Nabil SEMMAR
Scientific Tracks Abstracts: J Comput Sci Syst Biol
Metabolic regulation processes of drug metabolites can be statistically analysed from the variability between and within chromatographic profiles representing different subjects? states along time. To analyse such regulation processes, a new metabolomic approach was developed by combining in silico metabolic profiles representing different biochemical regulation states. Illustration was based on 248 profiles of L-dopa and its metabolites (3-OMD, DOPAC and HVA) analysed at different times (from L-dopa administration) in 34 patients suffering from Parkinson disease. After statistical classification of population into different metabolic trends (MbTrs), the separated profiles were iteratively combined in silico by applying a Scheffé?s mixture design. Taking into account the variability within and between MbTrs, the mixture design was iterated several times to calculate a complete set of average profiles from which gradual regulations between metabolites were graphically analysed to understand the functional aspect of the studied metabolic system. The results highlighted MbTr-dependent relationships between metabolites, revealing high metabolic flexibility. Apart from this static application, the mixture design was applied at the different sampling times. Results visualizations on 3-D plots (time t , metabolite x , metabolite y ) highlighted a counter- clock hysteresis between precursor (DOPAC) and product (HVA) suggesting metabolic regulation lag between them that was revealed to be compatible with metabolic network.
Metabolomics:Open Access received 895 citations as per Google Scholar report