Modeling of adipocyte signaling
Title: Modeling of adipocyte signaling
SNIC Project: LiU-2018-26
Project Type: LiU Compute
Principal Investigator: William Lövfors <>
Affiliation: Linköpings universitet
Duration: 2018-11-06 – 2020-12-01
Classification: 10610


Mathematical (mechanistic) modeling have emerged as a promising method for understanding the complex mechanisms involved in intracellular signaling. Recently, a model explaining an important subset of the intracellular signaling network, the insulin signaling pathways, in adipocytes (fat cells) have been developed in our group. In this model, the time-response of the key proteins involved have been measured experimentally and can now be explained by the model. However, this model is still only able to explain a small fraction of all proteins involved. In recent years, mass spectrometry have emerged as a promising technology for measuring protein levels in a high-troughput mannor. Now time-resolved measurements of every protein in the cell are available. With this new data, we can now extend the "core" insulin model to cover the entire proteome. We are currently developing a method to automate the expansion of the model, such that all of these newly measured protein can be added to the model. In more detail, this expansion is done by adding "neighbour" proteins not currently in the model given a set of interactions, and then using these newly added proteins to find new neighbours and so on. In this iterative expansion of the model all neighbours can be added simultaneously (which can be parallelized). The expanded model can then be used to simulate new experiments. These simulations can then assist in further understanding the mechanisms involved in diabetes, and possible also to screen for new drug targets in silico.