(246e) A Comprehensive Insulin Signaling Model for Predicting Phenotypes Using Expression Data

The insulin signal transduction
pathway is an active area of biomedical research. In this pathway, the peptide
insulin binds to the insulin receptor, and initiates a complex signal
transduction cascade that ultimately results in the transport of the glucose
transporter GLUT4 to the plasma membrane. There is currently immense interest
in developing a comprehensive mathematical model for this pathway that
incorporates newly-uncovered information including feedback mechanisms in the
pathway (Sogard P, et al., Acta Physiol. Scand.183: 125-126, 2005.).

In this presentation, we will
report a comprehensive mathematical model of the insulin signaling pathway. This
model will incorporate downstream signaling events between the protein kinases
(PKC, Akt) and GLUT4 translocation, based on recent findings in this area. The model
will also include recently elucidated feedback mechanisms in this pathway. Both
these features enable more accurate prediction of metabolic events by the
model, and have not been included in previous models of insulin signaling. In
addition, we will report an analysis of the feedback mechanisms in this pathway,
from a system dynamics perspective. This is expected to provide insights on
regulation within this pathway.

Furthermore, we will report the
use of this model to predict insulin sensitivities and phenotypes from experimental
gene expression data including microarray analysis of glycerol kinase-knockout
(Gyk k/o) mice. Preliminary analysis using the model predicted that certain
genes with altered expression in the Gyk k/o mice confer decreased insulin
sensitivity. The analysis with the extended model will explain, at least
partly, why patients with glycerol kinase deficiency (an inherited human
disorder) develop insulin resistance.

The mathematical model reported
here will thus provide a novel use of a metabolic engineering tool (pathway analysis)
to translate gene expression data into insulin sensitivity, an important
biomedical indicator of phenotype.

Keywords: Insulin signaling, GLUT4,
mathematical model, microarray, glycerol kinase.