Bridging Models and Mechanisms: Integrating Proteome Remodeling with Antibiotic Response

Abstract

Antibiotic resistance is an urgent challenge in medicine, and treatment outcomes are often moulded not only by genetic resistance but by the physiological adaptation of bacteria under drug exposure. Comprehending these constraints requires integrating how translational capacity, nutrient supply, and global feedback cooperatively determine recovery and survival. This thesis extends and validates a mechanistic model that integrates nutrient-dependent growth laws with dynamic proteome allocation to capture Escherichia coli's response to pulse-dose exposure to the ribosome-targeting antibiotic tetracycline in glucose- and glycerol-based media. Experimental measurements of growth delay times, RNA/protein ratios, and inhibition curves supplied direct physiological targets that guided model refinement, making certain that theory remained connected to reproducible lab data. The modeling effort highlighted two crucial effects that had previously been overlooked. First, following the removal of a ribosome-targeting antibiotic, ribosomes no longer constitute the primary limiter of growth, unlike their role under most steady-state conditions. Second, a proportional feedback controller based on the non-steady-state mismatch in amino acid flux was necessary to capture the rapid timescales of antibiotic adaptation and post-pulse recovery.

The resulting model reconciled all experimental datasets across both carbon sources, reproducing delay time plateaus, RNA/protein recovery dynamics, and inhibition profiles in a physiologically interpretable way. Sensitivity and Hessian analyses showed that recovery dynamics are primarily governed by transport rates and the strength of feedback control, whereas shifts in how binding and transport interact have little influence on the resulting physiological behavior predicted by the model. This contrast showcases which regulatory components are necessary for shaping recovery and which play only a minor, compensatory role. Clinically, the model argues against prolonged dosing, which permits rapid recovery during treatment, and instead supports pulses that maximize growth inhibition for a fixed total amount of antibiotic. Such regimes minimize the time bacteria spend in sub-inhibitory drug concentrations, thus limiting the opportunity for resistant variants to emerge, and provide a quantitative rationale for pulse- and intermittent-dosing strategies that exploit the post-antibiotic effect. More broadly, this work exemplifies how combining experiments with physiologically grounded modeling can illustrate unifying supply–demand principles of bacterial adaptation. Although developed for E. coli and tetracycline, the mathematical modeling framework is readily adaptable to other reversibly binding ribosome-targeting antibiotics, and could possibly be extended to other antibiotic classes, offering a foundation for linking physiology to treatment strategies in diverse microbial environments.