Publications of the group Biochemical Network Analysis
Showing entries 21 - 30 out of 36
2022
Bueschl, C., Doppler, M., Varga, E., Seidl, B., Flasch, M., Warth, B., & Zanghellini, J. (2022). PeakBot: machine-learning-based chromatographic peak picking. Bioinformatics, 38(13), 3422-3428. https://doi.org/10.1093/bioinformatics/btac344
Müller, S., Szeliova, D., & Zanghellini, J. (2022). Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth. PLoS Computational Biology, 18(2), Article e1009843. https://doi.org/10.1371/journal.pcbi.1009843
2021
Buchner, B. A., & Zanghellini, J. (2021). EFMlrs: a Python package for elementary flux mode enumeration via lexicographic reverse search. BMC Bioinformatics, 22(1), Article 547. https://doi.org/10.1186/s12859-021-04417-9
Müller, S., Szeliova, D., & Zanghellini, J. (2021). Elementary vectors and autocatalytic sets for computational models of cellular growth. bioRxiv. https://doi.org/10.1101/2021.10.31.466640
Brunmair, J., Gotsmy, M., Niederstaetter, L., Neuditschko, B., Bileck, A., Slany, A., Feuerstein, M. L., Langbauer, C., Janker, L., Zanghellini, J., Meier-Menches, S. M., & Gerner, C. (2021). Finger sweat analysis enables short interval metabolic biomonitoring in humans. Nature Communications, 12(1), Article 5993. https://doi.org/10.1038/s41467-021-26245-4
Stor, J., Ruckerbauer, D. E., Szeliova, D., Zanghellini, J., & Borth, N. (2021). Towards rational glyco-engineering in CHO: from data to predictive models. Current Opinion in Biotechnology, 71, 9-17. https://doi.org/10.1016/j.copbio.2021.05.003
Herrmann, H. A., Rusz, M., Baier, D., Jakupec, M. A., Keppler, B. K., Berger, W., Koellensperger, G., & Zanghellini, J. (2021). Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer. Cancers, 13(16), Article 4130. https://doi.org/10.3390/cancers13164130
Szeliova, D., Stor, J., Thiel, I., Weinguny, M., Hanscho, M., Lhota, G., Borth, N., Zanghellini, J., Ruckerbauer, D. E., & Rocha, I. (2021). Inclusion of maintenance energy improves the intracellular flux predictions of CHO. PLoS Computational Biology, 17(6), Article e1009022. https://doi.org/10.1371/journal.pcbi.1009022
Széliová, D., Iurashev, D., Ruckerbauer, D. E., Koellensperger, G., Borth, N., Melcher, M., & Zanghellini, J. (2021). Error propagation in constraint-based modeling of Chinese hamster ovary cells. Biotechnology Journal, 16(4), Article 2000320. https://doi.org/10.1002/biot.202000320
2020
Libiseller-Egger, J., Coltman, B., Gerstl, M. P., & Zanghellini, J. (2020). Environmental flexibility does not explain metabolic robustness. Npj systems biology and applications, 6(1), Article 39. https://doi.org/10.1038/s41540-020-00155-5
Showing entries 21 - 30 out of 36