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

tINIT

runINIT/getINITModel (RAVEN's MATLAB implementation of tINIT) build on the INIT method described in the paper below; tINIT has no citation of its own separate from it.

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