Obesity, type 2 diabetes and its associated comorbidities are among the most prevalent and challenging
conditions confronting the medical profession in the 21st century. A major metabolic consequence
of obesity is insulin resistance, which is strongly associated with deposition of triglycerides
and uncontrolled glucose production in the liver, thereby contributing to the development of type 2
diabetes. In spite of intense research, the complex etiology of insulin resistance remains poorly understood
because of a missing framework that integrates prior knowledge on cellular signaling with
systematically acquired quantitative data on multiple parts of the involved hormonal and metabolic
control networks and relate them to organ function and whole body metabolism. Here we propose
to employ a systems biology approach to characterize these regulatory networks and their connectivities
in the context of single cells, organ and whole organism and subjects with insulin resistance
to develop predictive models of metabolic control. The proposal is a central component of ongoing
programs at the Competence Center for Systems Physiology & Metabolic Diseases and will be executed
by a highly collaborative effort of leading laboratories in the field whose expertise extends
from molecular/medical physiology, proteomics, metabolomics, single cell analytics and nanotechnology
to computational biology and network modeling. It includes four subprojects: The goal of
Subproject 1 is to perform quantitative and dynamic measurements of hormonal signaling and metabolism
including key metabolites and metabolic fluxes, construction of a liver phosphoprotein
Atlas, measurement of the dynamic phosphoproteome as well as transcription in response to insulin
and glucagon in perfused livers of normal and insulin resistant mice. The goal of Subproject 2 is
to quantify the dynamics of hormonal and metabolic pathways regulating glucose and fatty acid
metabolism by analyzing key insulin signaling molecules and the activity of metabolic enzymes
using nanoplasmonic biosensors and integrated microfluidics at a single-cell level in hepatocytes.
Subproject 3 is an integrated effort to analyze and mathematically model metabolic control networks
in normal livers and in insulin resistant states using experimental data from Subprojects 1
and 2 and prior knowledge. The development of mechanistic dynamic models will yield experimentally
testable hypotheses on the links between signaling networks, gene regulation, posttranscriptional
regulation and metabolism. Lastly, the focus of Subproject 4 is on the experimental testing
and validation of novel hypotheses generated in Subprojects 1 to 3 using both mouse and human
livers. A wide spectrum of modern molecular and in vivo imaging techniques is available to perturb
specific signaling events and/or metabolic fluxes and study the effect on hepatic metabolism and
disease development/progression. Together, these aims will (i) establish a mammalian system (organ)
model describing the dynamics of hormonal and metabolic signaling networks in normal and
insulin resistance states in a quantitative manner, (ii) provide the basis for generating predictive,
testable models of metabolic control, (iii) establish the foundation for application of this systems
approach to other hormones, organs and diseases, and (iv) have broad implications for pharmacological
approaches to prevent/reduce the development of type 2 diabetes.