ArXiv TLDR

Mathematical Models of Evolution and Replicator Systems Dynamics. Chapter 1: Introduction to Replicator Systems

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2604.05720

A. S. Bratus, S. Drozhzhin, T. Yakushkina

q-bio.PEmath.DS

TLDR

This chapter introduces mathematical models of replicator systems, deriving the replicator equation and surveying quasispecies theory for generalized evolutionary dynamics.

Key contributions

  • Derives the replicator equation from Kolmogorov equations for interacting populations.
  • Examines three canonical replication regimes: independent, autocatalytic, and hypercyclic.
  • Demonstrates that hypercycles are permanent and intrinsically carry evolutionary variability.
  • Surveys the quasispecies framework, including Eigen and Crow-Kimura models and the error-threshold.

Why it matters

This chapter provides a unified mathematical framework for evolutionary processes, applicable to any system with heredity, variability, and selection. It emphasizes underlying mathematical structures, extending beyond traditional molecular biology to abstract evolutionary dynamics.

Original Abstract

This chapter is an overview of foundational results in the mathematical theory of replicator systems. Its primary aim is to provide a unified framework for the mathematical formalisation of evolutionary processes in the spirit of generalised Darwinism -- that is, for any system in which heredity, variability, and selection can be meaningfully defined, regardless of the specific biological substrate. Starting from the Kolmogorov equations for interacting populations, we derive the replicator equation and examine three canonical regimes: independent, autocatalytic, and hypercyclic replication. The hypercycle is shown to be permanent and to carry evolutionary variability intrinsically. We then survey the quasispecies framework -- the Eigen and Crow--Kimura models -- covering global stability of equilibria, sequence space structure, and the error-threshold phenomenon. Throughout, the emphasis is on the mathematical structures that underlie these models rather than on biological detail, with the goal of making the framework applicable to abstract evolutionary dynamics beyond its original molecular biology context.

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