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Genom Comput Biol. 2018;4(1). pii: e100050. doi: 10.18547/gcb.2018.vol4.iss1.e100050. Epub 2017 Dec 6.

Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model.

Author information

1
GenHotel EA3886, Univ Evry, Université Paris-Saclay, 91025, Evry, France.
2
Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg.
3
Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, USA; Department of Medicine, Weill Cornell Medical College, New York City, USA.
4
Faculty of Health Sciences Simone Veil, INSERM U1173, University of Versailles Saint-Quentin-en-Yvelines, Montigny-le-Bretonneux, France.
5
Centre National de Recherche en Génomique Humaine (CNRGH), CEA, Evry, France.
6
Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.

Abstract

In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.

KEYWORDS:

Complex human disease; Computational systems biology; Dynamical modelling; Interactive molecular map; Rheumatoid arthritis; Signaling network

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