Skip to main content
SpringerLink
Log in

Envisioning the Application of Systems Biology in Cancer Immunology

  • Chapter
  • First Online:
Cancer Immunology

Abstract

Biomedical research is nowadays concerned with the investigation of complex biological networks, in which dozens to thousands of proteins, genes, and miRNAs interact to control cellular- or tissue-level phenotypes. Investigation of these complex biological networks requires the use of various experimental methodologies that generate massive amounts of quantitative data. In this scenario, systems biology emerged a decade ago as a methodological approach that combines quantitative experimental data, mathematical modeling, and other tools from computational biology, aiming to understand the regulation of these complex biochemical systems.

The interaction between tumors and the immune system is not an exception to this scenario. The immune system is by definition a multi-scale system not only because it involves biochemical networks that regulate the fate of immune cells but also because immune cells communicate with each other by direct contact or through secretion of local or global signals. Furthermore, tumor and immune cells communicate, and this interaction is affected by the features of the microenvironment in which the tumor is hosted. Altogether, we are envisioning a complex multi-scale biological system, whose analysis requires a systemic view to succeed integrating massive amounts of quantitative experimental data coming from different temporal and spatial scales.

In this book chapter, we introduce the elements of the systems biology approach. Furthermore, we discuss some published results that suggest how systems biology can be used in the context of oncology and tumor immunology, with a focus on the development and assessment of anticancer therapies. To facilitate the reading, this chapter contains a glossary of systems biology terms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vera J, Wolkenhauer O. A system biology approach to understand functional activity of cell communication systems. Methods Cell Biol. 2008;90:399–415.

    Article  CAS  PubMed  Google Scholar 

  2. Brooks JD. Translational genomics: the challenge of developing cancer biomarkers. Genome Res. 2012;22(2):183–7.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature. 2000;406(6795):536–40.

    Article  CAS  PubMed  Google Scholar 

  4. Van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6.

    Article  Google Scholar 

  5. Quackenbush J. Computational approaches to analysis of DNA microarray data. Yearb Med Inform. 2006;1:91–103.

    Google Scholar 

  6. Vera J, Wolkenhauer O. Mathematical tools in cancer signalling systems biology. In: Cesario A, Marcus F, editors. Cancer systems biology, bioinformatics and medicine. Dordrecht: Springer; 2011. p. 185–212.

    Chapter  Google Scholar 

  7. Reynolds AR, Tischer C, Verveer PJ, Rocks O, Bastiaens PI. EGFR activation coupled to inhibition of tyrosine phosphatases causes lateral signal propagation. Nat Cell Biol. 2003;5(5):447–53.

    Article  CAS  PubMed  Google Scholar 

  8. Vera J, Schmitz U, Lai X, Engelmann D, Khan FM, Wolkenhauer O, Pützer BM. Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network. Cancer Res. 2013;73(12):3511–24.

    Article  CAS  PubMed  Google Scholar 

  9. Alexopoulos LG, Saez-Rodriguez J, Cosgrove BD, Lauffenburger DA, Sorger PK. Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. Mol Cell Proteomics. 2010;9(9):1849–65.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Rehm M, Huber HJ, Dussmann H, Prehn JH. Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein. EMBO J. 2006;25(18):4338–49.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Vera J, Bachmann J, Pfeifer AC, Becker V, Hormiga JA, Darias NV, Timmer J, Klingmüller U, Wolkenhauer O. A systems biology approach to analyse amplification in the JAK2-STAT5 signalling pathway. BMC Syst Biol. 2008;2:38.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Schoeberl B, Pace EA, Fitzgerald JB, Harms BD, Xu L, Nie L, Linggi B, Kalra A, Paragas V, Bukhalid R, Grantcharova V, Kohli N, West KA, Leszczyniecka M, Feldhaus MJ, Kudla AJ, Nielsen UB. Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal. 2009;2(77):ra31.

