Poster Presentation 2014 International Biophysics Congress

Dynamic systems model of the insulin signalling pathway for identifying causes of insulin resistance (#680)

Martin K.L. Wong 1 2 3 , Dougall Norris 1 , James Burchfield 1 , James Krycer 1 4 , Fatemah Vafaee 2 , Westa Domanova 1 2 3 , Zdenka Kuncic 2 3 , David James 1
  1. Diabetes and Obesity Program, Gravan Institute of Medical Research, Sydney, NSW, Australia
  2. Integrative Systems Lab, University of Sydney, Sydney, NSW, Australia
  3. Institute of Medical Physics, University of Sydney, Sydney, NSW, Australia
  4. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia

Background: Insulin resistance is a precursor to type 2 diabetes and is characterised by reduced cellular responses to the hormone, insulin. The precise topology of the insulin signalling network is not well-defined, making it difficult to identify the implications of signalling defects in causing insulin resistance. Here, systems modelling was undertaken to rigorously identify feedback regulation in the network and to visualise the effects of defects and quantify their contribution in causing insulin resistance in various disease models.

Method: The insulin signalling pathway was constructed using a linearised enzyme kinetic model. The model is parameterised in a healthy state, fitted to time course and dose responses of AKT localisation data from total internal reflection microscopy and phosphorylation data from Western blotting. The models are then validated to insulin resistance cell models by changing the concentration parameters of the system obtained by mass spectrometry. The associated defects of the insulin resistance models are then analysed and evaluated relative to the healthy state.

Results: Preliminary analysis of the model suggests that AKT localisation is a necessary component of the pathway and will be included in the next iteration of the kinetic model.

Conclusion: AKT localisation is an important mechanism governing the dynamics of insulin signalling.