Background Interleukin-6 is a pleiotropic cytokine with high clinical relevance and an important mediator of cellular communication, orchestrating both pro- and anti-inflammatory processes. Interleukin-6-induced signalling is initiated by binding of IL-6 to the IL-6 receptor α and subsequent binding to the signal transducing receptor subunit gp130. This active receptor complex initiates signalling through the Janus kinase/signal transducer and activator of transcription pathway. Of note, IL-6 receptor α exists in a soluble and a transmembrane form. Binding of IL-6 to membrane-bound IL-6 receptor α induces anti-inflammatory classic signalling, whereas binding of IL-6 to soluble IL-6 receptor α induces pro-inflammatory trans-signalling. Trans-signalling has been described to be markedly stronger than classic signalling. Understanding the molecular mechanisms that drive differences between trans- and classic signalling is important for the design of trans-signalling-specific therapies. These differences will be addressed here using a combination of dynamic mathematical modelling and molecular biology. Methods We apply an iterative systems biology approach using set-based modelling and validation approaches combined with quantitative biochemical and cell biological analyses. Results The combination of experimental analyses and dynamic modelling allows to relate the observed differences between IL-6-induced trans- and classic signalling to cell-type specific differences in the expression and ratios of the individual subunits of the IL-6 receptor complex. Canonical intracellular Jak/STAT signalling is indifferent in IL-6-induced trans- and classic signalling. Conclusion This study contributes to the understanding of molecular mechanisms of IL-6 signal transduction and underlines the power of combined dynamical modelling, model-based validation and biological experiments. The opposing pro- and anti-inflammatory responses initiated by IL-6 trans- and classic signalling depend solely on the expression ratios of the subunits of the entire receptor complex. By pointing out the importance of the receptor expression ratio for the strength of IL-6 signalling this study lays a foundation for future precision medicine approaches that aim to selectively block pro-inflammatory trans-signalling. Furthermore, the derived models can be used for future therapy design. Graphical abstract Electronic supplementary material The online version of this article (10.1186/s12964-019-0356-0) contains supplementary material, which is available to authorized users.

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The development of mathematical models is an iterative process. One of its key steps is the acquisition of experimental data. For developing quantitative and dynamical models, this data needs to contain time-courses as well as several perturbation experiments. This article gives an overview of and an introduction into various experimental techniques frequently used during the process of developing dynamical models of biological signalling networks. Among others, the article discusses western blotting, enzyme assays, flow cytometry, protein mass spectrometry, DNA microarrays and different fluorescent microscopy techniques and stresses the significance of single-cell versus population measurements.

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Apoptosis -programmed cell death -is a key component in cell renewal, i.e. for removing damaged cells or those not any more required without harming their environment. Understanding apoptosis is challenging since the involved signalling network is complex and intertwined. We use linear control theory to shine light on the complex signalling mechanisms and interactions. To this end we linearise different models around the steady state corresponding to the unstimulated case and use linear control analysis to uncover and identify a decentralised control scheme with two plant and two controller modules that are sparsely interconnected. While this separation on a first view is artificial, it allows for using the small gain theorem to analyse stability and to give insight into how inhibitors naturally modulate cell death. This sheds light onto the role of positive and negative feedback present in this signalling pathway. Sensitivity analysis reveals these most influential parameters on the time of death. To illustrate the analysis, we examine in simulations to which extent apoptosis can be reversed once initiated.