Type II diabetes is one of the most common diseases afflicting people today. Understanding how this disease works, not only on a cellular level and between different organs and tissues, but also how it affects whole body level homeostasis is crucial for enhancement of its treatment. We use model-bases analysis as a tool for distinguishing different biological hypothesis on the system behavior.
The Insulin Receptor (IR), is located in the cell membrane as a dimer, and thus has the potential two bind two different insulin molecules. It can also undergo a series of phosphorylations, as well as having the ability to become internalized, and thus be removed from the cell’s censing area. However, it can then be recycled back to the membrane again. The major target of IR is the Insulin Receptor Substrate 1 (IRS1). IRS1 in turn mediates the signal further downstream through Protein Kinase B (PBK) and mammalian Target of Rapamycin (mTOR). In adipocytes the end result is the translocation of internal vesicles containing Glucose Transporters (GLUT4) to the membrane, thus increasing the uptake of glucose. The liver, on the other hand, responds by down regulating the endogenous glucose production.
The activity of IRS1 is determined by its phospho-tyrosine composition. This in turn is regulated by at least two serine-phosphorylations, on ser307 and ser312. The serine levels of this protein are regulated by downstream kinases, of which only one is known, S6K. The ser307 phosphorylation appears to allow for a short term positive feedback while the ser312 phosphorylation has the dynamics of a more long term negative feedback.
The overall dynamics of the IRS1 tyrosine phosphorylation is a mirror of that of the Insulin Receptor. They both have a quick response to insulin within minutes, manifested as a high overshoot before declining to a steady state level. The overshoot behavior of this system can be explained either by a downstream negative feedback, or by having an advanced internalization and recycling model. Several hypotheses of the negative feedback mechanisms necessary to allow for the receptor to adopt such a behavior have previously been rejected by us. So has the hypothesis of internalization (unpublished data). The internalized Insulin Receptors can account for only a small fraction of the total amount of receptors, it however seems to be necessary for its own down regulation, since without it the overshoot behavior disappears.
The complexity of this system is immense and hence we keep to as minimal models as possible, only considering adding complexity to the system when data indicates so, or when a simpler model structure has been rejected. We model the system with a series of Ordinary Differential Equations (ODEs), optimize and estimate the parameters of a given model structure with the Systems Biology Toolbox (SBTB) and reject, or fail to reject, models based on their statistical agreement with our data. We search the entire approximated parameter space for a sample of all acceptable parameter values for any given parameter. We then look for commonalities shared between model simulations of all parameter sets in the sample. That is, a behavior of e.g. a state in the model that has to be above a certain threshold for it to be able to explain the data, while other states might be of arbitrary sizes. If we find such a commonality, we call it a core prediction. Assuming your data is correct and your analysis thorough, a Core Prediction has the same strength as a model rejection. The common aspect, shared between all acceptable parameter etc, is something that has to be true, no matter how much more data you acquire. One such core prediction, which led to the rejection of the internalization hypothesis, was that the amount of internalized IR had to be above 80% of the total receptor pool. We subsequently rejected this experimentally.
International Conferance on Systems Biology (ICSB), San Fransisco, August 30 - September 4, 2009