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Expression profiling of whole genomes, and modern high-throughput proteomics, has created a revolution in the study of disease states. Approaches for gene expression analysis (time series analysis and clustering) have been applied to functional genomics related to cancer research, and have yielded major successes in the pursuit of gene expression signatures. However, these analysis methods are primarily designed to identify correlative or causal relationships between entities, but do not consider the data in the proper biological context of a “biological pathway” model. Pathway models form a cornerstone of systems biology. They provide a framework for (1) systematic interrogation of biochemical interactions, (2) management of the collective knowledge pertaining to cellular components, and (3) discovery of emergent properties of different pathway configurations.
CFD Research Corporation has developed advanced techniques to interpret microarray data in the context of known biological pathways. We have applied this integrative biological pathway-centered approach to the specific problem of identifying a genetic cause for individuals predisposed to mefloquine neurotoxicity. Mefloquine (Lariam) is highly effective against drug-resistant malaria. However, adverse neurological effects (ataxia, mood changes) have been observed in human sub-populations. Microarray experiments were used to quantify the transcriptional response of cells exposed to mefloquine. Canonical pathway models containing the differentially expressed genes were automatically retrieved from the KEGG database, using recently developed software. The canonical pathway models were automatically concatenated together to form the final pathway model. The resultant pathway model was interrogated using a novel signaling control flux (SCF) algorithm that combines Boolean pseudodynamics (BPD) to relax the cumbersome steady-state assumptions of SCF. The SCF-BPD algorithm was used to identify and prioritize pathways critical to adverse effects of mefloquine. Further analysis resulted in the identification of specific sub-cellular targets that may explain mefloquine neurotoxicity in human subpopulations on the basis of known single-nucleotide polymorphisms.