• Sundqvist, et al. Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors. PLoS Computational Biology, 18(4):e1009999, 2022.


  • Kushnareva, et al. Functional Analysis of Immune Signature Genes in Th1* Memory Cells Links ISOC1 and Pyrimidine Metabolism to IFN-γ and IL-17 Production. The Journal of Immunology
  • Schober, et al. The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters. Science Advances 7:eabf0717, 2021.
  • Ruiz-Perez, et al. Inhibition of fatty acid synthesis induces differentiation and reduces tumor burden in childhood neuroblastoma. iScience 24:102128, 2021.


  • Irena Roci, et al. Mapping choline metabolites in normal and transformed cells. Metabolomics 16:125, 2020.
  • Roland Nilsson. Validity of natural isotope abundance correction for metabolic flux analysis. Mathematical Biosciences 330:108481, 2020.  preprint
  • Irena Roci, et al. Mapping metabolic oscillations during cell cycle progression. Cell Cycle 19:2676-2684, 2020.   preprint
  • Zulkifli, et al. Yeast homologs of human MCUR1 regulate mitochondrial proline metabolism. Nature Communications 11:4866, 2020.
  • Dhanwani, et al. Cellular sensing of extracellular purine nucleosides triggers an innate IFN-β response.  Science Advances  6:eaba3688XX, 2020.


  • Egerstedt, et al. Profiling of the plasma proteome across different stages of human heart failure. Nature Communications 10:5830, 2019.
  • Costas Koufaris, et al. Mitochondrial MTHFD isozymes display distinct expression, regulation, and association with cancer. Gene 7:144032, 2019.
  • Nina Grankvist, et al. Large-Scale Profiling of Cellular Metabolic Activities Using Deep ¹³C Labeling Medium. Methods in Molecular Biology 2088:72-93, 2020.
  • Oliynyk et al. MYCN-enhanced oxidative and glycolytic metabolism reveals vulnerabilities for targeting neuroblastoma. iScience, 21:188-204, 2019.
  • Irena Roci, et al. Mapping metabolic events in the cancer cell cycle reveals arginine catabolism in the committed SG2M phase. Cell Reports 26:P1691-1700.e5, 2019.  LCMS data set


  • Nina Grankvist, et al. Gabapentin can suppress cell proliferation independent of the cytosolic branched-chain amino acid transferase 1 (BCAT1).  Biochemistry 58: 6762-6766, 2018.
  • Nina Grankvist, et al. Profiling the metabolism of human cells by deep ¹³C labeling. Cell Chemical Biology 25:1415-1427, 2018.  Preprint | LCMS data set
  • Costas Koufaris & Roland Nilsson. Protein interaction and functional data indicate MTHFD2 involvement in RNA processing and translation. Cancer & Metabolism 6:12, 2018.
  • Dyczynski, et al. Metabolic reprogramming of acute lymphoblastic leukemia cells in response to glucocorticoid treatment.  Cell Death & Disease 9:846, 2018.


  • Yaroslav Lyutvinskiy, et al. A Web Service Framework for Interactive Analysis of Metabolomics Data. Analytical Chemistry,  89:5713–5718, 2017.
  • Roland Nilsson, et al. Estimation of flux ratios without uptake or release data: application to serine and methionine metabolism. Metabolic Engineering 43:137-146, 2017.
  • Irena Roci, et al. A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing. Journal of Visualized Experiments 120:e55011, 2017.


  • Roland Nilsson and Mohit Jain. Simultaneous tracing of carbon and nitrogen isotopes in human cells. Molecular Biosystems 12:1929-1937, 2016.
  • Irena Roci, et al. Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian CellsAnalytical Chemistry, 88:2707–2713,  2016.

