Large-scale network models of IL-1 and IL-6 signalling and their hepatocellular specification.

Abstract:

:The pro-inflammatory cytokines interleukin 1 (IL-1) and 6 (IL-6) are crucially involved in the regulation of a multitude of physiological processes, in particular coordinating the immune response upon bacterial infection and tissue injury. Both interleukins induce complex signalling cascades and trigger the production of mitogenic, pro-proliferative, anti-apoptotic, chemotactic, and pro-angiogenic factors thereby affecting the delicate balance between regeneration vs. invasive growth, tumourigenesis and metastasis. Moreover, several links to insulin resistance have been found within their associated signalling networks. Focusing on this from a systems biology perspective, we introduce comprehensive large-scale network models of IL-1 and IL-6 signalling which are based on a logical modelling approach and reflect the current biological knowledge. Theoretical network analysis enabled us to uncover general topological features and to make testable predictions on the stimulus-response behaviour of the networks. In this context, non-intuitive network-wide species dependencies as well as structures of regulatory feedback and feed-forward mechanisms could be characterised. By integrating high-throughput phosphoproteomic data from primary human hepatocytes we optimised the model structures to obtain models with high prediction accuracy for hepatocytes. Our model-based data analysis, for instance, suggested model modifications regarding (i) Akt contribution to IL-1-stimulated p38 MAPK activation and (ii) insignificant p38 MAPK activation in response to IL-6. In light of the presented results and in conjunction with the detailed model documentations, both models hold great potential for theoretical studies and practical applications.

journal_name

Mol Biosyst

journal_title

Molecular bioSystems

authors

Ryll A,Samaga R,Schaper F,Alexopoulos LG,Klamt S

doi

10.1039/c1mb05261f

subject

Has Abstract

pub_date

2011-12-01 00:00:00

pages

3253-70

issue

12

eissn

1742-206X

issn

1742-2051

journal_volume

7

pub_type

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