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

  • Protocol
Systems Medicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1386))

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.

*These authors contributed equally.

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Acknowledgements

This work was supported by the German Federal Ministry of Education and Research (BMBF) as part of the projects eBio:miRSys [0316175A to J.V. and B.S.], and eMed:CAPSyS [01ZX1304F to J.V. and B.S.]. Julio Vera is funded by the Erlangen University Hospital (ELAN funds, 14-07-22-1-Vera-González) and the German Research Foundation (DFG) through the project SPP 1757/1 (VE 642/1-1 to J.V.).

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    Authors and Affiliations

    1. Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

      Martin Eberhardt, Xin Lai, Namrata Tomar & Julio Vera

    2. Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

      Martin Eberhardt, Xin Lai, Namrata Tomar, Gerold Schuler & Julio Vera

    3. Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

      Shailendra Gupta

    4. Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Marburg, Philipps University, Marburg, Germany

      Bernd Schmeck

    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

      Bernd Schmeck

    6. Department of Immune Modulation at the Department of Dermatology, University Hospital Erlangen, Erlangen, Germany

      Alexander Steinkasserer

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    1. Martin Eberhardt
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    Correspondence to Julio Vera .

    Editor information

    Editors and Affiliations

    1. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

      Ulf Schmitz

    2. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

      Olaf Wolkenhauer

    Glossary

    Normalization in differential gene expression analysis 

    Analyses almost always combine measurements from multiple microarray chips or NGS runs which will differ in parameters that dictate background strength and signal ranges for each particular feature. To make those measurements comparable with each other, inter-measurement normalization techniques have been developed. The most widely employed is probably quantile normalization, an algorithm that orders features by their expression value separately for each sample, averages the expression for each rank across all samples, and redistributes the averages to the original feature position in each sample. In many software tools, the “normalization” step offers additional computations, often including the transformation into the logarithmic scale (see below).

    (Log-)transformation 

    The distribution of signal values in a sample covers several orders of magnitude. Usually, very low or undetectable (i.e., zero) signals are highly overrepresented while a few signals are found as upper outliers, and both skew the gross signal distribution. To remedy this and make the distribution more malleable to established methods of statistical testing, a simple option is transforming the data into log scale. Although the base of the logarithm is an arbitrary choice, base 2 is used in many software tools to make fold-change calculations more intuitive.

    Moderation 

    In nontechnical terms, moderation is a statistical method that manipulates a distribution in a way that is conceived to yield better results. In the case of microarrays, moderation can be used to decrease the relative difference in feature variances so that the conditions for further statistical tests are (better) met.

    False discovery rate (FDR) 

    The FDR is the fraction of falsely called features in a set of called features. For example, if differential expression analysis yields a list of 100 called features but only 90 of them can be validated experimentally, the empiric FDR is 10 %.

    Correction for multiple testing/control of the FDR 

    For a differential expression analysis on a set of 50,000 features, 50,000 tests must be performed. At a significance level of 0.05, we expect 2500 of those test results to be true just by chance. Such a large number of false positives will drown out the true positive features with no direct way to distinguish between both. There are ways for dealing with this unfortunate consequence by adjusting the test scenario in such a way that the proportion of falsely called features in the results is statistically limited (“controlled”).

    Correlation network 

    A network that is based on—and perhaps spatially arranged according to—the correlation values between its nodes.

    ODE model 

    A system of mechanistic ordinary differential equations that determine the temporal state of the corresponding system of biochemical reactions.

    PDE model 

    A system of partial differential equations is a quantitative description of how a biochemical system changes in space and time.

    Agent-based model 

    Agent-based modeling is a rule-based, discrete-event, and discrete-time computational modeling methodology that employs abstract objects and focuses on the interactions among the individual objects (i.e., agents) of a system.

    Boolean model 

    A Boolean network consists of a set of nodes whose state (0 or 1) is determined by linking other nodes in the network through Boolean functions, such as AND and OR.

    Deterministic model 

    A dynamic system is deterministic if its trajectory is uniquely determined by the initial state and a given parameter set.

    Stochastic model 

    In contrast to deterministic models, for which the output will be identical using the same initial state and parameter set as the input, a stochastic system, at a given initial state in the phase space, can end with different states with different probabilities. In other words, the same input given to a stochastic system several times can result in different outputs.

    Sensitivity analysis 

    Determining the change in model variables (such as concentrations of molecular species) influenced by changes in parameter values (such as velocities of biochemical reactions).

    Bifurcation analysis 

    An analysis that shows how the qualitative behavior (e.g., the loss of stability and appearance of sustained oscillations) of a model changes as a function of critical model parameters.

    Bistability 

    For a certain set of parameters, a system has two stable steady states that are separated by an unstable steady state.

    Hysteresis 

    A hallmark of bistability: as a critical parameter (i.e., bifurcation parameter) increases beyond a particular value, the system jumps to an alternative steady state from the original steady state; then, if the parameter decreases below the value, the system jumps back to the original steady state.

    In silico

    An expression used to mean “performed” on a computer or via computer simulation.

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    Cite this protocol

    Eberhardt, M. et al. (2016). Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_9

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    • DOI: https://doi.org/10.1007/978-1-4939-3283-2_9

    • Publisher Name: Humana Press, New York, NY

    • Print ISBN: 978-1-4939-3282-5

    • Online ISBN: 978-1-4939-3283-2

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