Mechanism-based models of biochemical networks and model-based experiments

The ‘systems biology’ concept has gained increasing interest in the last years. Large-scale high-throughput biomolecular techniques, such as functional genomics, have confronted (molecular) biologists with genes and gene products integrated in a functional network. Systems biology aims at understanding and manipulating the dynamic behavior and regulation of integrated intra- and intercellular biomolecular networks. It integrates descriptive, high-throughput experimental molecular biology, hypothesis driven research and computational modeling.

Compared to other biomedical ‘system’ research, such as in biomechanics (which is founded on physical laws), systems biology has a stronger focus on biology and molecular pathways. This implies one has to deal with the relatively limited quantitative information on systems dynamics contained in biological data.


In the Systems Biology program a multitude of physiological systems and biomedical problems is being investigated. This is feasible because similar mathematics and computational methods can be applied. The biomedical applications are based on state-of-the-art research of our partners in the field of cell biology, biochemistry and physiology.


Research topics:

A multi-scale approach, both in experiments as well as in modeling, is essential to gain understanding of complex, multi-factorial diseases.

Our strategy for the development of models for predictive biosimulation  combines a complementary top-down and bottom up approach. We start with the development of computational models based on literature and information from databases. Computational modeling of biosystems is crucial to transform the increasing amounts of data and information into (applicable) knowledge. The computational models integrate data and information from different experimental sources into quantitative, self-consistent descriptions.


1. Top-down:

Integrative Computational Physiology


2. Bottom-up:

Systems Biology



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Last update: February 2010


Current PhD students

Willemijn Groenendaal

(Philips Research)
3D model (spatio-temporal) of skeletal muscle myocyte metabolism

Joep Schmitz (PREDICCt)

Iintegrating computational modeling and NMR spectroscopy to study human skeletal muscle metabolism in type 2 diabetes

Christian Tiemann
Metabolic Syndrome Developing a computational model of liver metabolism in relation to insulin resistance
Joep Vanlier
Metabolic Syndrome Multi-scale modeling and model parameterization
Ceylan Çölmekçi

Systems biology of insulin signaling


Former PhD students

Mark Musters

(SenterNovem / Unilever Research)
Qualitative Modeling in Computational Systems Biology; Applied to Vascular Aging, PhD. Thesis, 2007