Functional interpretation of 2D gel, mass spec and NMR proteomics data represents a non-trivial problem in computational biology. First, there are almost no tools for linking high-throughput (HT) experimental data with cellular pathways, processes and diseases. Second, unlike genomics DNA sequencing and microarray-based RNA expression profiling, proteomics has not yet evolved into a robust lab methodology. It lags behind in terms of resolution (for example the number of distinguishable proteins vs. the number of expressed genes in a human blood sample), reproducibility and consistency. Third, concurrent comparison of different types of HT data for the same sample could be of help, but very few such parallel studies have been conducted. Finally, even when available, different data types are often as incompatible as “apples” and “oranges”.
We developed a systems biology platform for simultaneous visualization, cross-referencing and analysis of microarray gene expression, proteomics, metabolic profiles, and phenotypic data in the context of disease pathways. The system is based on a comprehensive, manually curated database on human biology and pathology covering multiple levels of cellular functionality. Genes, proteins and compounds are assembled into “vertical” pathways tracing the cellular response from membrane receptors via signal transduction pathways and transcriptional factors down to core metabolism. The database unique architecture allows to integrate different kinds of HT experimental data (even from different sources, samples and experiments) on the backbone of pathways and networks. We will demonstrate the system capabilities using microarray and proteomics data for cancer and glaucoma.
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