2015 and older

  • Nina Gustafsson Sheppard, et al. The folate-coupled enzyme MTHFD2 is a nuclear protein and promotes cell proliferation. Scientific Reports 5:15029, 2015.
  • Mohit Jain, et al. A systematic survey of lipids across mouse tissues. AJP Endocrinology and Metabolism 306:8, E854-E868, 2014.
  • Roland Nilsson, Mohit Jain, Nikhil Madhusudhan, Nina Gustafsson Sheppard, Laura Strittmatter,et al. Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer. Nature Communications 5:3128, 2014.
  • Sonia Sharma, et al. An siRNA screen for NFAT activation identifies septins as coordinators of store-operated Ca2+ entry. Nature 499:238-242, 2013.
  • Shohreh Maleki, Hanna M Björck, Lasse Folkersen, Roland Nilsson, Johan Renner, et al. Identification of a novel flow-mediated gene expression signature in patients with bicuspid aortic valve. Journal of Molecular Medicine 91:129–139, 2013.
  • Mohit Jain & Roland Nilsson, et al.
    Metabolite Profiling Identifies a Key Role for Glycine in Rapid Cancer Cell Proliferation. Science 336:1040-1044, 2012.
  • Vishal M. Gohil, Roland Nilsson, et al.
    Mitochondrial and nuclear genomic responses to loss of LRPPRC expression. Journal of Biological Chemistry 285:13742–13747, 2010.
  • Vishal M. Gohil & Sunil A. Sheth, Roland Nilsson, Andrew P. Wojtovich, Jeong H. Lee JH, et al.
    Nutrient-sensitized screening for drugs that shift energy metabolism from mitochondrial respiration to glycolysis. Nature Biotechnology 28:249–255, 2010.
  • Joshua M. Baughman, Roland Nilsson, et al. A Computational Screen for Regulators of Oxidative Phosphorylation Implicates SLIRP in Mitochondrial RNA Homeostasis. PLoS Genetics 5:8, 2009.
  • Roland Nilsson & Iman Schulz, Eric L. Pierce, Kathleen A. Soltis, Amornrat Naranuntaratet, et al. Discovery of Genes Essential for Heme Biosynthesis through Large-Scale Gene Expression Analysis. Cell Metabolism 10:2, 119–130, 2009.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. The Journal of Machine Learning Research 10, 1071–1094, 2009.
  • Roland Nilsson, Johan Björkegren and Jesper Tegnér. On reliable discovery of molecular signatures. BMC Bioinformatics 10:38, 2009.
  • Josefin Skogsberg, Andrea Dicker, Mikael Rydén, Gaby Åström, Roland Nilsson, et al. ApoB100-LDL Acts as a Metabolic Signal from Liver to Peripheral Fat Causing Inhibition of Lipolysis in Adipocytes. PLoS ONE 3:11, 2008.
  • Josefin Skogsberg, Jesper Lundström, Alexander Kovacs, Roland Nilsson, Peri Noori, et al. Transcriptional Profiling Uncovers a Network of Cholesterol-Responsive Atherosclerosis Target Genes. PLoS Genetics 4:3, 2008.
  • Alex Kovacs, Per Tornvall, Roland Nilsson, Jesper Tegnér, Anders Hamsten, et al. Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia. PNAS 104:34, 13768-13773, 2007.
  • Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Consistent feature selection for pattern recognition in polynomial time. Journal of Machine Learning Research 8, pp. 589–612, 2007 .
  • Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Detecting multivariate differential expression patterns. BMC Bioinformatics 8:150, 2007.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. Towards scalable and data efficient learning of Markov boundaries. International Journal of Approximate Reasoning 45:2, 211–232, 2007.
  • Jesper Tegnér, Roland Nilsson, Vladimir B. Bajic, Johan Björkegren and Timothy Ravasi. Systems biology of innate immunity. Cellular Immunology 244:2, 105–109, 2006.
  • Roland Nilsson, Vladimir B. Bajic, Harukazu Suzuki, Diego di Bernardo, Johan Björkegren, et al. Transcriptional network dynamics in macrophage activation. Genomics 88:2, pp. 133–42, 2006.
  • Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Evaluating feature selection for SVMs in high dimensions. In proc. of the 17th European Conference on Machine Learning, pp. 719–726, 2006.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. Identifying relevant nodes without learning the model. In proc. of the 22nd Conference on Uncertainty in Artificial Intelligence, pp. 367–374, 2006.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. Reading dependencies from the minimal undirected independence map of a graphoid that satisfies weak transitivity. In proc. of the 3rd European Workshop on Probabilistic Graphical Models, pp. 247–254, 2006.
  • Roland Nilsson, Johan Björkegren and Jesper Tegnér. A flexible implementation for support vector machines. The Mathematica Journal 10:1, pp. 114–127, 2006.
  • Roland Nilsson, Johan Björkegren and Jesper Tegnér. A powerful differential expression test for probe-level oligonucleotide microarray data. In proc. of the 2nd IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 10–14, 2004.