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Computational Infrastructures for Data and Knowledge Management in Systems Biology

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Systems Biology

Abstract

The volume, complexity and heterogeneity of data originating from high throughput functional genomics technologies have created challenges and opportunities for Information technology (IT) departments. These increased demands have also led to increasing costs for IT infrastructure such as necessary computing power and storage devices, as well as further costs for manpower effort, required for maintenance. This chapter describes some of the challenges for computational analysis infrastructure, including bottlenecks and most pressing needs that have to be addressed to effectively support the development of systems biology and its application in medicine.

The authors Fotis Georgatos and Stéphane Ballereau contributed equally to this chapter.

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Abbreviations

3D:

Three dimensional

4D:

Four dimensional typically 3D plus time dimension

BOINC:

Berkeley Open Infrastructure for Network Computing

CERN:

European Council for Nuclear Research

CPU:

Central Processing Unit

EBI:

European Bioinformatics Institute

EGEE:

Enabling Grids for E-sciencE

EGI:

European Grid infrastructure

ELIXIR:

European Life Sciences Infrastructure for Biological Information

EMBL:

European Molecular Biology Laboratory

Flops:

FLoating-point Operations Per Second

GPU:

Graphical Processing Unit

HPC:

High Performance Computing

HTC:

High Throughput Computing

IaaS:

Infrastructure as a Service

IT:

Information Technology

I/O:

Input/Output—typically used in the context of software processing of data

LHC:

Large Hadron Collider

MPI:

Message Passing Interface

Omics:

A collective term to refer to -omics keywords like metabolomics, genomics, proteomics etc.

PaaS:

Platform as a Service

PRACE:

Partnership for Advanced Computing in Europe

ROI:

Return On Investment

SaaS:

Software as a Service

SBML:

Systems Biology Markup Language

WLCG:

Worldwide LHC Computing Grid

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Acknowledgements

This work was supported by the CNRS, and in part by the EU grants to CA in the context of the MeDALL consortium (Mechanisms of the Development of Allergy, Grant Agreement FP7 N°264357), the U-BIOPRED consortium (Unbiased Biomarkers for the PREDiction of respiratory disease outcomes, Grant Agreement IMI 115010), and the eTRIKS consortium to CA, DB, MG, YG, RS, RB (European Translational research Information & Knowledge management Services, Grant Agreement n°115446). The formation of the European Institute for Systems biology & Medicine hosted at Claude Bernard University is supported by the Lyonbiopole competitive cluster and its academic, industrial and local authority partners, including Grand Lyon, Région Rhône-Alpes, Direction de la Recherche et de la Technologie, and the Finovi Foundation (CA). We would like to acknowledge the support of the Luxembourg Centre for Systems Biomedicine and the University of Luxembourg (LH and RB), the NIH General Medical Sciences Center for Systems Biology GM076547 (LH) and a Department of Defense contract on Liver Toxicity W911SR-09-C-0062 (LH).

Author information

Authors and Affiliations

  1. Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts-Fourneaux, 4362, Esch-sur-Alzette, Luxembourg

    Fotis Georgatos, Rudi Balling & Reinhard Schneider

  2. European Institute for Systems Biology and Medicine—CNRS-UCBL-ENS, Université de Lyon, 50 avenue Tony Garnier, 69007, Lyon, France

    Stéphane Ballereau, Johann Pellet & Charles Auffray

  3. Department of Computing, Imperial College London, London, SW7 2AZ, UK

    Moustafa Ghanem & Yi-Ke Guo

  4. Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109-5234, USA

    Nathan Price & Leroy Hood

  5. Centre de Calcul de l’IN2P3, USR6402 CNRS/IN2P3, 43 Bd du 11 Novembre 1918, 69622, Villeurbanne Cedex, France

    Dominique Boutigny

Authors
  1. Fotis Georgatos
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  2. Stéphane Ballereau
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  4. Moustafa Ghanem
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  11. Reinhard Schneider
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Correspondence to Reinhard Schneider .

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Editors and Affiliations

  1. Chemical and Biological Engineering, Vanderbilt University, Nashville, TN, USA

    Aleš Prokop

  2. Research Group on Process Network Engineering, Kaposvár University, Kaposvár, Hungary

    Béla Csukás

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Georgatos, F. et al. (2013). Computational Infrastructures for Data and Knowledge Management in Systems Biology. In: Prokop, A., Csukás, B. (eds) Systems Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6803-1_13

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