Bridging energy and ribosomal allocation models to predict the cost of traits in different environments

Many traits are costly because they require the diversion of resources from cell reproduction, however, the effect of environmental conditions and genetic background on the cost of traits is not well understood. Two different frameworks have been proposed to quantify the resource costs of traits, focusing on either energy allocation or ribosome allocation. These frameworks implicitly assume energy provisioning or protein production limits growth, respectively, but the connection between the two limitations has been underexplored. To better connect these frameworks, we reformulate previous models and incorporate the degradation, recycling, and energetic demands for cell maintenance to quantify the cost of traits, depending on the nature of the resources diverted, genetic background, and the environmental conditions experienced. Notably, our model predicts that increasing food quality increases the cost of traits that require the production of new structures, while decreasing the cost of traits requiring energy expenditure. Understanding how environmental change affects the cost of traits has important implications for the evolution of various traits, including antimicrobial resistance. Moreover, the model also accounts for several aspects of the observed relationship between the macromolecular composition of the cells and growth rate (also known as bacterial growth laws). Author SummaryMany traits divert resources from reproduction and are costly due to the physiological trade-offs organisms face. These traits might require energy expenditure or production of macromolecules, and it is unclear how their costs can be compared given their different units. Moreover, the effect of environmental conditions on the cost of traits is not well understood. Here, we develop a framework that considers limitations in energy provisioning and protein production simultaneously and enables us to compare the cost of traits requiring different resources. Using this model, we explore how environmental quality and genetic background affect the cost of traits.