    Article  PubMed  Google Scholar 

  13. Chmielecki J, Foo J, Oxnard GR, Hutchinson K, Ohashi K, Somwar R, et al. Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling. Sci Transl Med. 2011;3(90):90ra59.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Witz IP. Tumor-microenvironment interactions: dangerous liaisons. Adv Cancer Res. 2008;100:203–29.

    Article  CAS  PubMed  Google Scholar 

  15. Gilbert LA, Hemann MT. DNA damage-mediated induction of a chemoresistant niche. Cell. 2010;143(3):355–66.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Perfahl H, Byrne HM, Chen T, Estrella V, Alarcón T, Lapin A, et al. Multiscale modelling of vascular tumour growth in 3D: the roles of domain size and boundary conditions. PLoS One. 2011;6(4):e14790.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Byrne HM. Dissecting cancer through mathematics: from the cell to the animal model. Nat Rev Cancer. 2010;10(3):221–30.

    Article  CAS  PubMed  Google Scholar 

  18. Segata N, Blanzieri E, Priami C. Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data. J Integr Bioinform. 2008;5(1):87.

    Google Scholar 

  19. Nikolov S, Vera J, Schmitz U, Wolkenhauer O. A model-based strategy to investigate the role of microRNA regulation in cancer signalling networks. Theory Biosci. 2011;130(1):55–69.

    Article  CAS  PubMed  Google Scholar 

  20. Lai X, Schmitz U, Gupta SK, Bhattacharya A, Kunz M, Wolkenhauer O, Vera J. Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs. Nucleic Acids Res. 2012;40(18):8818–34.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Marin-Sanguino A, Gupta SK, Voit EO, Vera J. Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. Methods Enzymol. 2011;487:319–69.

    Article  CAS  PubMed  Google Scholar 

  22. Wong E, Baur B, Quader S, Huang C-H. Biological network motif detection: principles and practice. Brief Bioinform. 2011;13(2):202–15.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Khan FM, Schmitz U, Nikolov S, Engelmann D, Pützer BM, Wolkenhauer O, et al. Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic. Biochim Biophys Acta. 2014;1844(1 Pt B):289–98.

    Article  CAS  PubMed  Google Scholar 

  24. Vera J, Rath O, Balsa-Canto E, Banga JR, Kolch W, Wolkenhauer O. Investigating dynamics of inhibitory and feedback loops in ERK signalling using power-law models. Mol Biosyst. 2010;6(11):2174–91.

    Article  CAS  PubMed  Google Scholar 

  25. Nahta R, Esteva FJ. Herceptin: mechanisms of action and resistance. Cancer Lett. 2006;232(2):123–38.

    Article  CAS  PubMed  Google Scholar 

  26. Pappalardo F, Chiacchio F, Motta S. Cancer vaccines: state of the art of the computational modeling approaches. Biomed Res Int. 2013;2013:106407.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Yaddanapudi K, Mitchell RA, Eaton JW. Cancer vaccines: looking to the future. Oncoimmunology. 2013;2(3):e23403.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature. 2011;480(7378):480–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Schwartzentruber DJ, Lawson DH, Richards JM, Conry RM, Miller DM, Treisman J, et al. gp100 peptide vaccine and interleukin-2 in patients with advanced melanoma. N Engl J Med. 2011;364(22).

    Google Scholar 

  30. Kenter GG, Welters MJP, Valentijn ARPM, Lowik MJG, van der Meer Berends DMA, Vloon APG, et al. Vaccination against HPV-16 oncoproteins for vulvar intraepithelial neoplasia. N Engl J Med. 2009;361(19):1838–47.

    Article  CAS  PubMed  Google Scholar 

  31. Stevenson FK, Ottensmeier CH, Johnson P, Zhu D, Buchan SL, McCann KJ, et al. DNA vaccines to attack cancer. Proc Natl Acad Sci U S A. 2004;101:14646–52.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Campbell CT, Gulley JL, Oyelaran O, Hodge JW, Schlom J, Gildersleeve JC. Serum antibodies to blood group A predict survival on PROSTVAC-VF. Clin Cancer Res. 2013;19(5):1290–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Vanneman M, Dranoff G. Combining immunotherapy and targeted therapies in cancer treatment. Nat Rev Cancer. 2012;12(4):237–51.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Cheson BD. Ofatumumab, a novel anti-CD20 monoclonal antibody for the treatment of B-cell malignancies. J Clin Oncol. 2010;28(21):3525–30.

