Biological complexity at all levels, from single molecules to organisms, is a product of evolution. Therefore, quantitative and predictive models of evolution could have applications for a range of biological questions, from the evolution of disease-causing viruses to the analysis of evolved sequences in rapidly growing macromolecular databases.
My work uses modelling and theory to elucidate a key component of quantitative models of evolution: molecular and phenotypic changes introduced by variation through random mutations. This is addressed using the general framework of a genotype-phenotype (GP) map. GP maps describe how genotypic changes are translated to higher-order phenotypic characteristics. One central model in the field focuses on mutational changes in folded RNA secondary structures. Results from established models like RNA have a broad relevance because they can often be applied to other examples, ranging from other macromolecules to beyond the molecular scale. By improving such GP map models, my aim is to increase their biological realism and thus make progress towards models that can be compared directly to the rapidly increasing amount of available biological data.