ADAPT – Analysis of Dynamic Adaptations in Parameter Trajectories

This article describes a novel modelling approach to simulate disease progression and evaluate the long-term effects of pharmacological interventions. It has been published in the open access journal PLoS Computational Biology.

Tiemann CA, Vanlier J, Oosterveer MH, Groen AK, Hilbers PA, van Riel NA. Parameter trajectory analysis to identify treatment effects of pharmacological interventions. PLoS Comput Biol. 2013 Aug;9(8):e1003166.


A driving ambition of medical systems biology is to advance our understanding of molecular processes that drive the progression of complex diseases such as Type 2 Diabetes and cardiovascular disease. This insight is essential to enable the development of therapies to effectively treat diseases. A challenging task is to investigate the long-term effects of a treatment, in order to establish its applicability and to identify potential side effects. As such, there is a growing need for novel approaches to support this research. Here, we present a new computational approach to identify treatment effects. We make use of a computational model of the biological system. The model is used to describe the experimental data obtained during different stages of the treatment. To incorporate the long-term/progressive adaptations in the system, induced by changes in gene and protein expression, the model is iteratively updated. The approach was employed to identify metabolic adaptations induced by a potential anti-atherosclerotic and anti-diabetic drug target. Our approach identifies the molecular events that should be studied in more detail to establish the mechanistic basis of treatment outcome. New biological insight was obtained concerning the metabolism of cholesterol, which was in turn experimentally validated.

The research was funded by the Netherlands Consortium for Systems Biology (NCSB) and the FP7 Systems Medicine project RESOLVE.