    Article  CAS  PubMed  Google Scholar 

  35. Korman AJ, Peggs KS, Allison JP. Checkpoint blockade in cancer immunotherapy. Adv Immunol. 2006;90:297–339.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Guo K, Li J, Tang JP, Tan CPB, Hong CW, Al-Aidaroos AQO, et al. Targeting intracellular oncoproteins with antibody therapy or vaccination. Sci Transl Med. 2011;3(99):99ra85.

    Article  PubMed  Google Scholar 

  37. Hong CW, Zeng Q. Awaiting a new era of cancer immunotherapy. Cancer Res. 2012;72(15):3715–9.

    Article  CAS  PubMed  Google Scholar 

  38. Caballero OL, Chen Y-T. Cancer/testis (CT) antigens: potential targets for immunotherapy. Cancer Sci. 2009;100(11):2014–21.

    Article  CAS  PubMed  Google Scholar 

  39. Castle JC, Kreiter S, Diekmann J, Löwer M, van de Roemer N, de Graaf J, et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 2012;72(5):1081–91.

    Article  CAS  PubMed  Google Scholar 

  40. Charoentong P, Angelova M, Efremova M, Gallasch R, Hackl H, Galon J, et al. Bioinformatics for cancer immunology and immunotherapy. Cancer Immunol Immunother. 2012;61(11):1885–903.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Boon T, Coulie PG, Van den Eynde BJ, van der Bruggen P. Human T cell responses against melanoma. Annu Rev Immunol. 2006;24:175–208.

    Article  CAS  PubMed  Google Scholar 

  42. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia. 2004;6(1):1–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Muñoz N, Castellsagué X, de González AB, Gissmann L. Chapter 1: HPV in the etiology of human cancer. Vaccine. 2006;24 Suppl 3:S3/1–10.

    Google Scholar 

  44. De Villiers E-M, Fauquet C, Broker TR, Bernard H-U, zur Hausen H. Classification of papillomaviruses. Virology. 2004;324(1):17–27.

    Article  PubMed  Google Scholar 

  45. Dunne EF, Unger ER, Sternberg M, McQuillan G, Swan DC, Patel SS, et al. Prevalence of HPV infection among females in the United States. JAMA. 2007;297(8):813–9.

    Article  CAS  PubMed  Google Scholar 

  46. Wain G. The human papillomavirus (HPV) vaccine, HPV related diseases and cervical cancer in the post-reproductive years. Maturitas. 2010;65(3):205–9.

    Article  CAS  PubMed  Google Scholar 

  47. Gupta SK, Singh A, Srivastava M, Gupta SK, Akhoon BA. In silico DNA vaccine designing against human papillomavirus (HPV) causing cervical cancer. Vaccine. 2009;28(1):120–31.

    Article  PubMed  Google Scholar 

  48. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanović S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50(3–4):213–9.

    Article  CAS  PubMed  Google Scholar 

  49. Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003;12(5):1007–17.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics. 2005;6(1):132.

    Article  PubMed Central  PubMed  Google Scholar 

  51. Bui H-H, Sidney J, Peters B, Sathiamurthy M, Sinichi A, Purton K-A, et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics. 2005;57(5):304–14.

    Article  CAS  PubMed  Google Scholar 

  52. Nakamura Y, Gojobori T, Ikemura T. Codon usage tabulated from international DNA sequence databases: status for the year 2000. Nucleic Acids Res. 2000;28(1):292.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Klinman DM, Yamshchikov G, Ishigatsubo Y. Contribution of CpG motifs to the immunogenicity of DNA vaccines. J Immunol. 1997;158(8):3635–9.

