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Virtual-Laboratory for e-Science (VL-e) towards a new science paradigm. The VL-e project has four programme lines, most of them containing more than one subprogramme. These programme lines structure the research and facilitate the dissemination of results: P1 e-Science in applications

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Virtual-Laboratory for e-Science (VL-e)

towards a new science paradigm

The VL-e project has four programme lines, most of them containing more than one subprogramme.These programme lines structure the research and facilitate the dissemination of results:

  • P1 e-Science in applications

Creates several research prototypes of advanced e–Science application specific Problem Solving Environments (PSEs) in the area of food science, medical science, telescience, data intensive computing, bioinformatics and biodiversity.

  • P2 Generic Virtual Laboratory methodology

Develops the fundamental knowledge for the Virtual Laboratory. It focuses on generic methodologies for e-Science such as problem solving, adaptive information disclosure, visualization and user interfaces.

  • P3 Large-scale distributed systems

Develops fundamental knowledge development in the area of large-scale distributed computer systems based on high performance networking and Grid technology.

  • P4 Scaling up & validating in 'real-life applications'

Carries out field tests for evaluation and scaling up of the proof-of-concept environments under well-chosen real-life conditions.

SP1.3: Medical Diagnosis and Imaging

SP1.6: Dutch TeleScience Laboratory

  • Bioinformatics of biological data-integration.
  • Development of a PSE for Dutch food research institutes & industry.
  • Providing PSE for DUTELLA subprojects.
  • EcoGrid, a national database for biodiversity information.
  • Covers experimental sciences for which experiments generate large data sets.
  • Aims at developing a medical PSE.

mass spectrometer

proteins are cut by enzymes

  • Biomarker discovery with high resolution LC-FTICRMS.
  • Molecular imaging
  • Combining types of data
  • Image based diagnosis
  • Access to storage, computing and visualization resources
  • Neuro-imaging applications
  • Access for knowledge workers to:
    • Vast amount of data and information
    • Computing resources, irrespective of their location or formats
  • Data-integration
  • PSE for bioinformatics
  • Integrate experiment data from different 'omics' levels

VL-e Mission & Strategy

VL-e Research Programs

  • Storage of large data sets
  • Uniform & secure access to the data sets
  • Persistent, and efficient data management

data processing


healthy and diseased cerebral spinal fluid

  • Develop a PSE for integrated analysis of observations and model results.


and warnings



Compare results to find a biomarker for a specific disease

query databases for possible proteins

select best qualified peaks






Application feedback










SP2.2: Adaptive Information Disclosure

SP2.4: Collaborative Information Management

SP2.3: User Interfaces and Virtual Reality

SP2.1: Interactive PSE



Generic service



Lab. services

  • Dynamic, model-driven information and knowledge extraction tools on top of an architecture for grid-based distributed data analysis.
  • Design a PSE architecture that supports the HPC and HTC modes of computing on the Grid.
  • Study the roles of grid-enabled VR environments for efficient and effective analysis of large, complex, time-dependent 3D data sets.
  • Design of generic collaborative information management architecture

Virtual Lab. rapid prototyping

(interactive simulation) ( P2)

  • Adaptive algo-rithms to ensure performance on a dynamically changing Grid.
  • Generic architec-ture for a grid-based iPSE.

Virtual Laboratory Layer (P2)

  • Developing novel visualization techniques and interactive 3D interfaces which adhere to the requirements of Grid enabled applications.
  • Exploring Grid technology for federated query processing
  • Design system for automatic generation of database schema definitions based on ontology

Additional Grid Services (OGSA services)

Grid Middleware

  • Semantic models
  • Agent technology
  • Data mining, grammar induction
  • Dynamic maintenance of ontologies





Network Service

(lambda networking)

Infrastructure (computing, storage, network)

VL-E Proof of concept Environment (P4)

VL-E Experimental Environment (P3)




SP1.1: Data intensive Sciences


SP1.2: Food Informatics

SP1.5: Bioinformatics

SP1.4: Biodiversity

P1 e-Science Applications

P2 Generic Virtual Laboratory methodology

P2 Generic Virtual Laboratory methodology

P2 Generic Virtual Laboratory methodology

P3 Large-scale distributed systems

P4 Scaling up & validating in 'real-life applications

Stable Application

& VL-e component

Vl-E certification Environment (P4)

A set of tests that have to be passed

before any application software or VL-e component can be deployed on the VL-e proof of concept environment

SP4.1: Scaling up & validating

SP3.1: High Performance distributed Computing

SP2.5: VL & system Integration

  • Develops for large-scale Grid:
    • a Java-centric grid programming environment (Ibis) for high-performance applications
    • easy to use, highly portable & robust scheduling infrastructure for co-allocation.
  • Software engineering and certification
  • Deployment of a national-scale infrastructure
  • Knowledge dissemination and import
    • tutorials, developer & admin events
  • Defines the architecture for integrating software developed within VL-e.
  • Develops the framework for application workflow management
  • Developsa resource management for Grid based-systems

Process-Flow Template: represents the application Workflow

Study: Instance of the application


Experiment Topology: self contained data processing modules representing the Application dataflow


To boost e-Science by creating an e-Science environment and carrying out research on



To carry out concerted research along the complete e-Science technology chain, ranging from applications to networking, focusing on new methodologies and re-usable components.

The essential components of the total e-Science technology chain are:

  • e-Science development areas,
  • a Virtual Laboratory development area,
  • a Large-Scale Distributed computing development area, consisting of high performance networkingand grid parts.

Unstable Application

& VL-e component

R. Belleman, A. Belloum, M. Bouwhuis.

Virtual Laboratory for e-Science