Systems biology modeling of NAFLD and T2D
Title: Systems biology modeling of NAFLD and T2D
SNIC Project: LiU-compute-2021-7
Project Type: LiU Compute
Principal Investigator: Christian Simonsson <>
Affiliation: Linköpings universitet
Duration: 2021-03-01 – 2022-03-01
Classification: 10610


Obesity is increasing worldwide, making metabolic disease, such as nonalcoholic fatty liver-disease (NAFLD) and diabetes mellitus type 2 (T2D), some of the major health risks today. The development of NAFLD and T2D are both correlated to the metabolic syndrome, which is a cluster of risk factors for cardiovascular disease, which is another major health concern today. Thus, it is important to further understand how these types of metabolic disease develop. However, the underlaying mechanisms for the disease progression are not yet fully understood. The disease progressions for both NAFLD and T2D are complex and intertwined, involving many different organs, different biological levels, and different timescales. One way to study this complex interaction is to use system biology mathematical modeling. By creating mathematical models, we can include previous knowledge about the biological system into one cohesive framework. The model can then be trained and tested-to real life experimental data, using model based-hypothesis testing. However, to be able to determine if a model can describe in vivo experimental data, the model parameters need to be estimated. The parameter estimation can be a computationally heavy process. A process that greatly benefits from parallelization. This estimation of the model parameter values is a key step in the iterative process of testing the different model structures (representing hypothesis of how the biological system works). We now aim to create mechanistic models of the interactions between the two major organs effected by NAFLD and T2D, the adipose tissue and the liver. We have previously published models of how the adipose tissue is affected during insulin resistance and T2D, both on the cellular level and whole-body level. However, to fully explain the disease progression we need to create mathematical models of hepatic fat metabolism. For instance, in obesity, the fatty acids flux from the adipose tissue to the liver is increased, leading to higher storage of fat in the liver. This is one of the key mechanisms in the disease etiology of NAFLD. Having a mathematical model over this interconnection, as well as liver fat metabolism, could help answer questions regarding NAFLD and T2D disease progression.