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Understanding the forces that drive the dynamics of adaptive evolution is a goal of many subfields within evolutionary biology. The fitness landscape analogy has served as a useful abstraction for addressing these topics across many systems, and recent treatments have revealed how different environments can frame the particulars of adaptive evolution by changing the topography of fitness landscapes. In this study, we examine how the larger, ambient genotypic context in which the fitness landscape being modeled is embedded affects fitness landscape topography and subsequent evolution. Using simulations on empirical fitness landscapes, we discover that genotypic context, defined by genetic variability in regions outside of the locus under study (in this case, an essential bacterial enzyme target of antibiotics), influences the speed and direction of evolution in several surprising ways. These findings have implications for how we study the evolution of drug resistance in nature, and for presumptions about how biological evolution might be expected to occur in genetically-modified organisms. More generally, the findings speak to theory surrounding how “difference can beget difference” in adaptive evolution: that small genetic differences between organisms can greatly alter the specifics of how evolution occurs, which can rapidly drive even slightly diverged populations further apart. 

Despite the infrequent occurrence of cascading power failures, their large sizes and enormous social costs mean that they contribute substantially to the overall risk to society from power failures in the grid. Therefore it is important to accurately understand the risk associated with such events. A cascading event may be triggered by a small subset of k components failing simultaneously or in rapid succession. While most prior work, including our own work into an efficient “Random Chemistry” method for risk analysis, has assumed that components fail independently, this paper proposes a method for deriving correlated outage probabilities such that pairs of branches that are proximate in space are more likely to fail together than distant ones. Combining Random Chemistry risk analysis with this approach to correlated outage probabilities shows that overall blackout risk can greatly increase with even small amounts of correlation. Results from the 2383-bus Polish test case under various load levels illustrate the substantial impact that correlation has on blackout risk.