Literature DB >> 21968890

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

Anke Ryll1, Regina Samaga, Fred Schaper, Leonidas G Alexopoulos, Steffen Klamt.   

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.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21968890     DOI: 10.1039/c1mb05261f

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  12 in total

1.  Predicting neuroblastoma using developmental signals and a logic-based model.

Authors:  Jennifer C Kasemeier-Kulesa; Santiago Schnell; Thomas Woolley; Jennifer A Spengler; Jason A Morrison; Mary C McKinney; Irina Pushel; Lauren A Wolfe; Paul M Kulesa
Journal:  Biophys Chem       Date:  2018-04-30       Impact factor: 2.352

2.  Reconstructing Boolean models of signaling.

Authors:  Roded Sharan; Richard M Karp
Journal:  J Comput Biol       Date:  2013-01-03       Impact factor: 1.479

3.  Active compound of Zingiber cassumunar Roxb. down-regulates the expression of genes involved in joint erosion in a human synovial fibroblast cell line.

Authors:  Rujirek Chaiwongsa; Siriwan Ongchai; Phorani Boonsing; Prachya Kongtawelert; Ampai Panthong; Vichai Reutrakul
Journal:  Afr J Tradit Complement Altern Med       Date:  2012-10-01

4.  Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

Authors:  Regina Samaga; Steffen Klamt
Journal:  Cell Commun Signal       Date:  2013-06-26       Impact factor: 5.712

5.  The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail.

Authors:  Katrin Kolczyk; Regina Samaga; Holger Conzelmann; Sebastian Mirschel; Carsten Conradi
Journal:  BMC Bioinformatics       Date:  2012-09-28       Impact factor: 3.169

6.  Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies.

Authors:  Sven Thiele; Luca Cerone; Julio Saez-Rodriguez; Anne Siegel; Carito Guziołowski; Steffen Klamt
Journal:  BMC Bioinformatics       Date:  2015-10-28       Impact factor: 3.169

7.  Coordinating Role of RXRα in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling.

Authors:  Roland Keller; Marcus Klein; Maria Thomas; Andreas Dräger; Ute Metzger; Markus F Templin; Thomas O Joos; Wolfgang E Thasler; Andreas Zell; Ulrich M Zanger
Journal:  PLoS Comput Biol       Date:  2016-01-04       Impact factor: 4.475

Review 8.  A review of active learning approaches to experimental design for uncovering biological networks.

Authors:  Yuriy Sverchkov; Mark Craven
Journal:  PLoS Comput Biol       Date:  2017-06-01       Impact factor: 4.475

9.  Experimental design schemes for learning Boolean network models.

Authors:  Nir Atias; Michal Gershenzon; Katia Labazin; Roded Sharan
Journal:  Bioinformatics       Date:  2014-09-01       Impact factor: 6.937

Review 10.  Computational Modeling in Liver Surgery.

Authors:  Bruno Christ; Uta Dahmen; Karl-Heinz Herrmann; Matthias König; Jürgen R Reichenbach; Tim Ricken; Jana Schleicher; Lars Ole Schwen; Sebastian Vlaic; Navina Waschinsky
Journal:  Front Physiol       Date:  2017-11-14       Impact factor: 4.566

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.