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These experiments typically consist of a sequence of processing steps that need to be executed on selected computing platforms. Execution poses a challenge, however, due to (1) the<jats:italic>complexity and diversity of applications<\/jats:italic>, (2) the<jats:italic>diversity of analysis goals<\/jats:italic>, (3) the<jats:italic>heterogeneity of computing platforms<\/jats:italic>, and (4) the<jats:italic>volume and distribution of data<\/jats:italic>.<\/jats:p><jats:p>A common strategy to make these in silico experiments more manageable is to model them as<jats:italic>workflows<\/jats:italic>and to use a workflow management system to organize their execution. This article looks at the overall challenge posed by a new order of scientific experiments and the systems they need to be run on, and examines how this challenge can be addressed by workflows and workflow management systems. 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