2019IEEE AccessOpen access

Genetic Background Modifies the Topography of a Fitness Landscape, Influencing the Dynamics of Adaptive Evolution

C. Brandon Ogbunugafor, Margaret J. Eppstein

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Abstract

Understanding the forces that drive the dynamics of adaptive evolution is a goal of many sub-fields and applications of evolutionary biology, including in evolutionary computation. 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 genetic context in which a protein is embedded affects fitness landscape topography and subsequent evolution. Using simulations on empirical fitness landscapes, we discover that the genetic background - 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 (whether biological, computational, or technological): that small differences in environmental or genetic background can greatly alter the specifics of how evolution occurs, which can rapidly drive even slightly diverged populations further apart.

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Understanding the forces that drive the dynamics of adaptive evolution is a goal of many sub-fields and applications of evolutionary biology, including in evolutionary computation. 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 genetic context in which a protein is embedded affects fitness landscape topography and subsequent evolution. Using simulations on empirical fitness landscapes, we discover that the genetic background - 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 (whether biological, computational, or technological): that small differences in environmental or genetic background can greatly alter the specifics of how evolution occurs, which can rapidly drive even slightly diverged populations further apart.

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Available abstract

Understanding the forces that drive the dynamics of adaptive evolution is a goal of many sub-fields and applications of evolutionary biology, including in evolutionary computation. 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 genetic context in which a protein is embedded affects fitness landscape topography and subsequent evolution. Using simulations on empirical fitness landscapes, we discover that the genetic background - 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 (whether biological, computational, or technological): that small differences in environmental or genetic background can greatly alter the specifics of how evolution occurs, which can rapidly drive even slightly diverged populations further apart.

Key concepts: Fitness landscape, Computer science, Dynamics (music), Evolutionary biology, Biology, Physics, Population, Demography

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