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Envisioning the Application of Systems Biology in Cancer Immunology

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Cancer Immunology

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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 multiscale 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 multiscale 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, the chapter contains a glossary of systems biology terms.

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Acknowledgments

This work was supported by the German Federal Ministry of Education and Research (BMBF) as part of the projects eBio:MelEVIR [031L0073A to JV and 031L0073B to OW]. JV is funded by the Staedler Stiftung and the Manfred Roth Stiftung.

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Authors and Affiliations

  1. Department of Dermatology, Faculty of Medicine, Friedrich Alexander Universität Erlangen-Nürnberg, Erlangen, Germany

    Tanushree Jaitly, Gerold Schuler & Julio Vera

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

    Shailendra K. Gupta & Olaf Wolkenhauer

Authors
  1. Tanushree Jaitly
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  2. Shailendra K. Gupta
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  3. Olaf Wolkenhauer
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  4. Gerold Schuler
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  5. Julio Vera
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Correspondence to Julio Vera .

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Editors and Affiliations

  1. Research Center for Immunodeficiencies, Children’s Medical Center, Pediatrics Center of Excellence, Tehran University of Medical Sciences, Tehran, Iran

    Nima Rezaei

Glossary (Extended Definitions Are Available in the Encyclopedia of Systems Biology [110])

Pathway

Biochemical system with a unique input signal, in which network compound interactions follow a rather sequential cascade of events.

Network

Complex and highly interconnected biochemical system composed of dozens to hundreds of interacting proteins, metabolites, RNAs, as well as several concurrent input signals.

Cross-Talk

Property of a biochemical system integrated by several pathways, in which signals from one pathway modulate the activity of the other.

Regulatory Map

Graphical depiction, following a code of symbols, of the compounds, interactions, input signals, and phenotypic output of a biochemical network. One can say that a regulatory map is a visualization of the state of the art of the biomedical knowledge about the biochemical network.

Positive Feedback Loop

Biochemical system in which the activation of a biochemical event positively regulates a biochemical process upstream the system. Under some conditions, this kind of system induces signal amplification, bistability and hence the conversion of a transient signaling into a sustained one.

Negative Feedback Loop

Biochemical system in which the activation of a biochemical event negatively regulates a biochemical process upstream the system. Under some conditions, this kind of system induces homeostasis, but it can also provoke the emergence of sustained oscillations in the concentration or activation of the network compounds.

Feedforward Loop

Biochemical system in which a downstream network compound is simultaneously regulated by, for example, a transcription factor and a protein whose expression is regulated by the transcription factor. The feedforward loop is coherent when the downstream network compound is consistently regulated by both interactions (both interactions activate or both inhibit) and incoherent when the regulation is opposite.

Model Calibration

Computational procedure in which quantitative data are integrated with the mathematical model. The aim is to give values to the model parameters, in a way that model simulations are able to reproduce the experimental data available.

Predictive Model Simulation

Computational procedure in which the model can be used to extrapolate the behavior of the system investigated under experimental conditions not yet tested.

Model Validation

Procedure by which predictive model simulations are compared with new experimental data, not used in model calibration. A model is considered validated when there is an agreement between the predictive simulations and the experimental data used.

ODE Model

Mathematical model of biochemical systems that describe spatio-temporal changes of protein concentrations and other biological molecules using kinetic equations. These equations describe the variation on time of the populations or concentration of the considered biomolecules.

Boolean/Logic Model

Class of discrete computational models used to model biochemical systems, in which the network compounds can have one of the two possible states at any time: 1 or ON, which means that the compound is expressed or active; and 0 or OFF, nonexpressed or inactive.

Agent-Based Model

Class of discrete computational models used to model biochemical systems and cell-to-cell interactions. A cellular automaton is the computational representation of a regular grid of cells. Each cell can have a finite number of states (similar to the ON/OFF of Boolean models), and transitions in states affected by the states of the surrounding cells in the grid.

Bistability

Property of biochemical networks containing positive feedback loops, by which small perturbations drastically change the behavior of the system, for example, inducing a transition between quick signal termination after transient stimulation and persistent activation.

Self-Sustained Oscillations

Property of some biochemical systems containing negative feedback loops, in which the concentration of the network components oscillates regularly in time, even under constant external stimulation.

Sensitivity Analysis

Computational tool used to analyze mathematical models. This tool provides information about the model parameters for which a variation in their value significantly affects the behavior of the system.

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Jaitly, T., Gupta, S.K., Wolkenhauer, O., Schuler, G., Vera, J. (2020). Envisioning the Application of Systems Biology in Cancer Immunology. In: Rezaei, N. (eds) Cancer Immunology. Springer, Cham. https://doi.org/10.1007/978-3-030-30845-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-30845-2_27

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