A novel modelling approach to analyze complex metabolic pathway dynamics; application to LXR activated lipoprotein metabolism

Lipoprotein metabolism is governed by a highly complex network of metabolic pathways involved in lipid and protein synthesis and degradation. Understanding the dynamics of network regulation is impossible without the help of computational modeling. We present a new modeling approach to analyze the long term effects of a dietary or pharmacological intervention in lipoprotein metabolism. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of metabolic network adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition of steady states during different stages of the dietary or pharmacological treatment. The trajectories provide insight into the affected underlying biological systems and provide targeted direction to the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects on pathways caused by interactions with the proteome and transcriptome levels can be captured by the time-dependent descriptions of the parameters. The approach was employed to identify metabolic adaptations induced upon a 3-week activation of the liver X receptor (LXR) in C57BL/6J wild-type mice. The metabolic trajectories were modeled to analyze the metabolic adaptations in time. This provided a number of sometimes counterintuitive insights into the underlying mechanisms inducing hepatic steatosis, dynamic changes in plasma triglycerides, and plasma HDL content. The model predicted for instance decreased activity of the scavenger receptor class B1 (SR-B1) despite an increased flux mediated via the receptor. This prediction was validated experimentally by immunoblotting measurements of SR-B1 in hepatic membranes. We also show that this procedure can be used quite simply to select optimal therapeutic targets successfully.