Metabolic diseases are an ever-increasing health concern in the developed world. Non insulin dependent diabetes mellitus (NIDDM) affects over 100 million people worldwide and significantly contributes to chronic diseases such as atherosclerosis and kidney failure. Obesity accounts for increased morbidity in the industrial world and it is estimated that 31% of US adults fall into this category according to their Body Mass Index. At the heart of these conditions is glucose homeostasis, which balances carbohydrate energy requirements through gluconeogenesis in the fasted response.

                         Current molecular biology understands the regulation of this pathway at the genetic level through the action of transcription factors and molecules such as IRS-1, PPARa and PGC-1a. We wish to couple the genetic mechanisms of action of these factors to a physiologic picture of gluconeogenesis metabolism through the use of bioinformatic and metabolomic tools.

                         Specifically, the aims of this thesis will be: 1) to further improve available experimental methods that measure glucose and metabolite labeling in mammalian cell cultures and organisms; 2) to validate the developed method with current isotopomer analysis protocols used for laboratory measurement of gluconeogenesis; 3) to deploy the developed labeling methods for gluconeogenesis rate estimation in the analysis of glucose output and metabolic energy state in hepatocyte culture models; 4) to understand the metabolic state effects of genetic alterations related to gluconeogenesis in diabetes and obesity through cell culture models; 5) to combine gene expression tools with the aforementioned labeling methods to gain a systemic perspective of changes that regulate glucose levels upon fasting.