Author Archives: Natal van Riel

About Natal van Riel

Natal van Riel (1973) is Associate Professor in Systems Biology and Metabolic Diseases at the Department of Biomedical Engineering of the Eindhoven University of Technology (TU/e). He was trained in system identification and control engineering at the Department of Electrical Engineering, TU/e (MSc degree in 1995). In 2000 he obtained a PhD degree in Molecular Cell Biology from Utrecht University (Prof. Verrips; Netherlands) for research on integrating computational modelling and experiments to study cell metabolism. The research was carried out in the Biotechnology group of Unilever Research Vlaardingen. From 2000 to 2003 he worked in the Department of Electrical Engineering of TU/e investigating the application of system and control theory to understand biological processes. In 2003 he was appointed as Assistant Professor in the Department of Biomedical Engineering at the same university and initiated Systems Biology research in Eindhoven. This research was expanded when he joined the group of Prof. Hilbers in 2006 to lead the Computational Systems Biology research program, investigating complex, multi-factorial diseases. In 2014 he was appointed as Associate Professor. In 2014 he was a visiting scholar of the Department of Bioengineering at the University of California San Diego (UCSD) in the group of Prof. Palsson. His current research focuses on metabolic network modelling, methods for model parametrization and analysis of dynamic models, and applications in Metabolic Syndrome and associated diseases such as Type 2 Diabetes.

A systems biology approach reveals the physiological origin of hepatic steatosis induced by liver X receptor activation

Liver X receptor (LXR) agonists exert potent antiatherosclerotic actions but simultaneously induce excessive triglyceride (TG) accumulation in the liver. To obtain a detailed insight into the underlying mechanism of hepatic TG accumulation, we used a novel computational modeling approach called analysis of dynamic adaptations in parameter trajectories (ADAPT). We revealed that both input and output fluxes to hepatic TG content are considerably induced on LXR activation and that in the early phase of LXR agonism, hepatic steatosis results from only a minor imbalance between the two. It is generally believed that LXR-induced hepatic steatosis results from increased de novo lipogenesis (DNL). In contrast, ADAPT predicted that the hepatic influx of free fatty acids is the major contributor to hepatic TG accumulation in the early phase of LXR activation. Qualitative validation of this prediction showed a 5-fold increase in the contribution of plasma palmitate to hepatic monounsaturated fatty acids on acute LXR activation, whereas DNL was not yet significantly increased. This study illustrates that complex effects of pharmacological intervention can be translated into distinct patterns of metabolic regulation through state-of-the-art mathematical modeling.

Hijmans BS, Tiemann CA, Grefhorst A, Boesjes M, van Dijk TH, Tietge UJ, Kuipers F, van Riel NA, Groen AK, Oosterveer MH. A systems biology approach reveals the physiological origin of hepatic steatosis induced by liver X receptor activation. FASEB Journal, 2014 Dec 4. [Epub ahead of print]

http://www.ncbi.nlm.nih.gov/pubmed/25477282

http://www.fasebj.org/content/early/2014/12/03/fj.14-254656.long

A Physiology-Based Model Describing Heterogeneity in Glucose Metabolism: The Core of the Eindhoven Diabetes Education Simulator (E-DES)

Current diabetes education methods are costly, time-consuming, and do not actively engage the patient. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES, https://diabetessimulator.wordpress.com). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication. The physiological model consists of 4 compartments for which the inflow and outflow of glucose and insulin are calculated using 6 nonlinear coupled differential equations and 14 parameters. These parameters are estimated on 12 sets of oral glucose tolerance test (OGTT) data (226 healthy subjects) obtained from literature. The resulting parameter set is verified on 8 separate literature OGTT data sets (229 subjects). The model is considered verified if 95% of the glucose data points lie within an acceptance range of ±20% of the corresponding model value. All glucose data points of the verification data sets lie within the predefined acceptance range. Physiological processes represented in the model include insulin resistance and β-cell function. Adjusting the corresponding parameters allows to describe heterogeneity in the data and shows the capabilities of this model for individualization. We have verified the physiological model of the E-DES for healthy subjects. Heterogeneity of the data has successfully been modeled by adjusting the 4 parameters describing insulin resistance and β-cell function. Our model will form the basis of a simulator providing individualized education on glucose control.

Maas AH, Rozendaal YJ, van Pul C, Hilbers PA, Cottaar WJ, Haak HR, van Riel NA. A Physiology-Based Model Describing Heterogeneity in Glucose Metabolism: The Core of the Eindhoven Diabetes Education Simulator (E-DES). J Diabetes Sci Technol., 2014 Dec 18. [Epub ahead of print]

http://www.ncbi.nlm.nih.gov/pubmed/25526760

http://dst.sagepub.com/content/early/2014/12/18/1932296814562607.long

 

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.

YouTube: Systems Biology of Disease Progression

Systems Biology of Disease Progression

Systems Biology of Disease Progression