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Lipid metabolism is critically important to the normal function of individual cells and for signaling between tissues of the human body. There is a great need to quantify changes in lipid metabolism because of its implications in both normal healthy conditions and during disease development. In our first study, we developed a liquid chromatography mass spectrometry based workflow to assess in vivo metabolism of murine brain lipids. This involved sample preparation, data acquisition, and analysis software development and improvements. Regarding sample preparation, we maintained the mice at 5% body water for the duration of the experiment. As mouse metabolism proceeds, enzymes can add deuterium atoms (D) from D2O into lipid C-H bonds. These newly synthesized D-labeled lipids display shifts in their isotopic envelopes. To observe these shifts, we used mass spectrometry acquisitions to measure the mass spectra of isotopic envelopes. We calculated changes in these isotopic envelope shifts to deduce several metabolic metrics. We used these metabolic metrics to make inferences about metabolism changes. These metrics include n-value, fraction new, rate, and asymptote for each lipid. We deduced n-value by replacing one hydrogen with a deuterium at a time in the lipid's theoretical chemical formula. The number of deuterium atoms in the theoretical D-labeled chemical formula that agrees best with its respective empirical spectrum is the n-value. A large part of this effort was assessing the reproducibility and quality control of n-values that were derived from empirical spectra. We compared these n-values to two sets of ground truth n-values that we generated. We generated one set of ground truth n-values by referencing biochemical pathways and published n-values. We used a linear algebra approach to deduce the other set of ground truth n-values. We compared both sets of ground truth n-values to n-values derived from empirical D-labeled lipid spectra. We found that both sets of ground truth n-values correlate well with n-values from empirical spectra. Using these n-values, we calculated fraction new for each lipid. This fraction new indicates what percentage of a lipid's pool is newly synthesized at a given time. For a given lipid, we calculated the fraction new for each time point and biological replicate. We plotted these fraction values together against time in days. From this fraction new vs time plot for each lipid, we deduced its asymptote and rate constant. In our second study, we added the additional dimension of drift time to the data acquisition and analysis using ion mobility spectrometry. We added this additional dimension so that we could further separate lipid isomers and prevent spectral convolution. Preliminary results suggest that lipid isomers may have distinct metabolic regulation.