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Methods Mol Biol. 2016;1386:135-79. doi: 10.1007/978-1-4939-3283-2_9.

Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive.

Author information

1
Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
2
Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
3
Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
4
Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Marburg, Philipps University, Marburg, Germany.
5
Systems Biology Platform, Institute for Lung Research/iLung, German Center for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps University Marburg, Marburg, Germany.
6
Department of Immune Modulation at the Department of Dermatology, University Hospital Erlangen, Erlangen, Germany.
7
Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany. julio.vera-gonzalez@uk-erlangen.de.
8
Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany. julio.vera-gonzalez@uk-erlangen.de.

Abstract

The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.

KEYWORDS:

Immune intervention; Immunogenicity; Immunoinformatics; Kinetic modeling; Network reconstruction; Systems immunology

PMID:
26677184
DOI:
10.1007/978-1-4939-3283-2_9
[Indexed for MEDLINE]

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