Jonathan Robinson

Jonathan Robinson NBIS expert

systems biology, metabolism, omics integration, RNA-seq, scRNA-seq, metabolic modeling, kinetic modeling
phone +46 (0)739972769

Jonathan received a Ph.D. in Chemical & Biological Engineering from Princeton University under the supervision of Mark Brynildsen, where his research involved constructing and analyzing kinetic models of antimicrobial stress response in bacteria. After completing his Ph.D., Jonathan worked as a postdoctoral researcher with Jens Nielsen at Chalmers University of Technology. His postdoctoral work focused on the analysis of systems-level biological datasets (e.g., transcriptomics, proteomics, and other -omics) to gain a more mechanistic understanding of human disease, often involving integration with biological networks.

Jonathan also led the development of a genome-scale metabolic model (GEM) for human cells, Human-GEM, and was involved in the development of its companion web portal, Metabolic Atlas.

At the start of 2020, Jonathan joined NBIS as part of the bioinformatics long-term support team and the systems biology facility. His expertise includes metabolism, kinetic modeling, genome-scale metabolic modeling, bulk and single-cell transcriptome (RNA-Seq) analysis, and integrative omics analysis. Google Scholar profile

Selected publications

Robinson, J. L., Feizi, A., Uhlén, M., and Nielsen, J. (2019) A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome. Cell Reports, 26, 2622–2635.e5.

Robinson, J. L. and Nielsen, J. (2016) Integrative analysis of human omics data using biomolecular networks. Molecular BioSystems, 12, 2953–2964.

Robinson, J. L. and Brynildsen, M. P. (2016) Discovery and dissection of metabolic oscillations in the microaerobic nitric oxide response network of Escherichia coli. Proceedings of the National Academy of Sciences, 113, E1757–E1766.

Robinson, J. L. and Brynildsen, M. P. (2013) A Kinetic Platform to Determine the Fate of Nitric Oxide in Escherichia coli. PLoS Computational Biology, 9, e1003049.