A medical bioinformatics approach for metabolic disorders: biomedical data prediction, modeling, and systematic analysis

Authors:
Ming Chen;Ralf Hofestädt
Affiliations:
Group of Bioinformatics, College of Life Science, Zhejiang University, Hangzhou, China and Dept. of Bioinform./Med. Inform., Fac. of Technol., Bielefeld Univ., Postfach, Bielefeld, Germany;Bielefeld Univ., Fac. of Technol., Dept. of Bioinform. and Med. Inform., Universitätsstraße, Bielefeld and Dept. of Bioinform./Med. Inform., Fac. of Technol., Bielefeld Univ., Postfach, ...
Venue:
Journal of Biomedical Informatics
Year:
2006

Citing 4
Cited 1

Petri Net Theory and the Modeling of Systems

Petri Net Theory and the Modeling of Systems
Heterogeneous database integration in biomedicine

Computers and Biomedical Research
Petri Net Representations in Metabolic Pathways

Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Model Validation of Biological Pathways Using Petri Nets - Demonstrated for Apoptosis

CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology

From petri nets to differential equations – an integrative approach for biochemical network analysis

ICATPN'06 Proceedings of the 27th international conference on Applications and Theory of Petri Nets and Other Models of Concurrency

Quantified Score

Hi-index	0.00

Visualization

Abstract

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. PathA-ligner 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.Availability. 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.