Rasmus Ågren

Rasmus Ågren NBIS expert

omics integration, reproducible research, metabolism, systems biology

emailrasmus.agren@scilifelab.se
phone +46 (0)70 2826229

Rasmus has a PhD in Systems biology from Chalmers University of Technology. During his graduate studies he focused on developing methods for building metabolic networks and using these networks as scaffolds for analysis of omics data, with the objective of understanding global responses to nutrient and disease. He then went on to do a postdoc in the pharmaceutical industry, where he worked on modelling of heterologous protein expression. Rasmus joined the NBIS long-term support facility during 2015 and is primarily part of the integrative group, which focuses on projects involving integration of several types of data. He’s experienced in various modelling and optimization approaches, algorithmics, metabolism, and isn’t totally lost in a wet lab.

Selected publications

Agren, R., Mardinoglu, A., Asplund, A., Kampf, C., Uhlen, M., and Nielsen, J. (2014) Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Molecular systems biology, 10, 721.

Agren, R., Bordel, S., Mardinoglu, A., Pornputtapong, N., Nookaew, I., and Nielsen, J. (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol, 8, e1002518.

Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Uhlen, M., and Nielsen, J. (2014) Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nature communications, 5.

Agren, R., Liu, L., Shoaie, S., Vongsangnak, W., Nookaew, I., and Nielsen, J. (2013) The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum. PLOS Computational Biology, 9, e1002980.

Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., and others (2013) A community-driven global reconstruction of human metabolism. Nature biotechnology, 31, 419–425.