Type 2 diabetes is a global disease which affects an increasing number of people
every year. At the heart of the disease lies insulin resistance in the target tissues,
primarily fat and muscle. The insulin resistance is caused by the failure of a complex
signalling network, and several mechanistic hypotheses for this failure have
been proposed. Herein, we evaluate a hypothesis that revolves around the protein
mTOR (mammalian target of rapamycin) and its feedback signals to the insulin
receptor substrate-1 (IRS1). In particular, we have re-examined this hypothesis
and relevant biological data using a mathematical modelling approach.
	During the course of modelling we gained several important insights. For instance,
the model was unable to reproduce the relation between the EC50-values
in the dose-response curves for IRS1 and its serine residue 312 (Ser-312). This
implies that the presented hypothesis, where the phosphorylation of Ser-312 lies
downstream of the tyrosine phosphorylation of IRS1, is inconsistent with the provided
data, and that the hypothesis or the data might be incorrect. Similarly, we
also realized that in order to fully account for the information in the dose-response
data, time curves needed to be incorporated into the model.
	A preliminary model is presented, which explains most of the data-sets, but
still is unable to describe all the details in the data. All in all, the originally
proposed hypothesis as an explanation to the given data has been revised, and
our analysis serves to exemplify that an evaluation of a mechanistic hypothesis by
mere biochemical reasoning often misses out on important details, and/or leads to
incorrect conclusions. A model-based approach, on the other hand, can efficiently
pin-point such weaknesses, and if combined with a comprehensive understanding
of biological variation and generation of experimental data, mathematical modelling
can prove to be a method of great potential in the search for mechanistic
explanations to the cause of insulin resistance in type 2 diabetics.