    CAS  PubMed  Google Scholar 

  54. Harish N, Gupta R, Agarwal P, Scaria V, Pillai B. DyNAVacS: an integrative tool for optimized DNA vaccine design. Nucleic Acids Res. 2006;34(Web Server issue):W264–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Dolenc I, Seemüller E, Baumeister W. Decelerated degradation of short peptides by the 20S proteasome. FEBS Lett. 1998;434(3):357–61.

    Article  CAS  PubMed  Google Scholar 

  56. Chiang HL, Terlecky SR, Plant CP, Dice JF. A role for a 70-kilodalton heat shock protein in lysosomal degradation of intracellular proteins. Science. 1989;246(4928):382–5.

    Article  CAS  PubMed  Google Scholar 

  57. Donna LM, Kristala JP. Design of plasmid DNA constructs for vaccines. DNA vaccines. Methods Mol Med™. 2006;127:11–22.

    Google Scholar 

  58. Ashall L, Horton CA, Nelson DE, Paszek P, Harper CV, Sillitoe K, et al. Pulsatile stimulation determines timing and specificity of NF-kappaB-dependent transcription. Science. 2009;324(5924):242–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  59. Aguda BD, Kim Y, Piper-Hunter MG, Friedman A, Marsh CB. MicroRNA regulation of a cancer network: consequences of the feedback loops involving miR-17-92, E2F, and Myc. Proc Natl Acad Sci U S A. 2008;105(50):19678–83.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  60. Das J, Ho M, Zikherman J, Govern C, Yang M, Weiss A, et al. Digital signaling and hysteresis characterize ras activation in lymphoid cells. Cell. 2009;136(2):337–51.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  61. Guebel DV, Schmitz U, Wolkenhauer O, Vera J. Analysis of cell adhesion during early stages of colon cancer based on an extended multi-valued logic approach. Mol Biosyst. 2012;8(4):1230–42.

    Article  CAS  PubMed  Google Scholar 

  62. Saez-Rodriguez J, Simeoni L, Lindquist JA, Hemenway R, Bommhardt U, Arndt B, et al. A logical model provides insights into t cell receptor signaling. PLoS Comput Biol. 2007;3(8):e163.

    Article  PubMed Central  PubMed  Google Scholar 

  63. Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, et al. Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity. PLoS Comput Biol. 2013;9(4):e1003027.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Pigliucci M. Genotype–phenotype mapping and the end of the “genes as blueprint” metaphor. Phil Trans R Soc B. 2010;365(1540):557–66.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Deisboeck TS, Wang Z, Macklin P, Cristini V. Multiscale cancer modeling. Annu Rev Biomed Eng. 2011;13:127–55.

    Article  CAS  PubMed  Google Scholar 

  66. Ramis-Conde I, Drasdo D, Anderson ARA, Chaplain MAJ. Modeling the influence of the E-Cadherin-β-Catenin pathway in cancer cell invasion: a multiscale approach. Biophys J. 2008;95(1):155–65.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. Pak Y, Zhang Y, Pastan I, Lee B. Antigen shedding may improve efficiencies for delivery of antibody-based anticancer agents in solid tumors. Cancer Res. 2012;72(13):3143–52.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  68. Wolkenhauer O, Auffray C, Baltrusch S, Blüthgen N, Byrne H, Cascante M, et al. Systems biologists seek fuller integration of systems biology approaches in new cancer research programs. Cancer Res. 2010;70(1):12–3.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  69. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O. The road from systems biology to systems medicine. Pediatr Res [Internet]. [cited 17 Mar 2013]; 2013 http://www.nature.com/pr/journal/vaop/ncurrent/full/pr20134a.html.

  70. Engel C, Scholz M, Loeffler M. A computational model of human granulopoiesis to simulate the hematotoxic effects of multicycle polychemotherapy. Blood. 2004;104(8):2323–31.

