Vivian Lu Tan

Dr. Vivian Lu Tan

Department Environmental Toxicology

About Me

Project description
The Envirobot project ( aims to develop an automated system using physical, chemical and biological sensors for assessing water quality in the field in real-time. The project involves seven collaborators specialised in different areas of biology, toxicology and engineering. Vivian's work focuses on developing a whole-cell based system, mainly by using rainbow trout cells on a biochip with electrochemical transducing units, for sensing the overall toxicity.

About Vivian
Vivian Lu Tan received her PhD degree in 2010 at the University of Bonn, Germany. Her PhD research was focused on developing methods to combine structure-based and ligand-based virtual screening approaches. After her PhD, she obtained a Master of Philosophy in Bioscience Enterprise from the University of Cambridge.

From 2011 to 2013, she did post-doctoral research under the supervision of Prof. David Lomas at the Cambridge Institute for Medical Research, University of Cambridge. Her work focused on conformational diseases caused by aggregation of abnormal proteins, in particular the α1-antitrypsin deficiency. She was involved in a collaborative drug discovery programme between University of Cambridge and GSK targeting α1-antitrypsin deficiency. She was also active in research of computational chemistry in collaboration with Unilever Centre of Molecular Sciences Informatics, Dept of Chemistry, University of Cambridge.

In the end of 2013, Vivian joined Prof. Kristin Schirmer's group at eawag (Swiss Federal Institute of Aquatic Science and Technology, ETH-Domain) to work on the development of a whole-cell based biosensor for the Envirobot project.

In addition to her research activities, Vivian also participated in many entrepreneurial initiatives. Together with her team mates they won several prestigious business plan competition prizes for a toolbox of diagnosing infectious diseases and for a novel medical device to treat diabetes (2011-2013).

1. Tan, L., Dickens, J.A., DeMeo, D.L., Miranda, E., Perez, J., Rashid, S.T., Day, J., Ordoñez, A., Marciniak, S.J., Haq, I., Barker, A.F., Campbell, E.J., Eden, E., McElvaney, N.G., Rennard, S.I., Sandhaus, R.A., Stocks, J.M., Stoller, J.K., Strange, C., Turino, G., Rouhani, F.N., Brantly, M., and Lomas, D.A. (2014). Circulating polymer in α1-antitrypsin deficiency. European Respiratory Journal. In press.

2. Tan, L., Kirchmair, J. Computational approaches in metabolism prediction – available software. (2014) In Drug metabolism prediction, Kirchmair, J., Ed. Wiley-VCH: Weinheim, Germany. In press.

3. Xuan, S., Wang, M., Kang, H., Kirchmair, J., Tan, L., Yan, A. Support vector machine (SVM) models for predicting inhibitors of the 3’ processing step of HIV-1 integrase. (2013). Molecular Informatics. 32, 811-826.

4. Ordóñez, A., Snapp, E.L., Tan, L., Miranda, E., Marciniak, S.J., and Lomas, D.A. (2013). Endoplasmic reticulum polymers impair luminal protein mobility and sensitise to cellular stress in α (1) -antitrypsin deficiency. Hepatology. 57, 2049-2060

5. Mak, L., Liggi, S., Tan, L., Kusonmano, K., Rollinger, J.M., Koutsoukas, A., Glen, R.C., and Kirchmair, J. (2013). Anti-cancer Drug Development: Computational Strategies to Identify and Target Proteins Involved in Cancer Metabolism. Curr. Pharm. Des. 19, 532–577.

6. Kirchmair, J., Williamson, M.J., Tyzack, J.D., Tan, L., Bond, P.J., Bender, A., and Glen, R.C. (2012). Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 52, 617–648.

7. Tan, L., Batista, J., and Bajorath, J. (2010). Computational methodologies for compound database searching that utilize experimental protein-ligand interaction information. Chem Biol Drug Des 76, 191–200.

8. Tan, L., Batista, J., and Bajorath, J. (2010). Rationalization of the performance and target dependence of similarity searching incorporating protein-ligand interaction information. J Chem Inf Model 50, 1042–1052.

9. Batista, J., Tan, L., and Bajorath, J. (2010). Atom-centered interacting fragments and similarity search applications. J Chem Inf Model 50, 79–86.

10. Tan, L., Vogt, M., and Bajorath, J. (2009). Three-dimensional protein-ligand interaction scaling of two-dimensional fingerprints. Chem Biol Drug Des 74, 449–456.

11. Tan, L., and Bajorath, J. (2009). Utilizing target-ligand interaction information in fingerprint searching for ligands of related targets. Chem Biol Drug Des 74, 25–32.

12. Tan, L., Lounkine, E., and Bajorath, J. (2008). Similarity searching using fingerprints of molecular fragments involved in protein-ligand interactions. J Chem Inf Model 48, 2308–2312.

13. Tan, L., Geppert, H., Sisay, M.T., Gütschow, M., and Bajorath, J. (2008). Integrating structure- and ligand-based virtual screening: comparison of individual, parallel, and fused molecular docking and similarity search calculations on multiple targets. ChemMedChem 3, 1566–1571.

14. Yamazaki, S., Tan, L*., Mayer, G., Hartig, J.S., Song, J.-N., Reuter, S., Restle, T., Laufer, S.D., Grohmann, D., Kräusslich, H.-G., et al. (2007). Aptamer displacement identifies alternative small-molecule target sites that escape viral resistance. Chem. Biol. 14, 804–812. (*shared first authorship)

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This person does no longer work at Eawag. Please contact for further information.

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