Measuring system dynamics: mRNA, protein and metabolite profiling

Compared to the traditional reductionist approach, systems biology seeks to explain biological phenomenon, not on a gene-by-gene basis, but through the net interactions of all cellular and biochemical components within a cell or organism. As systems biology is driving technological developments, we sought to improve high-throughput measurements of the major cellular molecules and apply multiple molecular profiling approaches to measure cellular system dynamics. We focused on three classes of molecules: mRNA, proteins and metabolites. For mRNA, expression deconvolution, a new algorithm for expression pattern analysis, was proposed to reveal dynamic changes in cell populations by reinterpretation of DNA microarray data. For proteins, a novel statistical method was established to calculate protein expression levels from shotgun proteomics; protein levels measured by this approach correlate well with protein abundance measured by Western blot and 2D gels. For metabolites, we took advantage of the extended 13C NMR spectral range and developed 1 H-13C 2D-NMR for in vitro and in vivo metabolic profiling of cells. With these technologies, we combined mRNA, protein and metabolite profiling to study one carbon metabolism and the yeast cell cycle. Integrating various “omic” data, we showed that local changes in one carbon metabolism (AdoMet hyperaccumulation) causes a gross change in the global metabolome, accompanied by both transcriptional and post-transcriptional responses, ultimately leading to a G1-delay defect in the cell cycle. We began mapping the yeast cell cycle in terms of dynamic abundance changes of the major cellular molecules. All these studies indicate that for many cases the measurement of mRNA is not predictive of the corresponding protein or metabolite abundances. Consequently, these different types of data provide complementary information to elucidate control mechanisms otherwise evident. This validates an essential idea of systems biology: it is only by integrating different levels of biological information that the cell’s state can be fully described.