Citations

RAVEN

Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, Nielsen J, Kerkhoven EJ (2018). RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Computational Biology 14(10): e1006541. https://doi.org/10.1371/journal.pcbi.1006541

Agren R, Liu L, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J (2013). The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Computational Biology 9(3): e1002980. https://doi.org/10.1371/journal.pcbi.1002980

GEM reconstruction protocol

Zorrilla F, Kerkhoven EJ (2022). Reconstruction of Genome-Scale Metabolic Model for Hansenula polymorpha Using RAVEN. In: Mapelli V, Bettiga M (eds), Yeast Metabolic Engineering: Methods and Protocols, Methods in Molecular Biology, vol. 2513. Humana, New York, NY, pp. 271–290. https://doi.org/10.1007/978-1-0716-2399-2_16

Context-specific extraction (INIT / ftINIT)

ftINIT

Gustafsson J, Anton M, Roshanzamir F, Jörnsten R, Kerkhoven EJ, Robinson JL, Nielsen J (2023). Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data. Proceedings of the National Academy of Sciences 120(6): e2217868120. https://doi.org/10.1073/pnas.2217868120

Legacy tINIT method

runINIT and getINITModel (the legacy tINIT method of RAVEN in MATLAB, which ftINIT replaces) build on the INIT method described in the following paper. tINIT has no citation of its own.

Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012). Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Computational Biology 8(5): e1002518. https://doi.org/10.1371/journal.pcbi.1002518

Random sampling

Bordel S, Agren R, Nielsen J (2010). Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Computational Biology 6(7): e1000859. https://doi.org/10.1371/journal.pcbi.1000859