Postdoctoral Associate
Churchill Group, The Jackson Laboratory

Address:
The Jackson Laboratory
600 Main Street
Bar Harbor
Maine, 04609

Phone: 207-288-6715
Email: rachael.hageman@jax.org

I am currently working on three main projects:

Project One
DBA/2 mice were put on a high fat diet to assess the role of different tissues in processing of excessive amounts of saturated fat. A tissue survey consisting of 8 tissues (5 white adipose depots, brown adipose, liver, and muscle) were processed on microarrays. After 6 weeks of the diet, mice already showed signs of liver steatosis and an activation inflammation and immune pathways in adipose tissue. I am working to further dissect the tissue specific role of the fat tissues in the high fat diet condition.

Project Two
Over the past years The Jackson Laboratory’s mutagenesis program has established many heritable mutant models of disease. Among the heritable mutants is an excellent collection of phenotypic deviants with high HDL cholesterol levels. Liver samples from the 15 heritable lines were processed on microarrays. In collaboration with Ron Korstanje, we are working to identify possible null mutations and the downstream pathways that lead to high HDL levels in these mice.

Project Three
Mathematical modeling is critical to further our understanding of complex biological systems. Metabolism at a cellular level consists of biochemical pathways made up of reactions. Enzymes are required to facilitate metabolic reactions, but they are also members of larger regulatory network comprised of interacting genes and proteins.

The network stoichiometry of cell metabolism is fairly well understood and can be modeled as a system of ordinary differential equations. However, the regulatory network structure is less understood and likely to vary across individuals and tissue types. In order to predict the genetic effects on metabolism these networks must be integrated in a way that accounts for network variability.

The integration of these networks in the organism is a new frontier complicated by various tissue and cell types. With the advent of high throughput technology such as the microarray, we are able to construct tissue specific regulatory structure. A mathematical model of the mouse liver metabolism has been developed. A Gaussian graphical model has been developed from the microarray data in project 2. I am integrating these two systems to make quantitative predictions of the genetic perturbations that raise HDL levels.