Negative Epistasis Between Beneficial Mutations in an Evolving Bacterial Population

By A. I. Khan, Duy M. Dinh, D. Schneider, Richard E. Lenski, T. Cooper. 2011

579 citations recorded by the source corpus.

Subfields: Genetics, Public Health, Environmental and Occupational Health, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Public Health, Environmental and Occupational Health

In Sociology and Political Science

Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “One key issue in the description of a mutational landscape is to understand how much the effect of a mutation depends on the genetic background in which it appears [3, 8, 9].”
A model of substitution trajectories in sequence space and long-term protein evolution. (2015, background): “Two types of approaches are being used to reveal the type and amount of epistasis in protein evolution: First, studies aiming to reconstruct recent evolutionary trajectories revealing potential epistatic interactions among substitutions that occurred recently (Weinreich et al. 2006; Bridgham et al. ”
Global Epistasis Makes Adaptation Predictable Despite Sequence-Level Stochasticity (2014, background): “Related work has provided evidence for such patterns among particular small sets of mutations that arise in a single experimental line (22, 23).”
Patterns of Epistasis between Beneficial Mutations in an Antibiotic Resistance Gene (2013, background): “Several recent studies have found a pattern of diminishing returns epistasis among beneficial mutations, where the negative epistatic deviation from a null model increases with the expected benefit (MacLean et al. 2010; Chou et al. 2011; Khan et al. 2011; Nagel et al. 2012).”
Epistasis and Adaptation on Fitness Landscapes (2022, background): “When Hall et al. (2019) competed the 32 E.coli genotypes from Khan et al. (2011) across eight different environments, the resulting fitness landscapes looked vastly different between environments; epistasis was shown to be both strong and environment-specific (Figure 3; see also Flynn et al. 2013).”
Adaptation in protein fitness landscapes is facilitated by indirect paths (2016, methodology): “To quantify the magnitude of epistasis (") between substitutions a and b on a given background variant BG, the relative epistasis model (Khan et al., 2011) was employed as follows: ”
Inferring fitness landscapes by regression produces biased estimates of epistasis (2014, background): “Even in these limited cases genetic variation was restricted to a handful of genetic sites [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41].”
Should evolutionary geneticists worry about higher-order epistasis? (2013, background): “coli beneficial mutations in Lenski long-term lines [30]”