Complex Evolution of Resistance in Spatially Structured Populations

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For many naturally occurring populations, ranging from microbial biofilms to solid tumors, it has been argued that their spatial organization can drastically influence their evolutionary dynamics, and hence the establishment probability of new mutants. This is especially relevant in the context of adaptation to deteriorating environments, such as antibiotic or chemotherapeutic attacks, where the emergence of resistant clones can lead to treatment failure.For many naturally occurring populations, ranging from microbial biofilms to solid tumors, it has been argued that their spatial organization can drastically influence their evolutionary dynamics, and hence the establishment probability of mutant clones. This is especially relevant in the context of adaptation to deteriorating environments, such as antibiotic or chemotherapeutic attacks, where the emergence of resistant clones can lead to treatment failure. Despite its argued importance, the role of spatial organization in evolution remains poorly characterized. I here propose a series of experiments investigating the evolutionary dynamics within spatially structured populations in a quantitative manner. To this end, I will develop a collection of new model systems based on yeast strains that can effectively switch between several successive fitness states in a tunable fashion. These engineered mutations, mediated by recombinase action, are also coupled to a change in fluorescence color, enabling precise spatio-temporal tracking of each individual clone via microscopy. Furthermore, employing CRISPR/Cas9 technology will allow me to significantly increase the number of tracked mutation and hence the complexity of investigated scenarios. Using these setups I will study the evolutionary mechanisms leading to the establishment of resistant clones in spatially structured populations and compare them to the well-mixed cultures of cells. I will specifically assess how the intricate interplay of mutation rate, relative fitness and spatial effects result in complex evolutionary trajectories in clinically-relevant scenarios, such as the crossing of fitness valleys.