Predicting the evolution of antibiotic resistance is critical for realizing precision antibiotic therapies. How exactly to achieve such predictions is a theoretical challenge. Insights from mathematical models that reflect future behavior of microbes under antibiotic stress can inform intervention protocols. However, this requires going beyond heuristic approaches by modeling ecological and evolutionary responses linked to metabolic pathways and cellular functions. Developing such models is now becoming possible due to increasing data availability from systematic experiments with microbial systems. Here, I review recent theoretical advances promising building blocks to piece together a predictive theory of antibiotic resistance evolution. I focus on the conceptual framework of eco-evolutionary response models grounded on quantitative laws of bacterial physiology. These forward-looking models can predict previously unknown behavior of bacteria upon antibiotic exposure. With current developments covering mostly the case of ribosome-targeting antibiotics, I write this Opinion piece as an invitation to generalize the principles discussed here to a broader range of drugs and context dependencies.
The bacterial stressosome is a supramolecular multiprotein complex that acts as a critical signal integration and transduction hub, orchestrating cellular responses to environmental stimuli. Recent studies have resolved near-atomic stressosome structures from various bacterial species, revealing assemblies that should be capable of altering their configuration in response to external changes. Further genetic, biochemical, and cell biology research has elucidated interactions and phosphorylation status within the stressosome complex as well as its subcellular localization and mobility within living cells. These insights enhance our comprehension of the stressosome pathways and their roles in directing various survival responses during environmental stress.