Modelling and Efficient Characterization of Enzyme-Mediated Response to Antibiotic Treatments

Informations générales
Détails de la thèse/HDR
Rosalind Allen, Edinburgh University
Tobias Bollenbach, Cologne University
Marie Doumic, Inria, CNRS & Sorbonne University
Philippe Glaser, Institut Pasteur
Lingchong You, Duke University
Directeur (pour les thèses)
Grégory Batt
Résumé en anglais
Antibiotic resistance is widely recognized as one of the biggest
threats to global health.

In hospitals, the susceptibility of a strain to an antibiotic is
quantified by its Minimum Inhibitory Concentration (MIC): the minimal
concentration of antibiotic necessary to inhibit the growth of
the strain during 24 hours. This value plays a central role for
treatment decisions.

However, the MIC is a measure relying on a unique timepoint. Could
we get a more informative assessment of antibiotic resistance by
exploiting the whole growth curve, observed by optical density?
This information could be available in a clinical context, which
is a requirement of the approach. The problem is complex, notably
because β-lactam antibiotics induce cell filamentation, which
decorrelates the optical density from the number of live cells.

In this thesis, we build a mathematical model of the response of
bacterial populations to β-lactams, encompassing the different
kinds of antibiotic resistance under a unifying framework. Bridging
the three scales: molecular-, cell-, and population-level, this
model provides simultaneous predictions of the optical density and
the number of cells. Its core is a growth-fragmentation equation:
a partial differential equation that considers explicitly the
distribution of cell lengths. The PDE model is not very practical for
numerical optimization, notably for parameter inference. Therefore,
we describe the passage to a companion ODE model for efficient

After calibrating this model on a library of clinical isolates with
the help of a custom driver allowing the programmable use of a
commercial plate reader, we show that we can relate several parameters
to the antibiotic resistance genes and mutations present in the
strains. We then propose a method to cluster the strains despite the
presence of unidentifiable parameters, and show that three classes
emerge: sensitive, tolerant/resilient, and resistant strains. In
comparison with the classical system susceptible, intermediate, and
resistant, these classes provide a richer explanation of the behaviour
of the isolates, and allow a direct exploitation for treatment