Pro-/Seminar: Bacterial Resistance


Prof. Dr. Volkhard Helms


Kerstin Reuter, Ruslan Akulenko, Duy Nguyen, Daria Gaidar

Maximum number of participants:



block seminar, September 24, 25, 29 and 30, 1:00 pm – 5:00 pm, building E2 1, room 007

Preliminary meeting and placement of the topics:

Monday, July 20, 12:30 pm. E2.1, room 106


Knowledge corresponding to semester 4 (proseminar) or knowledge corresponding to BSc degree (seminar)

Conditions for certification:

Successful presentation, regular participation (at least 60% of talks)


5 (proseminar) or 7 (seminar)

Additional Information:

If you want us to print your handouts, please send them to Kerstin Gronow-Pudelek until 10 am on the day of your talk (for those who give their talk on Friday, please send the handouts also until Thursday).

Important hints for your seminar presentation.

Timetable for the talks:

Thursday, Sept. 24 Friday, Sept. 25 Tuesday, Sept. 29
Topic 10 Topic 3 Topic 2
Topic 11 Topic 6
Topic 16
Topic 15
Topic 14
Topic 9
Topic 13
Topic 12
Topic 4



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  2. Laabei, M, Recker, M, Rudkin, K, et al. (2014) Genome Research, doi:10.1101/gr.165415.113. Predicting the virulence of MRSA from its genome sequence.
  3. Kelsic, ED, Zhao, J, Vetsigian, K, and Kishony, R (2015) Nature, doi:10.1038/nature14485. Counteraction of antibiotic production and degradation stabilizes microbial communities.
  4. Lázár, V, Nagy, I, Spohn, R, et al., (2014) Nature Communications, DOI: 10.1038/ncomms5352. Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network.
  5. Eldholm, V, Monteserin, J, Rieux, A, et al. (2015) Nature Communications, doi: 10.1038/ncomms8119. Four decades of transmission of a multidrug-resistant Mycobacterium tuberculosis outbreak strain.
  6. Reeve, SM, Gainza, P, Frey, KM, et al. (2015) PNAS, 112, 749-754, doi: 10.1073/pnas.1411548112. Protein design algorithms predict viable resistance to an experimental antifolate.
  7. Tan, SY, Chua, SL, Chen, Y, et al.5(2012) Antimicrobial Agents and Chemotherapy, 57, 5629-5641. Identification of five structurally unrelated quorum-sensing inhibitors of pseudomonas aeruginosa from a natural-derivative database.
  8. Sousa, A, Magalhaes, S, and Gordo, I (2011) Molecular Biology and Evolution 29, 1417-1428. Cost of Antibiotic Resistance and the Geometry of Adaptation.
  9. Flandrois, J-P, Lina, G, and Dumitrescu, O (2014) BMC Bioinformatics 15:107. MUBII-TB-DB: a database of mutations associated with antibiotic resistance in Mycobacterium tuberculosis.
  10. Melnyk, AH, Wong, A, and Kassen, R (2010) Evolutionary Applications, doi:10.1111/eva.121966. The fitness costs of antibiotic resistance mutations.
  11. Novais, A, Comas, I, Baquero, F, et al. (2010) PLoS Pathogens, 6, e1000735. Evolutionary trajectories of beta-lactamase CTX-M-1 cluster enzymes: Predicting antibiotic resistance.
  12. Ghosh, TS, Gupta, SS, Nair, GB, and Mande, SS (2013) PLoS ONE, 8, e83823. In silico analysis of antibiotic resistance genes in the gut microflora of individuals from diverse geographies and age-groups.
  13. Coll, F, McNemey, R, Preston, MD, et al. (2015) Genome Medicine, 7:51, doi 10.1186/s13073-015-0164-0. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences.
  14. Schenk, MF, Szendro, IG, Salverda, MLM, et al. (2013) Molecular Biology and Evolution, 30, 1779-1787, doi:10.1093/molbev/mst096. Patterns of epistasis between beneficial mutations in an antibiotic resistance gene.
  15. Li, J, Qu, X, He, X, et al (2012) PLoS ONE, 7, e45878, doi:10.1371/journal.pone.0045878. ThioFinder: A web-based tool for the identification of thiopeptide gene clusters in DNA sequences.
  16. Annapoorani, A, Umamageswaran, V, Parameswari, R, et al. (2012) J Comput Aided Mol Des, doi 10.1007/s10822-012-9599-1. Computational discovery of putative quorum sensing inhibitors against LasR and RhlR receptor proteins of Pseudomonas aeruginosa.