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.
Highlights
In preclinical and clinical research, different stages of a disease can be phenotyped by collecting information of the genome, proteome, microbiome, etc
We presented applications of a recently developed, generic computational workflow employing mathematical modelling to predict the long-term metabolic adaptations in the development and progression of multi-factorial diseases, or after a therapeutic intervention
adaptations in parameter trajectories (ADAPTs) identifies which metabolic parameters and fluxes necessarily have to change to describe the dynamics of the experimental data collected at different stages of a disease or treatment intervention
Summary
In preclinical and clinical research, different stages of a disease can be phenotyped by collecting information of the genome, proteome, microbiome, etc. The data at each stage provide a snapshot of the phenotype [1]. To integrate and interpret this multivariate data, systems biology approaches, such as pathway-based analyses and computational modelling, are becoming increasingly important [2,3]. Despite the progress in bioinformatics and computational systems biology, novel computational approaches are necessary to exploit the full potential of the information contained in the data. We hypothesized that computational modelling using differential equations provides a suitable basis for the development of an approach to link phenotype snapshots as a function of time, hereby providing an integrated understanding of disease progression. Molecular data at the metabolome, proteome and transcriptome levels should be integrated (the field of molecular systems biology).
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