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

Mildau, K., Büschl, C., Zanghellini, J., & Van Der Hooft, J. J. J. (2024). Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST. Bioinformatics, 40(10), Article btae584. https://doi.org/10.1093/bioinformatics/btae584

Braun, G., Schaier, M., Werner, P., Theiner, S., Zanghellini, J., Wisgrill, L., Fyhrquist, N., & Koellensperger, G. (2024). MeXpose─A Modular Imaging Pipeline for the Quantitative Assessment of Cellular Metal Bioaccumulation. JACS Au, 4(6), 2197-2210. https://doi.org/10.1021/jacsau.4c00154

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

Mildau, K., Ehlers, H., Oesterle, I., Pristner, M., Warth, B., Doppler, M., Bueschl, C., Zanghellini, J., & van der Hooft, J. J. J. (2024). Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard. Analytical Chemistry, 96(15), 5798-5806. https://doi.org/10.1021/acs.analchem.3c04444

Predl, M., Mießkes, M., Rattei, T., & Zanghellini, J. (2024). PyCoMo: a python package for community metabolic model creation and analysis. Bioinformatics (Oxford, England), 40(4), Article btae153. https://doi.org/10.1093/bioinformatics/btae153

Coltman, B. L., Rebnegger, C., Gasser, B., & Zanghellini, J. (2024). Characterising the metabolic rewiring of extremely slow growing Komagataella phaffii. Microbial Biotechnology, 17(1), Article e14386. https://doi.org/10.1111/1751-7915.14386

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

Gotsmy, M., Strobl, F., Weiß, F., Gruber, P., Kraus, B., Mairhofer, J., & Zanghellini, J. (2023). Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes. Microbial Cell Factories, 22(1), Article 242. https://doi.org/10.1186/s12934-023-02248-2

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

Buchner, B., Clement, T. J., de Groot, D. H., & Zanghellini, J. (2023). ecmtool: fast and memory-efficient enumeration of elementary conversion modes. Bioinformatics (Oxford, England), 39(3), Article btad095. https://doi.org/10.1093/bioinformatics/btad095

El Abiead, Y., Bueschl, C., Panzenboeck, L., Wang, M., Doppler, M., Seidl, B., Zanghellini, J., Dorrestein, P. C., & Koellensperger, G. (2022). Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets. Analytica Chimica Acta, 1229, Article 340352. https://doi.org/10.1016/j.aca.2022.340352

Mildau, K., van der Hooft, J. J. J., Flasch, M., Warth, B., El Abiead, Y., Koellensperger, G., Zanghellini, J., & Bueschl, C. (2022). Homologue Series Detection and Management in LC-MS data with homologueDiscoverer. Bioinformatics, 38(22), 5139-5140. Article btac647. https://doi.org/10.1093/bioinformatics/btac647

Gotsmy, M., Brunmair, J., Bueschl, C., Gerner, C., & Zanghellini, J. (2022). Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements. BMC Bioinformatics, 23(1), Article 379. https://doi.org/10.1186/s12859-022-04918-1

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

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

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