    Article  CAS  PubMed  Google Scholar 

  71. Ribba B, Colin T, Schnell S. A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theor Biol Med Model. 2006;3:7.

    Article  PubMed Central  PubMed  Google Scholar 

  72. Foo J, Chmielecki J, Pao W, Michor F. Effects of pharmacokinetic processes and varied dosing schedules on the dynamics of acquired resistance to erlotinib in EGFR-mutant lung cancer. J Thorac Oncol. 2012;7(10):1583–93.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  73. Ballesta A, Dulong S, Abbara C, Cohen B, Okyar A, Clairambault J, et al. A combined experimental and mathematical approach for molecular-based optimization of irinotecan circadian delivery. PLoS Comput Biol. 2011;7(9):e1002143.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  74. Lévi F. Circadian chronotherapy for human cancers. Lancet Oncol. 2001;2(5):307–15.

    Article  PubMed  Google Scholar 

  75. Vera J, Curto R, Cascante M, Torres NV. Detection of potential enzyme targets by metabolic modelling and optimization: application to a simple enzymopathy. Bioinformatics. 2007;23(17):2281–9.

    Article  CAS  PubMed  Google Scholar 

  76. Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O. Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells. PLoS Comput Biol. 2012;8(12):e1002815.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  77. Kirouac DC, Du JY, Lahdenranta J, Overland R, Yarar D, Paragas V, et al. Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors. Sci Signal. 2013;6(288):ra68.

    Article  PubMed  Google Scholar 

  78. Kim PS, Lee PP. Modeling protective anti-tumor immunity via preventative cancer vaccines using a hybrid agent-based and delay differential equation approach. PLoS Comput Biol. 2012;8(10):e1002742.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  79. Maley CC, Reid BJ, Forrest S. Cancer prevention strategies that address the evolutionary dynamics of neoplastic cells: simulating benign cell boosters and selection for chemosensitivity. Cancer Epidemiol Biomarkers Prev. 2004;13(8):1375–84.

    PubMed  Google Scholar 

  80. Gatenby RA, Silva AS, Gillies RJ, Frieden BR. Adaptive therapy. Cancer Res. 2009;69(11):4894–903.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  81. Silva AS, Kam Y, Khin ZP, Minton SE, Gillies RJ, Gatenby RA. Evolutionary approaches to prolong progression-free survival in breast cancer. Cancer Res. 2012;72(24):6362–70.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  82. Dubitzky W, Wolkenhauer O, Yokota H, Cho KH. Encyclopedia of systems biology. New York: Springer; 2013. ISBN 978-1-4419-9862-0.

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the German Federal Ministry of Education and Research (BMBF) as part of the projects eBio:miRSys [0316175A to JV] and eBio:SysMet [0316171 to SKG].

Author information

Authors and Affiliations

  1. Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Hartmannstr. 14, Erlangen, Germany

    Julio Vera PhD

  2. Department of Dermatology, Faculty of Medicine, Friedrich Alexander Universität, University of Erlangen-Nurnberg, Ulmenweg 18, 91054, Erlangen, Germany

    Julio Vera PhD & Gerold Schuler PhD

  3. Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, 18051, Germany

    Shailendra K. Gupta PhD & Olaf Wolkenhauer PhD

  4. Department of Bioinformatics, CSIR-Indian Institute of Toxicology Research, Lucknow, India

    Shailendra K. Gupta PhD

Authors
  1. Julio Vera PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shailendra K. Gupta PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olaf Wolkenhauer PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gerold Schuler PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julio Vera PhD .

Editor information

Editors and Affiliations

  1. Children's Medical Center Hospital, Tehran University of Medical Sciences Research Center for Immunodeficiencies, Tehran, Iran

    Nima Rezaei

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Vera, J., Gupta, S.K., Wolkenhauer, O., Schuler, G. (2015). Envisioning the Application of Systems Biology in Cancer Immunology. In: Rezaei, N. (eds) Cancer Immunology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44006-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44006-3_23

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44005-6

  • Online ISBN: 978-3-662-44006-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation