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Interface Focus. 2013 Apr 6;3(2):20120084. doi: 10.1098/rsfs.2012.0084.

Applications of analysis of dynamic adaptations in parameter trajectories.

Author information

1
Department of Biomedical Engineering , Eindhoven University of Technology , Den Dolech 2, Eindhoven, 5612 AZ , The Netherlands ; Institute for Complex Molecular Systems , Eindhoven University of Technology , Den Dolech 2, Eindhoven, 5612 AZ , The Netherlands ; Netherlands Consortium for Systems Biology , University of Amsterdam , Science Park 904, Amsterdam, 1098 XH , The Netherlands.

Abstract

Metabolic profiling in combination with pathway-based analyses and computational modelling are becoming increasingly important in clinical and preclinical research. Modelling multi-factorial, progressive diseases requires the integration of molecular data at the metabolome, proteome and transcriptome levels. Also the dynamic interaction of organs and tissues needs to be considered. The processes involved cover time scales that are several orders of magnitude different. We report applications of a computational approach to bridge the scales and different levels of biological detail. Analysis of dynamic adaptations in parameter trajectories (ADAPTs) aims to investigate phenotype transitions during disease development and after a therapeutic intervention. ADAPT is based on a time-dependent evolution of model parameters to describe the dynamics of metabolic adaptations. The progression of metabolic adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages. To get a better understanding of the concept, the ADAPT approach is illustrated in a theoretical study. Its application in research on progressive changes in lipoprotein metabolism is also discussed.

KEYWORDS:

metabolic networks; multi-scale; parameter estimation; progressive disease; systems biology; uncertainty analysis

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