Qatar is now accumulating important expertise in biomedical data analytics. At QCRI, we are interested in providing for biomedical researchers based on their computational needs and in developing tools for data analytics in biomedical research. When computers extract patterns and classifiers from a body, they are used to predict new data that helps in defining a prior threat or disease. One non-invasive powerful technique for detecting and quantifying bio-markers linked to diseases (metabolites) is Nuclear Magnetic Resonance (NMR) spectroscopy. 1H NMR spectroscopy is commonly used in the metabolic profiling of biofluids. Metabolites in biofluids are in dynamic equilibrium with those in cells and tissues so their metabolic profile reflects changes in the state of an organism due to disease or environmental effects. The analysis of signals obtained from patients may be performed via methods which incorporate prior knowledge about the metabolites that contribute to the 1H NMR spectroscopic signals, recorded in a metabolite dataset. This paper presents a novel model and computationally automated approach that allows for the simulation of datasets of NMR spectra in order to test real data analysis techniques, hypotheses and experimental designs. Unlike others, this model generates NMR spectra of biofluids unlimited by the magnetic field or pH. It is simple to implement, requires small storage, and is easy to compute and compare. Moreover, it can treat metabolites with a relatively high number of 1H due to a special technique in programing based on physical properties. This model can open the door wide to a new technique of metabolite quantification and thus a better determination of metabolite concentrations which is the key of disease identification. The area of NMR expands rapidly and holds great promise in terms of the discovery of potential biomarkers of diseases, such as diabetes, an area of increasing concern in Qatar, and cancer, which is the third cause of death in Qatar.


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