Chapter

Recent Advances in the Theory and Application of Fitness Landscapes

Volume 6 of the series Emergence, Complexity and Computation pp 509-526

Predicting Evolution and Visualizing High-Dimensional Fitness Landscapes

  • Bjørn ØstmanAffiliated withDepartment Microbiology and Molecular Genetics & BEACON Center for the Study of Evolution in Action, Michigan State University Email author 
  • , Christoph AdamiAffiliated withDepartment Microbiology and Molecular Genetics & BEACON Center for the Study of Evolution in Action, Michigan State University

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Abstract

The tempo and mode of an adaptive process is strongly determined by the structure of the fitness landscape that underlies it. In order to be able to predict evolutionary outcomes (even on the short term), we must know more about the nature of realistic fitness landscapes than we do today. For example, in order to know whether evolution is predominantly taking paths that move upwards in fitness and along neutral ridges, or else entails a significant number of valley crossings, we need to be able to visualize these landscapes: we must determine whether there are peaks in the landscape, where these peaks are located with respect to one another, and whether evolutionary paths can connect them. This is a difficult task because genetic fitness landscapes (as opposed to those based on traits) are high-dimensional, and tools for visualizing such landscapes are lacking. In this contribution, we focus on the predictability of evolution on rugged genetic fitness landscapes, and determine that peaks in such landscapes are highly clustered: high peaks are predominantly close to other high peaks. As a consequence, the valleys separating such peaks are shallow and narrow, such that evolutionary trajectories towards the highest peak in the landscape can be achieved via a series of valley crossings.