Modélisation et caractérisation efficace de la réponse bactérienne aux antibiotiques

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 calibration.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 optimization.La résistance aux antibiotiques est connue comme l'un des plus grands dangers de santé publique. Dans les hôpitaux, la susceptibilité d'une souche à un antibiotique est quantifiée par sa Concentration Minimale Inhibitrice (CMI) : la dose minimale d'antibiotique nécessaire pour inhiber la croissance de la souche pendant 24 heures. Cette valeur joue un rôle central dans les décisions de traitements.Or, la CMI est une mesure reposant sur un unique point de temps. Pourrait-on obtenir une évaluation plus informative de la résistance d'une souche, en exploitant sa courbe de croissance entière, observée par densité optique (DO) ? Cette donnée pourrait être disponible dans un contexte clinique, ce qui est nécessaire pour la pertinence de l'approche. Le problème est complexe, notamment parce que les antibiotiques β-lactames provoquent la filamentation des cellules, ce qui décorrèle la DO du nombre de cellules vivantes.Dans cette thèse, nous développons un modèle mathématique de la réponse de populations bactériennes à des β-lactames, qui rassemble les différents types de résistance. Unifiant les échelles moléculaire, de la cellule et de la population, ce modèle offre des prédictions simultanées de la DO et du nombre de cellules. Son cœur est un modèle de croissance-fragmentation : une équation aux dérivées partielles considérant explicitement la distribution des tailles des cellules. Or, le modèle à EDP n'est pas idéal pour l'optimisation numérique, et notamment pour l'inférence de paramètres. Nous décrivons donc le passage à un modèle compagnon à équations différentielles ordinaires, pour une calibration efficace.Après calibration de ce modèle sur un ensemble d'isolats cliniques à l'aide d'un pilote sur mesure permettant l'automatisation d'un lecteur de plaques, nous montrons que nous pouvons relier plusieurs paramètres du modèle aux gènes et mutations contribuant à la résistance des souches aux antibiotiques. Nous proposons ensuite une méthode permettant de catégoriser les souches, en dépit de la présence de paramètres non identifiables, et observons l'émergence de trois classes : les souches sensibles, les souches tolérantes et résilientes, et les résistantes. En comparaison avec le système classique définissant les souches susceptibles, intermédiaire, et résistantes, ces classes fournissent une explication plus riche du comportement des isolats, et offrent un débouché direct sur l'optimisation de traitements

A model-based approach to characterize enzyme-mediated response to antibiotic treatments: going beyond the SIR classification

Posté dans BioRxiv le 17 juillet 2021To design appropriate treatments, one must be able to characterize accurately the response of bacteria to antibiotics. When exposed to β-lactam treatments, bacteria can be resistant and/or tolerant, and populations can exhibit resilience. Disentangling these phenomena is challenging and no consolidated understanding has been proposed so far. Because these responses involve processes happening at several levels, including the molecular level (e.g. antibiotic degradation), the cell physiology level (filamentation) and the population level (release of β-lactamases into the environment), quantitative modelling approaches are needed. Here, we propose a model of bacterial response to β-lactam treatments that accounts for bacterial resistance, tolerance, and population resilience. Our model can be calibrated solely based on optical density readouts, can predict the inoculum effect, and leads to a mechanistically relevant classification of bacterial response to treatments that goes beyond the classical susceptible / intermediate / resistant classification. Filamentation-mediated tolerance and collective enzyme-mediated antibiotic degradation are essential model features to explain the complex observed response of cell populations to antibiotic treatments

Optimal control of an artificial microbial differentiation system for protein bioproductioń

International audienceThe production of recombinant proteins is a problem of significant interest in bioengineering. Because of the existing trade-off between cellular growth and protein production, these two processes are separated in time in most commonly-employed strategies: a growth phase is followed by a production phase. Here, we investigate the potential of an alternative strategy using artificial cell specialization and differentiation systems in which cells either grow ("growers") or produce proteins ("producers") and growers can irreversibly "differentiate" into producers. Inspired by an existing two-population system implemented in yeast, we propose a model of a "yeast synthetic stem cell system" and define an optimal control problem to maximize bioproduction. Analytically, we first establish the well-posedness of the problem. Then, we prove the existence of an optimal control and derive non trivial optimality conditions. We finally use these results to find numerical optimal solutions. We conclude by a discussion of extensions of this work to models that capture the heterogeneity of the cell response to differentiation signals

Applying ecological resistance and resilience to dissect bacterial antibiotic responses

International audienceAn essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a notable change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, although their interpretations are often subject to debate. Here, we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended-spectrum b-lactamases (ESBLs) when treated by a b-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a b-lactam and a b-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor

pymc-devs/pymc: v5.8.2

What's Changed Bugfixes Fix bug in compute_log_likelihood when variable has dims without coords by @jaharvey8 in https://github.com/pymc-devs/pymc/pull/6882 Full Changelog: https://github.com/pymc-devs/pymc/compare/v5.8.1...v5.8.

