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We study the evolution of a population in a two-locus genotype space, in which the negative effects of two single mutations are overcompensated in a high-fitness double mutant. We discuss how the interplay of finite population size N and sexual recombination at rate r affects the escape times t(esc) to the double mutant. For small populations demographic(More)
The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we(More)
Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations to overcome this threat are valuable. Here we use the mathematical(More)
The within-host evolution of influenza is a vital component of its epidemiology. A question of particular interest is the role that selection plays in shaping the viral population over the course of a single infection. We here describe a method to measure selection acting upon the influenza virus within an individual host, based upon time-resolved genome(More)
Familial adenomatous polyposis (FAP) and MUTYH-associated polyposis (MAP) are inherited disorders associated with multiple colorectal adenomas that lead to a very high risk of colorectal cancer. The somatic mutations that drive adenoma development in these conditions have not been investigated comprehensively. In this study we performed analysis of paired(More)
The contribution of pre-existing and de novo genetic variation towards clonal adaptation is poorly understood, but essential to design successful antibiotic or cancer therapies. To address this, we evolved genetically diverse populations of budding yeast, S. cerevisiae, consisting of ∼10 7 diploid cells with unique haplotype combinations. We studied the(More)
1 Derivation of the EM algorithm for mutation spectra 1.1 The generative model The EM algorithm presented in this work is an application of the method first described in [1]. The starting point of the derivation is the conditional data likelihood, i.e. the probability of observed data (the mutation counts X), conditional on both hidden data (the process(More)
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