| Summary: | Microbes are constantly exposed to a wide range of environmental stresses. To cope, all microbial species have developed protective mechanisms that can safeguard against potential damages due to stress. While stress protection is a beneficial contributor to microbial survival, it also carries a cost. The launch of a stress response diverts cellular resources to the synthesis of energetically expensive stress protectants. Consequently, responding and adapting to stress reduces growth in nutrient-poor environments. Hence the ability to balance allocation of available resources between stress protection and nutritional capacity in response to environmental signals is a fundamental property required for microbial survival with important consequences for species abundance and distribution in nature. This thesis deals with microbial survival strategies and their ecological impacts in two parts: First, we investigate the balance between stress-protection and nutrition as a driver of intra-species evolutionary divergence. Using a simple mathematical model we show that the protection-nutrition balance itself is sufficient to generate diversity within an initially monomorphic microbial population growing in a spatially homogeneous environment containing a single limiting resource. From experimental data we then estimate resource allocation between nutritional and stress resistant properties in glucose-limited E. coli chemostat populations subject to a range of environmental challenges and find that the evolutionary trajectories of multiple types can be predicted using the mathematical model. Second, we investigate the impact of inter-species differential survival strategies on microbial community structure within a Candida infection niche. Focusing on the lifestyles of Candida albicans and Candida glabrata under antifungal stress we build a mathematical model of niche competition within an infection. Calibrating the model using experimental data, we then generate predictions for the long term Candida ecology and find that the model is indeed predictive of equilibrium population distribution within a given infection niche.
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