Researchers at the department of Biomedical Engineering combine basic science (how) with engineering (how to)‏. They investigate, develop and apply engineering principles and tools to unravel the pathophysiology of diseases and enhance diagnostics, intervention and treatment.

Interdisciplinary research has been realized in the three thematic programs: Regenerative Medicine, Molecular Imaging and Systems Biology

Systems Biology:

The Systems Biology theme aims to enhance the shift from ‘describing’ life’s processes to ‘understanding’ them and ‘capturing’ them in validated predictive models, and even ‘managing’ or ‘controlling’ them. The scope of this theme covers all levels from molecules to organs and humans, such as described by the transcriptome, proteome, metabolome and the physiome.


Eindhoven-TU/e-BMT is partner of 


Metabolic Networks and Metabolic Diseases

Metabolism of sugars and lipids involves a dynamic interplay between different tissues and organs. At the cellular level multiple metabolic pathways interact to use or store metabolic substrates and respond to changes in demand and supply. This metabolic network is controlled by signal transduction pathways, such as insulin signaling. A systems biology approach is used to study the complexity of the biochemical networks operating at multiple scales (in time and space). Computational modeling allows us to understand and predict how disturbances in the networks are related to metabolic diseases, such as Type 2 Diabetes and Metabolic syndrome.

Clinical applications

Systems Biology based approaches towards Personalized Healthcare and Personalized Medicine (Systems Medicine).

1. Metabolic Diagnostics
Developing comprehensive computational models of metabolic networks to provide a platform for computation, analysis and visualization of different types of biological data, such as metabolomics in combination with transcriptomics and proteomics (‘data integration’): Network-based analysis of metabolomic data (HUMETICS)

2. Self-management and empowerment of the diabetes patient with a lifestyle and decision support system based on predictive computer modelling: Personalized Virtual Diabetic Patient Simulator (e-DES)

Technology Development: Methods for Computational Systems Biology and Patient-Specific Modelling

Understanding of adaptations in biological systems composed of interacting pathways, cells and organs during disease progression and in response to therapeutic intervention is obtained through dynamic models in which unknown model parameters are inferred from longitudinal, time-course data.
Analysis and quantification of how the predictive power of models is affected by uncertainties in experimental data and in models is becoming increasingly important in Systems Biology. Methodology for parameter estimation, parameter sensitivity analysis, identifiability analysis and uncertainty analysis is developed. The methods can be used in experimental design to select experiments that will reduce the uncertainty of specific, key predictions in an optimal manner.

Publications of Van Riel and co-workers

An overview of publications can be found here.

Or search: PubMed or Google Scholar, citation analysis, h-index.

Presentations (SlideShare)

Systems Biology Education