Zehetner, L., Széliová, D., Kraus, B., Hernandez Bort, J. A., & Zanghellini, J. (2025). Multi-omics driven genome-scale metabolic modeling improves viral vector yield in HEK293. Metabolic engineering, 91, 103-118. https://doi.org/10.1016/j.ymben.2025.03.011

Lázaro, J., Joven, T., Széliová, D., Zanghellini, J., & Júlvez, J. (2025). Multi-scale design and optimization of antibody production via flexible nets. Computational and Structural Biotechnology Journal, 27, 1498-1510. https://doi.org/10.1016/j.csbj.2025.03.040

Széliová, D., Müller, S., & Zanghellini, J. (2024). Costs of ribosomal RNA stabilization affect ribosome composition at maximum growth rate. Communications Biology, 7(1), Article 196. https://doi.org/10.1038/s42003-024-05815-4

Zehetner, L., Széliová, D., Kraus, B., Hernandez Bort, J. A., & Zanghellini, J. (2024). Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways. PLoS Computational Biology, 20(6), Article e1012236. https://doi.org/10.1371/journal.pcbi.1012236

Sedlmayr, V. L., Széliová, D., De Kock, V., Gansemans, Y., Van Nieuwerburgh, F., Peeters, E., Quehenberger, J., Zanghellini, J., & Spadiut, O. (2024). Impact of nutrient excess on physiology and metabolism of Sulfolobus acidocaldarius. Frontiers in Microbiology, 15, Article 1475385. https://doi.org/10.3389/fmicb.2024.1475385

Zehetner, L., Széliová, D., Kraus, B., Graninger, M., Zanghellini, J., & Hernandez Bort, J. A. (2023). Optimizing VLP production in gene therapy: Opportunities and challenges for in silico modeling. Biotechnology Journal, 18(7), Article 2200636. https://doi.org/10.1002/biot.202200636

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

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

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