Predicting metabolic biomarkers of human inborn errors of metabolism

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome-scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red-blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome-scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10-fold increase in biomarker detection performance

Information management and multivariate analysis techniques for metabolomics data

Among the so-called "omics" disciplines,metabolomics has been receiving considerable attention over the last few years. Metabolomics is the large-scale study ofmetabolites that are smallmolecules within cells, biofluids and tissues, produced as a result ofmetabolism. The growing interest inmetabolomics has been encouraged by rapid advances inmetabolic profiling techniques and by technological developments of the diverse analytical platforms, including proton NucleicMagnetic Resonance (1H NMR), Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS), used for extracting metabolic profiles. The output generated from these experimental techniques results in the production of a huge amount of data and information. This thesis attempts to provide an overview of the analytical technologies, the resources and databases employed in this emerging discipline, and ismainly focused on the following two aspects: (i) the challenges of handling the large amounts of data generated and managing the complex experimental processes needed to produce them; (ii) the techniques for the multivariate analysis of metabolomics data, with a special emphasis on methods based on the randomforest algorithm. To this aim, a detailed description and explanation of QTREDS, a software platform designed for managing, monitoring and tracking the experimental processes and activites of "omics" laboratories is provided. In addition, a thorough elucidation of the software package RFmarkerDetector, available through the Comprehensive R Archive Network (CRAN), and a description of the multivariate analysis techniques it implements, is also given

Constructing a bio-health knowledge base for access via a standardised electronic health record prototype

Aim and Objectives: To explore the feasibility of accessing biological information and associated health information through a standards-based electronic health record. The objectives include constructing: a condition specific knowledge base prototype; an EHR system prototype based on a standard record architecture; and an interface that connects the two. Method: An ontology was constructed to organise biological and health information in a formal and structured way. Cystic fibrosis was selected as an exemplar condition and the Continuity of Care Record was selected for an EHR prototype application. The sequence variations information and health information in the knowledge base are presented through the EHR prototype's interface and the results are evaluated. Results: A substantive knowledge base prototype of cystic fibrosis was constructed. The content includes: the most common genetic mutations related to cystic fibrosis; time-oriented descriptions of cystic fibrosis; Cochrane conclusions; and gene therapy for cystic fibrosis. The content is organised on both time and problem oriented axes. It was found to be possible to present bio-health information that was case-specific through the EHR prototype interface. Conclusion: Sequence variations information and associated health information can be made accessible through a standards-based electronic health record prototype. Complex knowledge can be accessed, to some extent automatically, thereby providing a starting point for integrating formal and structured biological information within health record systems which can be deployed in clinical settings.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Constructing a bio-health knowledge base for access via a standardised electronic health record prototype

Aim and Objectives: To explore the feasibility of accessing biological information and associated health information through a standards-based electronic health record. Theobjectives include constructing: a condition specific knowledge base prototype; an EHR system prototype based on a standard record architecture; and an interface that connects the two.Method: An ontology was constructed to organise biological and health information in a formal and structured way. Cystic fibrosis was selected as an exemplar condition andthe Continuity of Care Record was selected for an EHR prototype application. The sequence variations information and health information in the knowledge base are presented through the EHR prototype's interface and the results are evaluated.Results: A substantive knowledge base prototype of cystic fibrosis was constructed. The content includes: the most common genetic mutations related to cystic fibrosis;time-oriented descriptions of cystic fibrosis; Cochrane conclusions; and gene therapy for cystic fibrosis. The content is organised on both time and problem oriented axes. It was found to be possible to present bio-health information that was case-specific through the EHR prototype interface.Conclusion: Sequence variations information and associated health information can be made accessible through a standards-based electronic health record prototype. Complexknowledge can be accessed, to some extent automatically, thereby providing a starting point for integrating formal and structured biological information within health recordsystems which can be deployed in clinical settings

RAMEDIS - Rare Metabolic Diseases Publishing Tool for Genotype-Phenotype Correlation

Hofestädt R, Mischke U, Scholz U, Töpel T, Scheible D, Trefz FK. RAMEDIS - Rare Metabolic Diseases Publishing Tool for Genotype-Phenotype Correlation. In: Patel VL, ed. Proceedings of the 10th World Congress on Medical Informatics. Studies in health technology and informatics. Vol 84. Amsterdam: IOS Press; 2001: 970-974