Eindhoven University of Technology (TU/e)
Den Dolech 2, 5612 AZ Eindhoven
Prof. dr. ir. N.A.W. van Riel, Prof. dr. P.A.J. Hilbers
0031 (0)40 2475506 N.A.W. van Riel 0031 (0)40 2475512 P.A.J. Hilbers
The Computational Biology (CBio) group of the Department of Biomedical Engineering (BMT) provides computational biology expertise for the development of systems medicine approaches and implementation in clinical research and personalized healthcare. We investigate and apply systems biology, molecular modeling methods, machine learning, and artificial intelligence techniques to construct computational models. With these models we improve our qualitative and quantitative knowledge of biomedical processes and structures such as biomembranes, protein interactions, complex biochemical networks and diseases like cancer, metabolic syndrome and diabetes mellitus.
- Biomedical & health
- systems medicine (enabling personalized medicine and healthcare)
- data science and artificial intelligence
- differential equation modelling (including parameter estimation and uncertainty analysis)
- metabolic network modelling
- synthetic biology and microfluidics
- Metabolism and metabolic diseases
- Metabolic Syndrome
Expertise and Track Record
435002017 Modelling longitudinal multi-omics responses to a weight loss diet: ADAPTing DIOGenes with Blaak (Maastricht University).
Unique services of TU/e-CBio (Technology and Expertise):
- Integration of longitudinal data from different biological levels (metabolomics, proteomics, transciptomics) using metabolic pathway models (‘ADAPT’). The dynamic interaction of pathways, but also of tissues and organs is modeled to describe and analyse data on disease progression and treatment-in-time. Applications in clinical and preclinical studies, with or without intervention.
- Dynamic modelling of biological pathways and networks from prior knowledge and data. Methods related to simulation of nonlinear differential equations, parameter estimation/identification (Maximum Likelihood Estimation, Bayesian inference), identifiability analysis, prediction uncertainty analysis and experimental design (to select experiments that will reduce the uncertainty of specific predictions in an optimal manner).
- Application of computational Systems Biology to human metabolism and metabolic diseases (Metabolic Syndrome, associated co-morbidities such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease).
Five examples of public‐private partnership (PPP) projects recently supported by the CBio group:
(1) Development of a physiology-based computational model that is the basis of a game to educate patients with diabetes (both type 1 and type 2 diabetes): e-DES. Website: http://diabetessimulator.wordpress.com/. Project in collaboration with Máxima Medisch Centrum in Eindhoven and funded by NovoNordisk.
(2) Development of a predictive model of insulin signalling in skeletal muscle for application in the pharmaceutical industry. The model has been applied for optimal design of new experiments. Client: AstraZeneca.
(3) Development of a spatio‐temporal computational model of glucose dynamics in the human skin. Website: http://cbio.bmt.tue.nl/~nvriel/Research/skin/skin.html Client: Philips Research.
(4) Development of a spatio‐temporal computational model to asses the safety of Magnetic Resonance guided High Intensity Focused Ultrasound (MR‐HIFU) for the ablation of tumours. Client: Philips Research.
(5) Analysis of proteomic data from Type 2 Diabetes patients before and after life‐style intervention to identify disease state and intervention specific biomarkers. project: CTMM PREDICCt
- Integration of longitudinal data, metabolomics and transcriptomics.
* PROJECT: FP7 project RESOLVE (http://www.resolve-diabetes.org/),
NGI NCSB program (Netherlands Consortium for Systems Biology)
** ARTICLE: Tiemann CA, Vanlier J, Oosterveer MH, Groen AK, Hilbers PA, van Riel NA. (2013) Parameter trajectory analysis to identify treatment effects of pharmacological interventions. PLoS Comput Biol. 2013;9(8):e1003166. http://www.ncbi.nlm.nih.gov/pubmed/23935478
- Analysis of lipoprotein profiles and metabolism.
* PROJECT: FP7 project RESOLVE (http://www.resolve-diabetes.org/)
** ARTICLE: Sips FL, Tiemann CA, Oosterveer MH, Groen AK, Hilbers PA, van Riel NA. (2014) A computational model for the analysis of lipoprotein distributions in the mouse: translating FPLC profiles to lipoprotein metabolism. PLoS Comput Biol. 10(5):e1003579. http://www.ncbi.nlm.nih.gov/pubmed/24784354
- Analysis of dynamic data of protein activity / targeted proteomics (immunoblotting, immunohistochemistry):
* PROJECT: NGI NCSB program (Netherlands Consortium for Systems Biology),
Public-private partnership with AstraZeneca.
