The regulation of gene expression is at the intersection of all facets of biology. It is clear that a systems-level analysis of gene expression will provide the most accurate perspective of this intersection. Systems biology assumes that the whole does not equal the sum of all of the parts. Knowing all of the genes and the total sum of their interactions will not translate into an understanding of how the genome programs cells in the way that it does. Instead, the genome confers emergent properties onto cells that can best be captured by systems-level analysis. This requires the synthesis of biological, computational, mathematical, and engineering approaches.
We use the model experimental systems of budding yeast and Drosophila to analyze gene regulatory networks. We use a variety of methods in our experiments. These range from the genome-wide scale (RNA-seq, ChIP-seq, whole-genome sequencing) to the single-molecule scale (single-molecule FISH, fluorescence correlation spectroscopy). We routinely use fluorescent protein tagging coupled with quantitative microscopy to measure protein levels in individual cells within tissues. We can even measure the differential protein output from the two alleles of a single gene. We work closely with theorists in applied mathematics, physics, and engineering to construct models that explain and predict our experimental data.
Some of the questions we are pursuing have to with the following. Does the inherent biochemical stochasticity that underlies gene expression cause errors in cell behavior or are there mechanisms to suppress this noise. How does the metabolic rate within cells couple to gene regulation so that cell growth and gene expression are synchronized. How does the genome become globally reprogrammed for gene expression as cells transition from pluripotency to restricted fates in an embryo.