Literature DB >> 16986268

Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform.

J A C Rullmann1, H Struemper, N A Defranoux, S Ramanujan, C M L Meeuwisse, A van Elsas.   

Abstract

A large-scale mathematical model, the Entelos Rheumatoid Arthritis (RA) PhysioLab platform, has been developed to describe the inflammatory and erosive processes in afflicted joints of people suffering from RA. The platform represents the life cycle of inflammatory cells, endothelium, synovial fibroblasts, and chondrocytes, as well as their products and interactions. The interplay between these processes culminates in clinically relevant measures for inflammation and erosion. The simulation model is deterministic, which allows tracing back the mechanism of action for a particular simulation result. Different patient phenotypes are represented by different virtual patients. The RA PhysioLab platform has been used to systematically and quantitatively study the predicted therapeutic effect of modulating several molecular targets, which resulted in a ranking of putative drug targets and a workflow to confirm the simulations experimentally. In addition, critical pathways were identified that drive the predicted disease outcome. Within these pathways, targets were identified from public literature that were not previously associated with arthritis. The model provides insights into the biology of RA and can be used as a platform for hypothesis-driven research. Case studies of therapies directed against IL-12 and IL-15 illustrate the approach, with emphasis on the analysis of system dynamics.

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Year:  2005        PMID: 16986268     DOI: 10.1049/ip-syb:20050053

Source DB:  PubMed          Journal:  Syst Biol (Stevenage)        ISSN: 1741-2471


  16 in total

Review 1.  Consistent design schematics for biological systems: standardization of representation in biological engineering.

Authors:  Yukiko Matsuoka; Samik Ghosh; Hiroaki Kitano
Journal:  J R Soc Interface       Date:  2009-06-03       Impact factor: 4.118

Review 2.  Mechanistic systems modeling to guide drug discovery and development.

Authors:  Brian J Schmidt; Jason A Papin; Cynthia J Musante
Journal:  Drug Discov Today       Date:  2012-09-19       Impact factor: 7.851

Review 3.  A systems biology approach to synovial joint lubrication in health, injury, and disease.

Authors:  Alexander Y Hui; William J McCarty; Koichi Masuda; Gary S Firestein; Robert L Sah
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2011-08-08

Review 4.  Mechanistic systems biology of inflammatory gene expression in airway smooth muscle as tool for asthma drug development.

Authors:  Chi-Ming Hai
Journal:  Curr Drug Discov Technol       Date:  2008-12

5.  Quantitative Systems Pharmacology: A Framework for Context.

Authors:  Ioannis P Androulakis
Journal:  Curr Pharmacol Rep       Date:  2016-04-08

Review 6.  Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study.

Authors:  David J Klinke
Journal:  J Immunother Cancer       Date:  2015-06-16       Impact factor: 13.751

7.  Systems toxicology: from basic research to risk assessment.

Authors:  Shana J Sturla; Alan R Boobis; Rex E FitzGerald; Julia Hoeng; Robert J Kavlock; Kristin Schirmer; Maurice Whelan; Martin F Wilks; Manuel C Peitsch
Journal:  Chem Res Toxicol       Date:  2014-01-21       Impact factor: 3.739

8.  A Mechanistic Systems Pharmacology Model for Prediction of LDL Cholesterol Lowering by PCSK9 Antagonism in Human Dyslipidemic Populations.

Authors:  K Gadkar; N Budha; A Baruch; J D Davis; P Fielder; S Ramanujan
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2014-11-26

9.  Virtual Systems Pharmacology (ViSP) software for simulation from mechanistic systems-level models.

Authors:  Sergey Ermakov; Peter Forster; Jyotsna Pagidala; Marko Miladinov; Albert Wang; Rebecca Baillie; Derek Bartlett; Mike Reed; Tarek A Leil
Journal:  Front Pharmacol       Date:  2014-10-22       Impact factor: 5.810

10.  Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis.

Authors:  Brian J Schmidt; Fergal P Casey; Thomas Paterson; Jason R Chan
Journal:  BMC Bioinformatics       Date:  2013-07-10       Impact factor: 3.169

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