** ARTICLE: Vanlier J, Tiemann CA, Hilbers PA, van Riel NA. (2012) An integrated strategy for prediction uncertainty analysis. Bioinformatics. 28(8):1130-5. http://www.ncbi.nlm.nih.gov/pubmed/22355081
- Analysis of in vivo metabolic time-course data (NMR spectroscopy):
* PROJECT: CTMM PREDICCt (http://www.ctmm.nl/en/projecten/hartvaat/predicct)
** ARTICLE: Schmitz JP, Jeneson JA, van Oorschot JW, Prompers JJ, Nicolay K, Hilbers PA, van Riel NA.2012 Prediction of muscle energy states at low metabolic rates requires feedback control of mitochondrial respiratory chain activity by inorganic phosphate. PLoS One. 7(3):e34118. http://www.ncbi.nlm.nih.gov/pubmed/22470528
- • Félix Garza ZC, Liebmann J, Born M, Hilbers PA, van Riel NA. (2019) In silico clinical studies on the efficacy of blue light for treating psoriasis in virtual patients. Systems Medicine, in press.
- • Sips FLP, Eggink HM, Hilbers PAJ, Soeters MR, Groen AK, van Riel NA. (2018) In silico analysis identifies intestinal transit as a key determinant of systemic bile acid metabolism. Front Physiol. 9:631.
- Rozendaal, Y. J. et al. Model-based analysis of postprandial glycemic response dynamics for different types of food. Clinical Nutrition Experimental 19, 32–45 (2018).
- • Rozendaal YJW, Wang Y, Paalvast Y, Tambyrajah LL, Li Z, Willems van Dijk K, Rensen PCN, Kuivenhoven JA, Groen AK, Hilbers PAJ, van Riel NAW. (2018) In vivo and in silico dynamics of the development of Metabolic Syndrome. PLoS Comput Biol. 14(6):e1006145.
- • Sips FL, Nyman E, Adiels M, Hilbers PA, Strålfors P, van Riel NA, Cedersund G. (2015) Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State. PLoS One, 10(9):e0135665.
- Eilers W, Gevers W, van Overbeek D, de Haan A, Jaspers RT, Hilbers PA, van Riel N, Flück M. (2014) Muscle-type specific autophosphorylation of CaMKII isoforms after paced contractions. Biomed Res Int. 2014:943806. http://www.ncbi.nlm.nih.gov/pubmed/25054156
- Heisen M, Fan X, Buurman J, van Riel NA, Karczmar GS, ter Haar Romeny BM. (2010) The influence of temporal resolution in determining pharmacokinetic parameters from DCE-MRI data. Magn Reson Med. 63(3):811-6. http://www.ncbi.nlm.nih.gov/pubmed/20187187
- Groenendaal W, von Basum G, Schmidt KA, Hilbers PA, van Riel NA. (2010) Quantifying the composition of human skin for glucose sensor development. J Diabetes Sci Technol. 4(5):1032-40. http://www.ncbi.nlm.nih.gov/pubmed/20920423
- Jaspers K, Aerts HJ, Leiner T, Oostendorp M, van Riel NA, Post MJ, Backes WH. (2009) Reliability of pharmacokinetic parameters: small vs. medium-sized contrast agents. Magn Reson Med. 62(3):779-87 http://www.ncbi.nlm.nih.gov/pubmed/19623622
- Groenendaal W, Schmidt KA, von Basum G, van Riel NA, Hilbers PA. (2008) Modeling glucose and water dynamics in human skin. Diabetes Technol Ther. 10(4):283-93. http://www.ncbi.nlm.nih.gov/pubmed/18715202
- Netherlands Bioinformatics and Systems Biology Research School (BioSB, http://biosb.nl/)
- Institute for Programming research and Algorithmics (IPA, http://www.win.tue.nl/ipa). IPA is a KNAW recognised national inter‐university research school.
- RESOLVE (http://www.resolve‐diabetes.org). RESOLVE is a flagship project of the FP7 Health programme on Systems Medicine (Systems medicine: Applying systems biology approaches for understanding multifactorial human diseases and their comorbidities). Dr. van Riel is coordinator of the computational modelling in this project. ADAPT is a key technology for modelling and data integration.
- COST Action MouseAGE: Development of a European network for preclinical testing of interventions in mouse models of age and age-related diseases. (http://www.cost.eu/domains_actions/bmbs/Actions/BM1402)
- postdocs (fte): 3
- dedicated technicians: 1
- IT-specialists: 1
- software: Matlab, Python, R; hardware: high performance computing, CPU clusters, GPU
models: BioModels Database, software: GitHub, data: 4TU.Datacentrum and dedicated proprietary databases