcript, BielefeldGoogle Scholar
  8. Bernsen HJ, van der Kogel AJ (1999) Antiangiogenic therapy in brain tumor models. J Neurooncol 45(3):247–255PubMedCrossRefGoogle Scholar
  9. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C et al (2001) Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat Genet 29(4):365–371PubMedCrossRefGoogle Scholar
  10. Brochhausen M, Blobel B (2011) Architectural approach for providing relations in biomedical terminologies and ontologies. Stud Health Technol Inform 169:739–743PubMedGoogle Scholar
  11. Brochhausen M, Spear AD, Cocos C, Weiler G, Martín L, Anguita A et al (2011) The ACGT Master Ontology and its applications—towards an ontology-driven cancer research and management system. J Biomed Inform 44(1):8–25Google Scholar
  12. Bucur A, Rüping S, Sengstag T, Sfakianakis S, Tsiknakis M, Wegener D (2011) The ACGT project in retrospect: lessons learned and future outlook. Proc Comput Sc 4:1119–1128CrossRefGoogle Scholar
  13. Burgoon LD (2007) Clearing the standards landscape: the semantics of terminology and their impact on toxicogenomics. Toxicol Sci 99(2):403–412PubMedCrossRefGoogle Scholar
  14. Burkhardt H, Smith B (1991) Handbook of metaphysics and ontology. Philosophia, MunichGoogle Scholar
  15. Callon M (1986) Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay. In: Law J (ed) Power, action and belief: a new sociology of knowledge. Routledge & Kegan Paul, London, pp 196–233Google Scholar
  16. Callon M (1992) The dynamics of techno-economic networks. In: Coombs R, Saviotti P, Walsh V (eds) Technological change and company strategies. Economic and sociological perspectives. Academic, London, pp 72–102Google Scholar
  17. Coebergh JW, van Veen EB, Vandenbroucke JP, van Diest P, Oosterhuis W (2006) One-time general consent for research on biological samples: opt out system for patients is optimal and endorsed in many countries. BMJ 332(7542):665PubMedCentralPubMedCrossRefGoogle Scholar
  18. Deisboeck TS, Zhang L, Yoon J, Costa J (2009) In silico cancer modeling: is it ready for prime time? Nat Clin Pract Oncol 6(1):34–42PubMedCentralPubMedCrossRefGoogle Scholar
  19. Desmedt C, Di Leo A, de Azambuja E, Larsimont D, Haibe-Kains B, Selleslags J et al (2011) Multifactorial approach to predicting resistance to anthracyclines. J Clin Oncol 29(12):1578–1586PubMedCrossRefGoogle Scholar
  20. Dionysiou DD, Stamatakos GS, Uzunoglu NK, Nikita KS, Marioli A (2004) A four-dimensional simulation model of tumour response to radiotherapy in vivo: parametric validation considering radiosensitivity, genetic profile and fractionation. J Theor Biol 230(1):1–20PubMedCrossRefGoogle Scholar
  21. Edwards PN, Mayernik MS, Batcheller AL, Bowker GC, Borgman CL (2011) Science friction: data, metadata, and collaboration. Soc Stud Sci 41(5):667–690PubMedCrossRefGoogle Scholar
  22. Field D, Sansone SA, Collis A, Booth T, Dukes P, Gregurick SK et al (2009) Megascience. ‘Omics’ data sharing. Science 326(5950):234–236PubMedCentralPubMedCrossRefGoogle Scholar
  23. Forgó N, Kollek R, Arning M, Kruegel T, Petersen I (2010) Ethical and legal requirements for transnational genetic research. C.H. Beck, MunichCrossRefGoogle Scholar
  24. García-Sancho M (2012) From the genetic to the computer program the historicity of ‘data’ and ‘computation’ in the investigations on the nematode worm C. elegans (1963–1998). Stud Hist Philos Biol Biomed Sci 43(1):16–28PubMedCrossRefGoogle Scholar
  25. Graf N, Hoppe A (2006) What are the expectations of a clinician from in silico oncology? 2nd international advanced research workshop on in silico oncology (IARWISO). Kolympari, Chania, GreeceGoogle Scholar
  26. Graf N, Hoppe A, Georgiadi E, Belleman R, Desmedt C, Dionysiou D et al (2009) ‘In silico’ oncology for clinical decision making in the context of nephroblastoma. Klin Padiatr 221(3):141–149PubMedCrossRefGoogle Scholar
  27. Gramelsberger G (2013) Simulation and systems understanding. In: Andersen H, Dieks D, Gonzalez WJ, Uebel T, Wheeler G (eds) New challenges to philosophy of science. Springer, Dordrecht, pp 151–161CrossRefGoogle Scholar
  28. Green S, Wolkenhauer O (2012) Integration in action. EMBO Rep 13(9):769–771PubMedCentralPubMedCrossRefGoogle Scholar
  29. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674PubMedCrossRefGoogle Scholar
  30. Hanseth O, Monteiro E, Hatling M (1996) Developing information infrastructure: the tension between standardization and flexibility. Sci Technol Hum Values 21(4):407–426CrossRefGoogle Scholar
