Biology subjects

Zwep, L. B.

Publications and source records attributed to Zwep, L. B..

4 recordsLinked to original sources

bioRxiv2026

Collateral effects of bacteriophage resistance on antibiotic sensitivity in Pseudomonas aeruginosa

Collateral effect (CEs) of phage resistance can alter antibiotic sensitivity, leading to collateral sensitivity (CS). CS of phage resistance can potentially be used to design more effective phage--antibiotic combination treatments, yet insufficient knowledge on the occurrence of such phage resistance associated CS is currently available. Here, we characterized CEs across diverse Pseudomonas aeruginosa strains, antibiotics, and phages. For each of eight strains (seven clinical isolates and PAO1), we generated mutants resistant to phage PAO1-1 and, separately, mutants resistant to each of six antibiotics. Phage-resistant mutants were also generated using four additional phages (M32, NP1, NP3, and PNM), and double phage- and antibiotic-resistant mutants from one strain. We measured growth curves across antibiotic concentration ranges after which the area-under-the-curve (AUC) was quantified as a measure of bacterial burden. Dose-response curves for AUC against antibiotic concentrations were constructed to estimate a fold change in EC50 of mutants relative to their parental strains. PAO1-1-resistant mutants showed significant CS to colistin and polymyxin B. For other antibiotics, CEs varied by phage and parental strain: PAO1-1-resistant mutants showed CS to tobramycin, whereas mutants resistant to the other four phages tested were associated with CR. For double phage- and antibiotic resistant mutants we found CS to colistin and tobramycin relative to the parental strain. Resistance mutations occurred predominantly in genes involved in metabolism, virulence, and DNA replication. Antibiotic-resistant mutants also showed increased phage sensitivity, with the strongest effect in colistin-resistant mutants. In conclusion this study demonstrates collateral effects of phage resistance are most specific to both strain- and antibiotic-specific.

pharmacology and toxicology↗

bioRxiv2026

From exact gradients to exact standard errors: third-order sensitivity equations for the FOCE and FOCEI population likelihood

Asymptotic standard errors for the parameters of a nonlinear mixed-effects model fitted by first-order conditional estimation (FOCE) or FOCE with interaction (FOCEI) require the observed (Fisher) information -- the negative second derivative of the population objective at the optimum. The gradient of this objective can be computed exactly from sensitivity equations, but the observed information is conventionally still formed by finite differencing, which is less accurate and step-size dependent. Our objectives are to (i) derive the FOCE and FOCEI observed information in closed form within the same sensitivity-equation framework, and (ii) quantify the precision this recovers. Writing the objective as a data term plus the log-determinant of the first-order inner Hessian, the population Hessian splits so that the data term reuses the second-order sensitivities already needed for the gradient, whereas the log-determinant term requires third-order sensitivity equations -- confining the third-order dependence to a single term, where it enters in exactly two places. A finite-difference error analysis shows the differenced Hessian attains an accuracy no better than the square root of the objectives evaluation accuracy, whereas the analytic form is limited only by the sensitivity and differential-equation solutions, with no step size to tune. We illustrate this on a one-compartment oral model with first-order absorption fitted to warfarin data, where the differenced standard error is usable only over a narrow band of step sizes while the analytic value carries none. Implemented in the open-source R package nlmixr2, the method makes exact, reproducible standard errors routine, supporting more dependable confidence intervals, identifiability assessment, and uncertainty propagation.

pharmacology and toxicology↗

bioRxiv2026

Topological Investigation of Protein Folding and Intrinsic Disorder

Mapping protein conformations into a space of fold topologies offers an unprecedented perspective on the long-standing protein folding problem. In this study, we apply circuit topology to investigate the folding landscape of both stably folded and intrinsically disordered proteins. This topological approach quantifies intra-chain contact arrangements within a polypeptide chain. We demonstrate that ordered and disordered proteins can be distinguished by their topological organization, and that a topology-based model can predict chain compaction and folding state. Furthermore, topology relates to folding and unfolding kinetics and thermodynamics. These findings establish topology as a fundamental concept for understanding protein folding and disorder.

biophysics↗

bioRxiv2025

Aggregate data modelling: a fast implementation for fitting pharmacometrics models to summary-level data in R

Pharmacometric modelling is traditionally performed using individual level data. Recently a new method was developed to fit pharmacometric models to summary level - or aggregate - data. This methodology allows for jointly modelling different data sources, once transformed into aggregate data. As such, the method can be applied to a combination of individual data, pharmacometric models, and aggregate data. In this study we aimed to (1) implement this methodological framework into an accessible R package (admr) and (2) develop a novel algorithm with enhanced computational efficiency. The developed R-package allows calculating aggregate data from different data sources, jointly fitting one or multiple data sources and assessing model performance. The implementation of the newly developed algorithm improves computational efficiency by iteratively reweighting internal Monte Carlo predictions. Three simulation scenarios using different data generating models demonstrated an improvement of 3 to 100-fold speed-up when using the novel Iterative Reweighting Monte Carlo (IR-MC) algorithm, while maintaining the convergence properties of the original MC algorithm. These analyses demonstrated that estimation with the IR-MC algorithm is increasingly more efficient as model complexity rises as compared to the standard MC algorithm, indicating the utility for more complex pharmacometric models. In conclusion, the aggregate data modelling implementation in the admr R package allows for a fast and user-friendly application of the aggregate data modelling framework.

pharmacology and toxicology↗