Pro-/Seminar: Modeling of Bacterial Resistance

Lecturer: Prof. Dr. Volkhard Helms

Dates: March 11 – March 15, 12:30 pm – 7:00 pm, Building E2 1, room 007

Place: building E2 1, room 007

Tutors: Ruslan Akulenko, Mohamed Hamed, Po-Hsien Lee, Nadine Schaadt

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

Preliminary discussion and placement of the topics: Friday, February 1, 4:00 pm, E2 1, room 007; the topics are going to be announced one week before.

Condition for certification: successful presentation, regular participation.

Maximum number of participants: to be decided

5 (proseminar) or 7 (seminar)

Important Hints for your seminar presentation.

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).



  1. Austin DJ, Kakehashi M, and Anderson RM (1997) Proc. R. Soc. B, 264, 1629-1638. The transmission dynamics of antibiotic–resistant bacteria: the relationship between resistance in commensal organisms and antibiotic consumption.
  2. Austin DJ, Kristinsson KG, and Anderson RM (1999) PNAS, 96, 1152-1156. The relationship between the volume of antimicrobial consumption in human communities and the frequency of resistance.
  3. Handel A, Regoes RR, and Antia R (2006) PLOS Comp. Biol. 2, 1262-1270. The role of compensatory mutations in the emergence of drug resistance.
  4. Liu B and Pop M (2009) Nucleic Acids Res., 37, D443-D447. ARDB—Antibiotic Resistance Genes Database.
  5. Takatsuka Y,  Chen C, and Nikaido H, (2010) PNAS, 107, 6559-6565; doi:10.1073/pnas.1001460107, Mechanism of recognition of compounds of diverse structures by the multidrug efflux pump AcrB of Escherichia coli
  6. Vargiu, A.V. and Nikaido, H. (2012) PNAS, 109, 20637-20642. Multidrug binding properties of the AcrB efflux pump characterized by molecular dynamics simulations.
  7. Zhao Y, Wu J, Yang J, Sun S, Xiao J, and Yu J (2012) Bioinformatics, 28,416-418. PGAP: Pan-Genomes Analysis Pipeline.
  8. Greulich P, Waclaw B, and Allen RJ (2012) Physical Review Letters, 109, 088101. Mutational pathway determines whether drug gradients accelerate evolution of drug-resistant cells.
  9. Fozard JA, Lees M, King JR, Logan BS (2012) Biosystems 109, 105-114. Inhibition of quorum sensing in a computational biofilm simulation.
  10. Luciani F, Sisson SA, Jiang H, Francis AR, and Tanaka MM (2009) PNAS 106, 14711-14715. The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis.
  11. Kinnings SL, Xie Li, Fung KH, Jackson RM, Xie Lei, Bourne PE (2010) PLoS Comp. Biol., 6, e1000976. The Mycobacterium tuberculosis drugome and Iits polypharmacological implications.
  12. Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, Bourne PE (2009) PLoS Comput. Biol., 5, e1000423. Drug discovery using chemical systems biology: Repositioning the safe medicine comtan to treat multi-drug and extensively drug resistant tuberculosis.
  13. Murphy JT, Walshe R, and Devocelle M (2008) J. Theor. Biol., 254, 284-293. A computational model of antibiotic-resistance mechanisms in Methicillin-Resistant Staphylococcus aureus (MRSA).
  14. Kouyos RD, Abel zur Wiesch P, and Bonhoeffer S (2011) PLoS Pathogens, 7, e1001334. On being the right size: The impact of population size and stochastic effects on the evolution of drug resistance in hospitals and the community.
  15. Wozniak M, Tiuryn J, and Wong L (2012) BMC Genomics, 13:S23. An approach to identifying drug resistance associated mutations in bacterial strains.