pymc-devs/pymc: v5.9.1

<p><!-- Release notes generated using configuration in .github/release.yml at main --></p> <h2>What's Changed</h2> <h3>New Features </h3> <ul> <li>Allow batched parameters in MvNormal and MvStudentT distributions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6897</li> <li>Logprob derivation of Max for Discrete IID distributions by @Dhruvanshu-Joshi in https://github.com/pymc-devs/pymc/pull/6790</li> <li>Support logp derivation of <code>power(base, rv)</code> by @LukeLB in https://github.com/pymc-devs/pymc/pull/6962</li> </ul> <h3>Bugfixes </h3> <ul> <li>Make <code>Model.str_repr</code> robust to variables without monkey-patch by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6942</li> <li>Fix bug in GP Periodic and WrappedPeriodic kernel full method by @lucianopaz in https://github.com/pymc-devs/pymc/pull/6952</li> <li>Fix rejection-based truncation of scalar variables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6923</li> </ul> <h3>Documentation </h3> <ul> <li>Add expression for NegativeBinomial variance by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6957</li> </ul> <h3>Maintenance </h3> <ul> <li>Add constant and observed data to nutpie idata by @Y0dler in https://github.com/pymc-devs/pymc/pull/6943</li> <li>Improve multinomial moment by @aerubanov in https://github.com/pymc-devs/pymc/pull/6933</li> <li>Fix HurdleLogNormal Docstring by @amcadie in https://github.com/pymc-devs/pymc/pull/6958</li> <li>Use numpy testing utilities instead of custom close_to* by @erik-werner in https://github.com/pymc-devs/pymc/pull/6961</li> <li>Include more PyTensor functions in math module by @jaharvey8 in https://github.com/pymc-devs/pymc/pull/6956</li> <li>Improve blackjax sampling integration by @junpenglao in https://github.com/pymc-devs/pymc/pull/6963</li> </ul> <h2>New Contributors</h2> <ul> <li>@Y0dler made their first contribution in https://github.com/pymc-devs/pymc/pull/6943</li> <li>@amcadie made their first contribution in https://github.com/pymc-devs/pymc/pull/6958</li> <li>@erik-werner made their first contribution in https://github.com/pymc-devs/pymc/pull/6961</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc/compare/v5.9.0...v5.9.1</p&gt

pymc-devs/pymc: v5.9.2

<p><!-- Release notes generated using configuration in .github/release.yml at main --></p> <h2>What's Changed</h2> <h3>New Features </h3> <ul> <li>Recognize alternative form of sigmoid in logprob inference by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6978</li> <li>Allow IntervalTransform to handle dynamic infinite bounds by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/7001</li> </ul> <h3>Bugfixes </h3> <ul> <li>Fix compute_test_value error when creating observed variables by @vandalt in https://github.com/pymc-devs/pymc/pull/6982</li> <li>Fix memory leak in logp of transformed variables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6991</li> </ul> <h3>Documentation </h3> <ul> <li>fix typo in notebook about Distribution Dimensionality by @nicrie in https://github.com/pymc-devs/pymc/pull/7005</li> </ul> <h3>Maintenance </h3> <ul> <li>Add more missing functions to math module by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6979</li> </ul> <h2>New Contributors</h2> <ul> <li>@vandalt made their first contribution in https://github.com/pymc-devs/pymc/pull/6982</li> <li>@nicrie made their first contribution in https://github.com/pymc-devs/pymc/pull/7005</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/pymc-devs/pymc/compare/v5.9.1...v5.9.2</p&gt

pymc-devs/pymc: v5.8.1

What's Changed New Features Logprob derivation for Min of continuous IID variables by @Dhruvanshu-Joshi in https://github.com/pymc-devs/pymc/pull/6846 Derive logprob for exp2, log2, log10, log1p, expm1, log1mexp, log1pexp (softplus), and sigmoid transformations by @LukeLB in https://github.com/pymc-devs/pymc/pull/6826 ### Bugfixes Fix wrong ZeroSumNormal logp expression by @lucianopaz in https://github.com/pymc-devs/pymc/pull/6872 Fix bug in univariate Ordered and SumTo1 transform logp by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6903 ### Documentation Link to updated PyMC port of DBDA in README by @cluhmann in https://github.com/pymc-devs/pymc/pull/6890 ### Maintenance Reject logp derivation of binary operations with broadcasted measurable input by @shreyas3156 in https://github.com/pymc-devs/pymc/pull/6893 Cast ZeroSumNormal shape operations to config.floatX by @thomasjpfan in https://github.com/pymc-devs/pymc/pull/6889 Bump docker/build-push-action from 4.1.1 to 4.2.1 by @dependabot in https://github.com/pymc-devs/pymc/pull/6900 Bump pytensor by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6910 Full Changelog: https://github.com/pymc-devs/pymc/compare/v5.8.0...v5.8.