Statistical Physics of Evolutionary Trajectories on Fitness Landscapes

Abstract

Random walks on multidimensional nonlinear landscapes are of interest in many areas of science and engineering. In particular, properties of adaptive trajectories on fitness landscapes determine population fates and thus play a central role in evolutionary theory. The topography of fitness landscapes and its effect on evolutionary dynamics have been extensively studied in the literature. We will survey the current research knowledge in this field, focusing on a recently developed systematic approach to characterizing path lengths, mean first-passage times, and other statistics of the path ensemble. This approach, based on general techniques from statistical physics, is applicable to landscapes of arbitrary complexity and structure. It is especially well-suited to quantifying the diversity of stochastic trajectories and repeatability of evolutionary events. We demonstrate this methodology using a biophysical model of protein evolution that describes how proteins maintain stability while evolving new functions.

References in corpus (9)

• First-passage times in complex scale-invariant media
• Neutral genetic drift can aid functional protein evolution
• Thermodynamics of Neutral Protein Evolution
• Evolution favors protein mutational robustness in sufficiently large populations
• Predictability of evolutionary trajectories in fitness landscapes
• Closed-form solutions for continuous time random walks on finite chains
• Transition path sampling algorithm for discrete many-body systems
• A path-based approach to random walks on networks characterizes how proteins evolve new function
• Single temperature for Monte Carlo optimization on complex landscapes

Cited by in corpus (2)

• Scaling properties of evolutionary paths in a biophysical model of protein adaptation
• Computing the Asymptotic Spectrum for Networks Representing Energy Landscapes using the Minimal Spanning Tree