Network-based analysis of metabolomic data

Integration of large, multi-dimensional sets of molecular data

The collection of biological data has grown expansively, producing vast amounts of data. The integration of large, multi-dimensional datasets (combining, e.g., genomics, transcriptomics, proteomics and metabolomics) is currently considered to be one of the major bottlenecks in molecular life sciences and systems biology.

Metabolomics and metabolic profiling

The metabolome (metabolite profile) can be considered as a good indication of the current physiological state of a cell or organism, constituting the interaction of the genotype with the environment. In medical research and clinical diagnostics metabolomics (metabolite profiling) can provide the bridge to interpret genetic data in the context of individual patients.

Genome-scale metabolic models and Constrained-based simulation

The basis of the data integration method are computational models containing all metabolic reactions known to exist in a certain species or in a specific cell type of a multicellular organism (Genome-Scale Metabolic Models, GSMM). The models enable to link individual variations in the genome to the phenotype-specific (personal) metabolic profile. The aim is to obtain a better understanding of how genetic differences affect metabolism. And the other way around, how the metabolic profile reflects the combination of one’s genotype and health condition. This is relevant for medical research and clinical diagnostics.

HUMETICS - HUman METabolic diagnostICS

HUMETICS is a software platform for the automated analysis of human metabolome data. Current applications include neuropsychiatric disorders, in particular Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and depression. These neuropsychiatric disorders are associated with disturbed metabolism of tryptophan, an important precursor for the neurotransmitter serotonin.

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