  16. Muller A, Mauny F, Talon D, Donnan PT, Harbarth S, and Bertrand X (2006) J. Antimicrobial Chemotherapy, 58, 878-881. Effect of individual- and group-level antibiotic exposure on MRSA isolation: a multilevel analysis.
  17. Goldstein H, Pan H, and Bynner J (2003) Understanding Statistics, 3, 85-99. A Flexible Procedure for Analyzing Longitudinal Event Histories Using a Multilevel Model.
  18. Nielsen KL, Pedersen TM, Udekwu KI, et al. (2012) J. Antimicrobial Chemotherapy, 67, 1325-1332. Fitness cost: a bacteriological explanation for the demise of the first international methicillin-resistant Staphylococcus aureus epidemic.
  19. Ruffing U, Akulenko R, Bischoff M, Helms V, Herrmann M, and von Müller L (2012) PLoS ONE, 7, e52487. Matched-cohort DNA microarray diversity analysis of methicillin sensitive and methicillin resistant Staphylococcus aureus isolates from hospital admission patients.
  20. Blower SM and Chou T (2004) Nature Medicine, 10 (10), 1111-1116, DOI: 10.1038/nm1102.  The emergence of the ‘hot zones’: tuberculosis and the amplification dynamics of drug resistance.
  21. Izu A, Cohen T, Mitnick C, Murray M, De Gruttola V (2011) Stat Med. 30, 2708-20. DOI: 10.1002/sim.4287.
    Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains.
  22. Wakamoto Y, Dhar N, Chait R, Schneider K, Signorino-Gelo F, Leibler S, McKinney JD, Science 2013, 339, 91-95 DOI: 10.1126/science.1229858 Dynamic persistence of antibiotic-stressed mycobacteria.
  23. Kussell E and Leibler S (2005) Science 309, 2075, DOI: 10.1126/science.1114383
    Phenotypic diversity, population growth, and information in fluctuating environments.
  24. Pauling J,  Röttger R, Neuner A,  Salgado H, Collado-Vides J, Kalaghatgi P,  Azevedo V, Tauch A,  Pühler A, and Baumbach J (2012) Integr. Biol., 4, 728-733. On the trail of EHEC/EAEC—unraveling the gene regulatory networks of human pathogenic Escherichia coli bacteria.
  25. Korves T and Colosimo ME (2009) Trends in Microbiology, 17, 279–285, Controlled vocabularies for microbial virulence factors.
  26. Kumar V, Sun P, Vamathevan J, Li Y, Ingraham K, Palmer L, Huang J, and Brown JR (2011) Antimicrob. Agents Chemother, 55:4267-4276; doi:10.1128/AAC.00052-11 Comparative Genomics of Klebsiella pneumoniae Strains with Different Antibiotic Resistance Profiles.
  27. Padiadpu J, Vashisht R, and Chandra N (2010) Systems and Synthetic Biology, 4, 311-322. Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance.
  28. Zheng L-L, Li Y-X, Ding J, Guo X-K, Feng K-Y, et al. (2012) PLoS ONE 7(8): e42517. A Comparison of Computational Methods for Identifying Virulence Factors. DOI:10.1371/journal.pone.0042517
  29. Zhu, W, Zhang, Y, Sinko, W, Hensler, ME, Olson, J, et al. (2013), 110, 123-128. Antibacterial drug leads targeting isoprenoid biosynthesis.
  30. Nübel, U, Nachtnebel, M, Falkenhorst, G, Benzler, J, Hecht, J, et al. () PLoS ONE 8(1): e54898. doi:10.1371/journal.pone.0054898. MRSA transmission on a neonatal intensive care unit: epidemiological and genome-based phylogenetic analyses.
  31. Gladki, A, Kaczanowski, S, Szczesny, P, and Zielenkiewicz, P. (2013) BMC Bioinformatics, 14:36. The evolutionary rate of antibacterial drug targets.
  32. Holden, MTG, Hsu, L-Y, Kurt, K, et al. (2013) Genome Research, doi:10.1101/gr.147710.112. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic.