We explain how e-Science is essential to providing the context in terms of methods, tools and infrastructure for the development of a virtual fly brain. We show examples of steering computational processes, managing knowledge in the spatial context of an organism and formulation of models in developmental biology.
PowerNest (www.powernest.net) is a software tool enabling experimenters to explore the effect of sampling on noise propagation throughout qPCR assays. The sampling process is assumed to be comprised of a number of levels; the acquisition of a sample and the preparation of extracted material, reverse-transcription of the mRNA, and the qPCR itself. Given a small set of data, representative of a larger assay, the error at each stage of the experiment is profiled using a nested-ANOVA.
The Cystic Fibrosis group in the Molecular Medicine Centre (MMC) at the Western General Hospital are developing gene therapies to treat Cystic Fibrosis (CF), a life-threatening hereditary disease. To understand which genes are responsible for the disease, and which genes may have diagnostic or prognostic value, they go through a long process of laboratory experiments and analysis, involving microarray experiments and several types of statistical tests.
The main aim of this project is to provide biologists with a compact, efficient, and uncomplicated software platform for the robust analysis, intuitive representation, and reliable preservation of all data generated by high-throughput gene expression analysis techniques; such as quantitative real-time PCR (qPCR) or microarrays.
Currently, the analysis of gene expression data generated by microarray and real-time PCR experiments is a slow process, often taking several weeks to properly process data from a small number of patients. In anticipation of a new clinical trial expected to involve 200 patients it is necessary to improve the efficiency of these analyses, through automation, in order to return meaningful biological data on a much shorter time scale.
Currently, the analysis of gene expression data generated by microarray and real-time PCR experiments is a slow process, often taking several weeks to properly process data from a small number of patients. In anticipation of a new clinical trial expected to involve 200 patients it is necessary to improve the efficiency of these analyses, through automation, in order to return meaningful biological data on a much shorter time scale.
