Description
Leader of the group Life Sciences and Health: Leen Stougie.
Our research group creates fundamental knowledge and applied solutions in the broad field of life sciences. We promote understanding of how biological processes work in detail. Our interdisciplinary team of computer scientists, mathematicians and theoretical biologists develops new models, theories, and decision support systems in collaboration with experimental biologists and medical experts. We are motivated by applications of our work in practice
Watch our group video to get a glimpse of our activities or click here for more information about our group structure.
News
Cells’ own contractions stimulate growth orientation
During the growth of embryos cells align with forces from the surrounding tissue. These forces originate from differences in growth rates, muscle contractions or gravity. Mathematicians at the CWI research centre in Amsterdam now showed for the first time that active cell contractions can accelerate and facilitate this reorientation of cells. Their research was recently published in the leading Biophysical Journal (112).
Shape instructs future fate of cells in Nature Communications
How does a cell make a decision about his future, on what type of cell it will be after the cell division? This process is described by Roeland Merks and Cong Chen of the Centrum Wiskunde & Informatica (CWI) in Amsterdam in an article in Nature Communications that was published on 28 June, together with a Japanese team of biologists.
ERCIM News 104 on Big Data in Life Sciences co-coordinated by Roeland Merks
ERCIM News No. 104 was published in January 2016 at http://ercim-news.ercim.eu/en104
Members
Associated Members
Publications
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Luong, N.H, Alderliesten, T, Pieters, B.R, Bel, A, Niatsetski, Y, & Bosman, P.A.N. (2019). Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy. Applied Soft Computing, 84. doi:10.1016/j.asoc.2019.105681
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Baller, A.C, van Ee, M, Hoogeboom, M, & Stougie, L. (2019). Complexity of inventory routing problems when routing is easy. Networks. doi:10.1002/net.21908
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Lopez Rincon, A, Martinez-Archundia, M, Martinez-Ruiz, G.U, Schönhuth, A, & Tonda, A. (2019). Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection. BMC Bioinformatics, 20(1). doi:10.1186/s12859-019-3050-8
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Barton, C, Kociumaka, T, Liu, C, Pissis, S.P, & Radoszewski, J. (2019). Indexing weighted sequences: Neat and efficient. Information and Computation. doi:10.1016/j.ic.2019.104462
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Dushatskiy, A, Alderliesten, T, Mendrik, A, & Bosman, P.A.N. (2019). Convolutional neural network surrogate-assisted GOMEA. In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 753–761). doi:10.1145/3321707.3321760
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Charalampopoulos, P, Kociumaka, T, Pissis, S.P, Radoszewski, J, Rytter, W, Straszyński, J, … Zuba, W. (2019). Circular pattern matching with k mismatches. In Fundamentals of Computation Theory (pp. 213–228). doi:10.1007/978-3-030-25027-0_15
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Yin, B, Balvert, M, van der Spek, R.A.A, Dutilh, B.E, Bohte, S.M, Veldink, J, & Schönhuth, A. (2019). Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. In Bioinformatics (Vol. 35, pp. i538–i547). doi:10.1093/bioinformatics/btz369
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Pirpinia, K, Bosman, P.A.N, Sonke, J.-J, van Herk, M, & Alderliesten, T. (2019). Evolutionary machine learning for multi-objective class solutions in medical deformable image registration. Special Issue: Evolutionary Algorithms in Health Technologies, 12(5). doi:10.3390/a12050099
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Baaijens, J.A, & Schönhuth, A. (2019). Overlap graph-based generation of haplotigs for diploids and polyploids. Bioinformatics, 35(21), 4281–4289. doi:10.1093/bioinformatics/btz255
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Wang, Z, Virgolin, M, Bosman, P.A.N, Balgobind, B.V, Bel, A, & Alderliesten, T. (2019). Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients. In Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications. doi:10.1117/12.2512758
Software
VirtualLeaf: a modeling framework for plant tissue morphogenesis
VirtualLeaf is a computer modeling framework for the simulation of plant tissue morphogenesis, i.e., the biological development of an organism's shape
Current projects with external funding
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3D dose reconstruction for children with long-term follow-up Toward improved decision making in radiation treatment for children with cancer ()
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Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion ()
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Enhancing protein-drug binding prediction
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ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning
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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
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Multi-Objective Deformable Image Registration (MODIR) – An Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with (MODIR)
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Networks
Related partners
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AMC Medical Research
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Elekta Limited
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Nucletron Operations BV
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Xomnia
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Academisch Medisch Centrum
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Biomedical Imaging Group Rotterdam
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Erasmus Universiteit Rotterdam
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KiKa
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Technische Universiteit Delft
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Universiteit Leiden