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Overview

Project schedule:

Monday:

  • preparation for the projects to check Python software requirements

we will fit Hill models to dosage-response curves of E. coli under streptomycin challenge.

Tuesday:

  • crash course on bacterial growth
  • crash course on bacterial growth data

we will fit Hill functions to dosage-response curves of E. coli mutants selected under streptomycin challenge and study correlations in the data. How far can we go without a mechanistic model of dosage-response?

  • mechanistic models of growth response for ribosome-targeting drugs I

we will build a mechanistic model of growth inhibition by ribosome-targeting drugs and study its properties

Wednesday:

  • mechanistic models of growth response for ribosome-targeting drugs II

we will fit the mechanistic model of growth inhibition by ribosome-targeting drugs to the dosage-response curves of E. coli mutants selected under streptomycin challenge and study correlations in the data

we will build fitness models for different resistance mechanisms and we will predict the dosage-response curves of families of mutants with common mechanisms

Thursday:

  • phase diagram for resistance mechanisms

we will compare the growth of mutants with different resistance mechanisms, and we will study the growth conditions favoring different mechanisms

  • preparation for the presentation

References:

Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z., & Hwa, T. (2010). Interdependence of cell growth and gene expression: origins and consequences. Science, 330(6007), 1099-1102.

Greulich, P., Scott, M., Evans, M. R., & Allen, R. J. (2015). Growth‐dependent bacterial susceptibility to ribosome‐targeting antibiotics. Molecular systems biology, 11(3), 796.

Pinheiro, F., Warsi, O., Andersson, D. I., & Lässig, M. (2021). Metabolic fitness landscapes predict the evolution of antibiotic resistance. Nature Ecology & Evolution, 5(5), 677-687.