  31. Kitchin R (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society 1. doi: 10.1177/2053951714528481
  32. Kohl P, Noble D (2009) Systems biology and the virtual physiological human. Mol Syst Biol 5:292PubMedCentralPubMedCrossRefGoogle Scholar
  33. Kollek R (2009) Informed consent. Comment on article 6. In: Ten Have HAMJ, Jean MS (eds) Universal declaration on bioethics and human rights. Background, principles and application. UNESCO Publishing, Paris, pp 123–138Google Scholar
  34. Kuiper RA, Schellens JH, Blijham GH, Beijnen JH, Voest EE (1998) Clinical research on antiangiogenic therapy. Pharmacol Res 37(1):1–16PubMedCrossRefGoogle Scholar
  35. Kyriazis D, Menychtas A, Dionysiou D, Stamatakos G, Varvarigou T (eds) (2008) Clinical trial simulation in grid environments. Conf. Proc. 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE). Athens, 8–10 October 2008. 6 pGoogle Scholar
  36. Latour B (1987) Science in action: how to follow scientists and engineers through society. Open University Press, Milton KeynesGoogle Scholar
  37. Latour B (1992) Where are the missing masses? The sociology of a few mundane artifacts. In: Bijker W, Law J (eds) Shaping technology/building society: studies in sociotechnical change. MIT Press, Cambridge, pp 225–259Google Scholar
  38. Latour B (1996) On actor-network theory. A few clarifications. Soziale Welt 47(4):369–382Google Scholar
  39. Latour B (2005) Reassembling the social: an introduction to actor-network-theory. Oxford University Press, OxfordGoogle Scholar
  40. Law J (1987) Technology and heterogeneous engineering: the case of portuguese expansion. In: Bijker W, Law J (eds) The social construction of technological systems: new directions in the sociology and history of technology. MIT Press, Cambridge, pp 111–134Google Scholar
  41. Law J, Hassard J (eds) (1999) Actor network theory and after. Blackwell and the Sociological Review, OxfordGoogle Scholar
  42. Lee JK, Coutant C, Kim YC, Qi Y, Theodorescu D, Symmans WF et al (2010) Prospective comparison of clinical and genomic multivariate predictors of response to neoadjuvant chemotherapy in breast cancer. Clin Cancer Res 16(2):711–718PubMedCentralPubMedCrossRefGoogle Scholar
  43. Leonelli S (2010) Packaging data for re-use: databases in model organism biology. In: Howlett P, Morgan MS (eds) How well do facts travel? The dissemination of reliable knowledge. Cambridge University Press, Cambridge, pp 325–348CrossRefGoogle Scholar
  44. Leonelli S (2012) Introduction: making sense of data-driven research in the biological and biomedical sciences. Stud Hist Philos Biol Biomed Sci 43(1):1–3PubMedCrossRefGoogle Scholar
  45. Leonelli S (2014) What difference does quantity make? On the epistemology of big data in biology. Big Data & Society 1. doi: 10.1177/2053951714534395
  46. Leonelli S, Ankeny RA (2012) Re-thinking organisms: the impact of databases on model organism biology. Stud Hist Philos Biol Biomed Sci 43(1):29–36PubMedCrossRefGoogle Scholar
  47. Leonelli S, Diehl AD, Christie KR, Harris MA, Lomax J (2011) How the gene ontology evolves. BMC Bioinformatics 12:325PubMedCentralPubMedCrossRefGoogle Scholar
  48. Lin B, White JT, Lu W, Xie T, Utleg AG, Yan X et al (2005) Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease. Cancer Res 65(8):3081–3091PubMedGoogle Scholar
  49. Mayer-Schonberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work and think. John Murray, LondonGoogle Scholar
  50. Meier S, Gehring C (2008) A guide to the integrated application of online data mining tools for the inference of gene functions at the systems level. Biotechnol J 3(11):1375–1387Google Scholar
  51. Meuser M, Nagel U (1991) ExpertInneninterviews—vielfach erprobt, wenig bedacht. Ein Beitrag zur qualitativen Methodendiskussion. In: Garz D, Kraimer K (eds) Qualitativ-empirische Sozialforschung. Konzepte, Methoden, Analysen. Westdeutscher Verlag, Opladen, pp 441–471CrossRefGoogle Scholar
  52. Michelson S, Sehgal A, Friedrich C (2006) In silico prediction of clinical efficacy. Curr Opin Biotechnol 17(6):666–670PubMedCrossRefGoogle Scholar
  53. Nyrönen TH, Laitinen J, Tourunen O, Sternkopf D, Laurikainen R, Öster P et al (2012) Delivering ICT infrastructure for biomedical research. Proc. WICSA/ECSA Companion Volume. pp 37–44Google Scholar
  54. O’Malley MA, Soyer OS (2012) The roles of integration in molecular systems biology. Stud Hist Philos Biol Biomed Sci 43(1):58–68PubMedCrossRefGoogle Scholar
  55. Rakyan VK, Down TA, Balding DJ, Beck S (2011) Epigenome-wide association studies for common human diseases. Nat Rev Genet 12(8):529–541PubMedCentralPubMedCrossRefGoogle Scholar
  56. Rubin DL, Shah NH, Noy NF (2008) Biomedical ontologies: a functional perspective. Brief Bioinform 9(1):75–90PubMedCrossRefGoogle Scholar
  57. Sanga S, Frieboes HB, Zheng X, Gatenby R, Bearer EL, Cristini V (2007) Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth. Neuroimage 37(Suppl 1):S120–S134PubMedCentralPubMedCrossRefGoogle Scholar
  58. Stamatakos G (2011) In silico oncology part I: clinically oriented cancer multilevel modeling based on discrete event simulation. In: Deisboeck T, Stamatakos G (eds) Multiscale cancer modelling. CRC, Boca Raton, pp 407–436Google Scholar
  59. Stamatakos GS, Antipas VP, Uzunoglu NK (2006a) A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo. The paradigm of glioblastoma multiforme treated by temozolomide. IEEE Trans Biomed Eng 53(8):1467–1477PubMedCrossRefGoogle Scholar
  60. Stamatakos GS, Antipas VP, Uzunoglu NK, Dale RG (2006b) A four dimensional computer simulation model of the in vivo response to radiotherapy of glioblastoma multiforme. Studies on the effect of clonogenic cell density. Br J Radiol 79:389–400PubMedCrossRefGoogle Scholar
  61. Stamatakos GS, Dionysiou DD, Graf NM, Sofra NA, Desmedt C, Hoppe A et al (2007) The “Oncosimulator”. A multilevel, clinically oriented simulation system of tumor growth and organism response to therapeutic schemes. Towards the clinical evaluation of in silico oncology. Conf. Proc. IEEE Eng Med Biol Soc: pp 6629–6632Google Scholar
  62. Stamatakos GS, Dionysiou DD, Zacharaki EI, Mouravliansky NA, Nikita KS, Uzunoglu NK (2002) In silico radiation oncology: combining novel simulation algorithms with current visualization techniques. Conf. Proc. IEEE Special Issue Bioinformatics Adv Challenges 90(11):1764–1777Google Scholar
  63. Stamatakos GS, Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Franz A et al (2013) In silico oncology: Exploiting clinical studies to clinically adapt and validate multiscale oncosimulators. Conf Proc IEEE Eng Med Biol Soc: 5545–5549Google Scholar
  64. Sujanski W (2001) Heterogeneous database integration in biomedicine. J Biomed Inform 34(4):285–298CrossRefGoogle Scholar
  65. Swertz MA, Jansen RC (2007) Beyond standardization: dynamic software infrastructures for systems biology. Nat Rev Genet 8(3):235–243PubMedCrossRefGoogle Scholar
  66. Symmans WF, Hatzis C, Sotiriou C, Andre F, Peintinger F, Regitnig P et al (2010) Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 28(27):4111–4119PubMedCentralPubMedCrossRefGoogle Scholar
  67. Thomas G (2011) A typology for the case study in social science following a review of definition, discourse and structure. Qual Inq 17(6):511–521CrossRefGoogle Scholar
  68. Tomita M (2001) Whole-cell simulation. A grand challenge of the 21st century. Trends Biotechnol 19(6):205–210Google Scholar
  69. Tsiknakis M, Kafetzopoulos D, Potamias G, Analyti A, Marias K, Manganas A (2006) Building a European biomedical grid on cancer: the ACGT integrated project. Stud Health Technol Inform 120:247–258PubMedGoogle Scholar
  70. Van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536CrossRefGoogle Scholar
  71. Werner-Wasik M, Scott CB, Nelson DF, Gaspar LE, Murray KJ, Fischbach JA et al (1996) Final report of a phase I/II trial of hyperfractionated and accelerated hyperfractionated radiation therapy with carmustine for adults with supratentorial malignant gliomas. Radiation Therapy Oncology Group Study 83-02. Cancer 77(8):1535–1543PubMedCrossRefGoogle Scholar
  72. Wierling C, Herwig R, Lehrach H (2007) Resources, standards and tools for systems biology. Brief Funct Genomic Proteomic 6(3):240–251PubMedCrossRefGoogle Scholar
  73. Wolkenhauer O, Green S (2013) The search for organizing principles as a cure against reductionism in systems medicine. FEBS J. doi: 10.1111/febs.12311 PubMedGoogle Scholar
  74. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O (2013) The road from systems biology to systems medicine. Pediatr Res 73(4 Pt 2):502–507PubMedCrossRefGoogle Scholar
  75. Yin RK (2009) Case study research: design and methods, 4th edn. SAGE, Thousand OaksGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Imme Petersen
    • 1
    Email author
  • Regine Kollek
    • 1
  • Anne Brüninghaus
    • 1
  • Martin Döring
    • 1
  1. 1.Research Centre for Biotechnology, Society and the Environment (FSP BIOGUM)University of HamburgHamburgGermany

Personalised recommendations