During the past century, studies of metabolic disorders have focused research efforts to improve clinical diagnosis and management, to illuminate metabolic mechanisms, and to find effective treatments. The availability of human genome sequences and transcriptomic, proteomic, and metabolomic data provides us with a challenging opportunity to develop computational approaches for systematic analysis of metabolic disorders. In this paper, we present a strategy of bioinformatics analysis to exploit the current data available both on genomic and metabolic levels and integrate these at novel levels of understanding of metabolic disorders. PathAligner is applied to predict biomedical data based on a given disorder. A case study on urea cycle disorders is demonstrated. A Petri net model is constructed to estimate the regulation both on genomic and metabolic levels. We also analyze the transcription factors, signaling pathways and associated disorders to interpret the occurrence and regulation of the urea cycle. PathAligner's metabolic disorder analyzer is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner/ pathaligner_MDA.html. Supplementary materials are available at http://www.techfak.uni-bielefeld.de/~mchen/metabolic_disorders. © 2005 Elsevier Inc. All rights reserved.
Chen, M., & Hofestädt, R. (2006). A medical bioinformatics approach for metabolic disorders: Biomedical data prediction, modeling, and systematic analysis. Journal of Biomedical Informatics, 39(2), 147–159. https://doi.org/10.1016/j.jbi.2005.05.005