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
CWI and VU develop algorithms for pharmaceutical research
In their search for new drugs, researchers increasingly use computer simulations to discover and test new molecules for pharmaceutical use. This does not only increase the chance of finding new or better drugs, but also prevents wasting resources on unsuccessful experiments.
New network model can learn to remember past events
Scientists understand how neurons, the smallest computational units of the brain, behave during tasks, but how brains learn to make efficient choices is unknown, in particular when the brain’s working memory is involved.
CWI to improve ICT for radiotherapy
CWI launches a research project with the Academic Medical Center (AMC) and the company Elekta Brachytherapy to improve the ICT that is used in radiotherapy for cancer patients. In the next years, researchers of these organizations will investigate the improvement of medical software and instruments that are used for internal radiation.
New mathematical models predict success rate of drug trials
A large part of drug trials fail when it turns out that the results for laboratory animals do not transfer to humans (1). New mathematical models developed at Centrum Wiskunde & Informatica (CWI) are able to predict the transferability of results between different organisms.
Members
Associated Members
Publications
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Luong, N.H. (2018, October 17). Design and application of scalable evolutionary algorithms in electricity distribution network expansion planning.
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Aalvanger, G.H, Luong, N.H, Bosman, P.A.N, & Thierens, D. (2018). Heuristics in permutation GOMEA for solving the permutation flowshop scheduling problem. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 146–157). doi:10.1007/978-3-319-99253-2_12
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Luong, N.H, La Poutré, J.A, & Bosman, P.A.N. (2018). Exploiting linkage information and problem-specific knowledge in evolutionary distribution network expansion planning. Evolutionary Computation, 26(3), 471–505. doi:10.1162/EVCO_a_00209
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Thierens, D, & Bosman, P.A.N. (2018). Model-based evolutionary algorithms: GECCO 2018 tutorial. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 553–583). doi:10.1145/3205651.3207874
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van der Meer, M.C, Alderliesten, T, Pieters, B.R, Bel, A, Niatsetski, Y, & Bosman, P.A.N. (2018). Better and faster catheter position optimization in HDR brachytherapy for prostate cancer using multi-objective real-valued GOMEA. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1387–1394). doi:10.1145/3205455.3205505
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Maree, S.C, Thierens, D, Alderliesten, T, & Bosman, P.A.N. (2018). Real-valued evolutionary multi-modal optimization driven by hill-valley clustering. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 857–864). doi:10.1145/3205455.3205477
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Virgolin, M, Alderliesten, T, Bel, A, Witteveen, C, & Bosman, P.A.N. (2018). Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1395–1402). doi:10.1145/3205455.3205604
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Bosman, T, van Ee, M, Jiao, Y, Marchetti Spaccamela, A, Ravi, R, & Stougie, L. (2018). Approximation algorithms for replenishment problems with fixed turnover times. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 217–230). doi:10.1007/978-3-319-77404-6_17
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Feuerstein, E, Marchetti Spaccamela, A, Schalekamp, F, Sitters, R.A, van der Ster, S.L, Stougie, L, & van Zuylen, A. (2017). Minimizing worst-case and average-case makespan over scenarios. Journal of Scheduling, 20(6), 545–555. doi:10.1007/s10951-016-0484-y
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Szabó, A, & Merks, R.M.H. (2017). Blood vessel tortuosity selects against evolution of aggressive cells in confined tissue environments: A modeling approach. PLoS Computational Biology, 13(7). doi:10.1371/journal.pcbi.1005635
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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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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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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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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Identificatie van homogene subgroepen bij mensen met een chronische ziekte, die ernstig vermoeid zijn, door middel van een persoonsgerichte studie opzet. (Beter Gezond)
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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)
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