Anze Zupanic

Dr. Anze Zupanic

Department Environmental Toxicology

About Me

Head of Systems Biology Group

The Systems Biology group was formed in 2014 and studies how the use of bioinformatics and biologicial network modeling can help predict the effects of environmental stressors on aquatic species and the aquatic ecosystem. 

Research Summary

My current research interest fall within two general themes: determining the molecular mechanism of environmental stress and quantitative prediction of physiologically and ecologically relevant outcomes based on low- and high-throughput molecular assays. A mechanistic understanding of cellular processes that leads from molecular initiating events to an adverse outcome will aid in establishing sets of stress specific biomarkers, leading to cheaper biomarker-based molecular assays that could be used in ecosystems monitoring. Understanding of the toxic mechanisms of two individual stressors will lead to better understanding of the interactions between them and the prediction of synergistic and antagonistic effects. Validated mechanistic toxicological models can be used to quantitatively predict adverse outcome of environmental exposures, based on either biomarker or high-throughput omics measurements. 

Science communication

While excellent and rigorous research is necessary to move science forward, it is not enough to achieve the full impact scientific discoveries should have on our society. Equally important are positive interactions with policy makers, the industry and the general public. In this respect, I have in the past given and organized several public talks, one of which you can see here:  Biological Complexity at TEDxLjubljana. I am also a member of the SETAC Steering Committe on OMICS research in environmental toxicology and Scientific Advisory Board member of the ISGSB. 

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One of the most important challenges in environmental toxicology is to understand how responses to chemical pollutants scale through levels of biological complexity...
The development of mathematical models for prediction of ecological risks posed by environmental chemicals requires good understanding of toxicological mechanisms.
In recent years, OMICS (transcriptomics, proteomics, metabolomics) datasets combined with bioinformatic methods, such as differential gene expression analysis and network inference, have proven very useful for discovering biological networks.

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Curriculum Vitae

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Selected publications

Sgier, L.; Freimann, R.; Zupanic, A.; Kroll, A. (2016) Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics, Nature Communications, 7, 11587 (10 pp.), doi:10.1038/ncomms11587, Institutional Repository
Vriens, B.; Behra, R.; Voegelin, A.; Zupanic, A.; Winkel, L. H. E. (2016) Selenium uptake and methylation by the microalga Chlamydomonas reinhardtii, Environmental Science and Technology, 50(2), 711-720, doi:10.1021/acs.est.5b04169, Institutional Repository
Zupanic, A.; Meplan, C.; Huguenin, G. V. B.; Hesketh, J. E.; Shanley, D. P. (2016) Modeling and gene knockdown to assess the contribution of nonsense-mediated decay, premature termination, and selenocysteine insertion to the selenoprotein hierarchy, RNA, 22(7), 1076-1084, doi:10.1261/rna.055749.115, Institutional Repository
Dolan, D. W. P.; Zupanic, A.; Nelson, G.; Hall, P.; Miwa, S.; Kirkwood, T. B. L.; Shanley, D. P. (2015) Integrated stochastic model of DNA damage repair by non-homologous end joining and p53/p21- mediated early senescence signalling, PLoS Computational Biology, 11(5), 1-19, doi:10.1371/journal.pcbi.1004246, Institutional Repository
Zupanic, A.; Meplan, C.; Grellscheid, S. N.; Mathers, J. C.; Kirkwood, T. B. L.; Hesketh, J. E.; Shanley, D. P. (2014) Detecting translational regulation by change point analysis of ribosome profiling data sets, RNA, 20(10), 1507-1518, doi:10.1261/rna.045286.114, Institutional Repository

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Phone: +41 58 765 5402
Fax: +41 58 765 5802
Address: Eawag
Überlandstrasse 133
8600 Dübendorf
Office: BU E04

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Research Group

Systems Biology

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Research Focus

Systems Biology, Computational Biology, Dynamic modeling, Metabolic Modeling, Network Analyis

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