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Envisioning the Application of Systems Biology in Cancer Immunology

  • Julio Vera
  • Shailendra K. Gupta
  • Olaf Wolkenhauer
  • Gerold Schuler
Chapter

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.

Keywords

Biochemical Network Cleavage Motif Quantitative Experimental Data Complex Biochemical System Predictive Model Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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].

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Julio Vera
    • 1
    • 2
  • Shailendra K. Gupta
    • 3
    • 4
  • Olaf Wolkenhauer
    • 3
  • Gerold Schuler
    • 2
  1. 1.Laboratory of Systems Tumor Immunology, Department of DermatologyUniversity Hospital ErlangenErlangenGermany
  2. 2.Department of Dermatology, Faculty of MedicineFriedrich Alexander Universität, University of Erlangen-NurnbergErlangenGermany
  3. 3.Department of Systems Biology and Bioinformatics, Institute of Computer ScienceUniversity of RostockRostockGermany
  4. 4.Department of BioinformaticsCSIR-Indian Institute of Toxicology ResearchLucknowIndia

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