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Distributed, high-performance and grid computing approaches in biology and biotechnology are faced with a number of challenges that render such endeavors much more complex and intricate than development and deployment of these technologies in classical application areas such as physics, engineering, manufacturing, transport, construction, automotive, etc., and conventional business areas (retail, marketing, customer relationship management and finance). Some of the added complexities arise from the

-  Conceptual complexity of biological knowledge and the methodologies used in biology and biotechnology;

-  Need to understand biological systems and processes at a detailed mechanistic, systemic and quantitative level across several levels of organization (ranging from molecules, to cells, populations, and the environment);

-  Growing availability of high-throughput data from genomics, transcriptomics, proteomics and metabolomics;

-  Widespread use of image data in biological research and development (microscopy, NMR, MRI, PET, X-ray, CT, etc.);

-  Increasing number of investigations studying the properties and dynamic behavior of biological systems and processes using computational techniques (molecular dynamics, QSAR/QSPR, simulation of gene-regulatory, signaling and metabolic networks, protein folding/unfolding, etc);

-  Requirement to combine (in an ad hoc fashion) data, information and compute services (e.g., sequence alignments) residing on systems geographically distributed around the world;

-  Variety of different technologies, instruments, infrastructures and systems used in life science R&D;

-  Huge variety of information formats and frequent addition of new formats arising from new experimental protocols, instruments and phenomena to be studied;

-  Large and growing number of investigated biological and biomedical phenomena;

-  Fact that life science R&D is based heavily on the use of distributed and globally accessible computing resources (databases, knowledge bases, model bases, instruments, text repositories, compute-intensive services).