Understanding the complex relationships among immune cells with mathematical tools
Systems Biology of inflammation
The mammalian immune response depends on the interaction and collaboration of many highly individual cells. In particular, a network of interacting T cells is critical for the course of an inflammatory response. However, in certain circumstances the immune system can turn on itself and thus evoke chronic inflammatory diseases such as rheumatoid arthritis. While research has provided an enormous body of knowledge about the regulatory mechanisms behind chronic inflammation, it is difficult to assess the contribution of each individual process with current biological methods. In particular, what are the critical components and conditions triggering a change towards chronic inflammation, despite the multi-faceted mechanisms that promote immune tolerance? Furthermore, can we develop a rationale for improved strategies of therapeutic intervention? Currently available drugs targeting immune cell communication (so-called targeted or “biological” therapies), such as TNF-alpha blockers, often show limited effectiveness and considerable adverse effects.
Analysis of complex networks requires mathematical methods. Our research group will develop and apply advanced mathematical modelling and data analysis techniques to investigate the regulation of immune responses. In particular, we will employ high-throughput image analysis methods to illuminate the spatial distribution of interacting immune cells. Moreover, we will apply and extend the scope of our response-time modelling framework (Thurley, Wu, Altschuler, Cell Systems 2018), an approach to study generic cell-cell communication networks and integrate kinetic data.
Overall, the goal of the group is to develop an interdisciplinary framework for dissecting and rationalizing intercellular communication networks, to investigate the effects of perturbations and thus pave the road for optimization of targeted therapies in the future.
High-dimensional time series analysis
T cell communication
Dynamical network analysis
Dr. Kevin Thurley
Philipp Burt, Bsc. Biophysics, PhD student
Sebastian Serve, Bsc. Mathematics, MD student
Gustav Geißler, Bsc. Physics, Master student
Prof Steven Altschuler PhD, University of California San Francisco, USA
Prof Lani Wu PhD, University of California San Francisco, USA
Prof Orion Weiner PhD, University of California San Francisco, USA
Prof Dr Max Löhning, DRFZ Berlin
Prof Dr Andreas Radbruch, DRFZ Berlin
Prof Dr Anja Hauser, DRFZ Berlin
Prof Dr Chiara Romagnani, DRFZ Berlin
Prof Dr Thomas Höfer, DKFZ Heidelberg
Dr Alba Diz-Muñoz, EMBL Heidelberg
Dr Elfriede Friedmann, University of Heidelberg
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Thurley K, Wu LF, Altschuler SJ. Modeling cell-to-cell communication networks using response-time distributions. Cell Systems 6:355 (2018).
Thurley K, Herbst C, Wesener F, Koller B, Wallach T, Maier B, Kramer A, Westermark PO. Principles for circadian orchestration of metabolic pathways. PNAS (2017).
Diz-Muñoz A, Thurley K, Chintamen S, Altschuler SJ, Wu LF, Fletcher DA, Weiner OD. Membrane Tension Acts Through PLD2 and mTORC2 to Limit Actin Network Assembly During Neutrophil Migration. PLoS Biol 14(6):e1002474 (2016).
Thurley K, Gerecht D, Friedmann E, Höfer T. Three-Dimensional Gradients of Cytokine Signaling between T Cells. PLoS Comput Biol 11(4):e1004206 (2015).