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Univ.-Prof. Dipl.-Ing. Dr. Jürgen Zanghellini

T: +43-1-4277-52306

juergen.zanghellini@univie.ac.at

https://chemnet.univie.ac.at/

Winter term 2025

270013 SE Writing of the Bachelor´s Thesis

270030 VU Cellular energetics

270103 PR Bachelor Laboratory Course B - Analytical Chemistry and Food Chemistry

270214 PR Data science in metabolomics and proteomics in R

270239 PR Data Science in Metabolomics in R

530037 SE Current topics in multi-omics network analysis

Summer term 2025

270013 SE Writing of the Bachelor´s Thesis

270103 PR Bachelor Laboratory Course B - Analytical Chemistry and Food Chemistry

270190 VU Introduction to metabolic modelling

270191 VU Introduction to R

530037 SE Current topics in multi-omics network analysis

970024 KU univie: summer school Economic Principes in Cell Biology

Winter term 2024

053640 SE Master's Seminar

270013 SE Writing of the Bachelor´s Thesis

270030 VU Cellular energetics

270103 PR Bachelor Laboratory Course B - Analytical Chemistry and Food Chemistry

270214 PR Data science in metabolomics and proteomics in R

270239 PR Data Science in Metabolomics in R

530037 SE Current topics in multi-omics network analysis

Karasová, M., Jobst, M., Framke, D., Bergen, J., Meier-Menches, S., Keppler, B., Koellensperger, G., Zanghellini, J., Gerner, C., & Del Favero, G. (2025). Mechanical cues rewire lipid metabolism and support chemoresistance in epithelial ovarian cancer cell lines OVCAR3 and SKOV3. Cell communication and signaling, 23(1), Article 193. https://doi.org/10.1186/s12964-025-02144-9

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

Graf, A. C., Libiseller-Egger, J., Gotsmy, M., & Zanghellini, J. (2025). Fedbatchdesigner: A User-Friendly Dashboard for Modeling and Optimizing Growth-Arrested Fed-Batch Processes. ACS Synthetic Biology, 14(8), 3252-3257. https://doi.org/10.1021/acssynbio.5c00357

Klamt, S., Zanghellini, J., & Von Kamp, A. (2025). Minimal cut sets in metabolic networks: From conceptual foundations to applications in metabolic engineering and biomedicine. Briefings in bioinformatics, 26(2), Article bbaf188. https://doi.org/10.1093/bib/bbaf188

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

Schlögel, G., Lück, R., Kittler, S., Spadiut, O., Kopp, J., Zanghellini, J., & Gotsmy, M. (2024). Optimizing bioprocessing efficiency with OptFed: Dynamic nonlinear modeling improves product-to-biomass yield. Computational and Structural Biotechnology Journal, 23, 3651-3661. https://doi.org/10.1016/j.csbj.2024.09.024

Gotsmy, M., Erian, A., Marx, H., Pflügl, S., & Zanghellini, J. (2024). Predictive dynamic control accurately maps the design space for 2,3-butanediol production. Computational and Structural Biotechnology Journal, 23, 3850-3858. https://doi.org/10.1016/j.csbj.2024.10.016

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

Gotsmy, M., Giannari, D., Mahadevan, R., & Zanghellini, J. (2024). Optimizing Fed-Batch Processes with Dynamic Control Flux Balance Analysis. IFAC-PapersOnLine, 58(23), 109-114. https://doi.org/10.1016/j.ifacol.2024.10.019

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

Lázaro, J., Júlvez, J., & Zanghellini, J. (2024). Flexible Nets to Improve GEM Cell Factories by Combining Kinetic and Proteomics Data. In R. Gori, P. Milazzo, & M. Tribastone (Eds.), Computational Methods in Systems Biology - 22nd International Conference, CMSB 2024, Proceedings (Vol. 14971, pp. 137-154). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-71671-3_11

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

Chemical crosslinking and quantitative mass spectrometry: Insights beyond distance information

Brandstetter, S. (Speaker), Neuditschko, B. (Contributor), Herzog, F. (Contributor) & Zanghellini, J. (Contributor)

2 Apr 2025