Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.

Mutations reflect the net effects of myriad types of damage, replication errors, and repair mechanisms, and thus are expected to differ across cell types with distinct exposures to mutagens, division ...

In simple microbial populations, the fitness effects of most selected mutations are generally taken to be constant, independent of genotype frequency. This assumption underpins predictions about evolutionary dynamics, epistatic interactions, and the maintenance of genetic diversity in populations. Here, we systematically test this assumption using beneficial mutations from early generations of the Escherichia coli Long-Term Evolution Experiment (LTEE). Using flow cytometry-based competition assays, we find that frequency-dependent fitness effects are the norm rather than the exception, occurring in approximately 80\% of strain pairs tested. Most competitions exhibit negative frequency-dependence, where fitness advantages decline as mutant frequency increases. Furthermore, we demonstrate that the strength of frequency-dependence is predictable from invasion fitness measurements, with invasion fitness explaining approximately half of the biological variation in frequency-dependent slopes. Additionally, we observe violations of fitness transitivity in several strain combinations, indicating that competitive relationships cannot always be predicted from fitness relative to a single reference strain alone. Through high-resolution measurements of within-growth cycle dynamics, we show that simple resource competition explains a substantial portion of the frequency-dependence: when faster-growing genotypes dominate populations, they deplete shared resources more rapidly, reducing the time available for fitness differences to accumulate. Our results demonstrate that even in a simple model system designed to minimize ecological complexity, subtle ecological interactions between closely related genotypes create frequency-dependent selection that can fundamentally alter evolutionary dynamics. ### Competing Interest Statement The authors have declared no competing interest.

Cells require highly polymorphic receptors to perform accurate self/non-self recognition. In the amoeba Dicytostelium discoideum, polymorphic TgrB1 & TgrC1 proteins are used to bind sister cells and e...

We are a computational lab focusing on developing machine learning algorithms for biological data. Currently we are working on inference tasks related to natural selection and demographic inference, as well as generative models for genomic data from humans, mosquitos, and other species.

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The purpose of these awards is to provide support for students entering U.S.-based Ph.D. programs with a plan to perform research in ecology and evolution. While we will consider all projects in ecolo...

Abstract. HIV’s exceptionally high recombination rate drives its intrahost diversification, enabling immune escape and multidrug resistance within people l