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Advisor(s)
Abstract(s)
The dynamics of catalytic networks have been widely studied over the last decades because of their implications in several fields like prebiotic evolution, virology, neural networks, immunology or ecology. One of the most studied mathematical bodies for catalytic networks was initially formulated in the context of prebiotic evolution, by means of the hypercycle theory. The hypercycle is a set of self-replicating species able to catalyze other replicator species within a cyclic architecture. Hypercyclic organization might arise from a quasispecies as a way to increase the informational containt surpassing the so-called error threshold. The catalytic coupling between replicators makes all the species to behave like a single and coherent evolutionary multimolecular unit. The inherent nonlinearities of catalytic interactions are responsible for the emergence of several types of dynamics, among them, chaos. In this article we begin with a brief review of the hypercycle theory focusing on its evolutionary implications as well as on different dynamics associated to different types of small catalytic networks. Then we study the properties of chaotic hypercycles with error-prone replication with symbolic dynamics theory, characterizing, by means of the theory of topological Markov chains, the topological entropy and the periods of the orbits of unimodal-like iterated maps obtained from the strange attractor. We will focus our study on some key parameters responsible for the structure of the catalytic network: mutation rates, autocatalytic and cross-catalytic interactions.
Description
Keywords
Chaos Hypercycles Markov Metrics Prebiotic Evolution Topological Entropy Spatiotemporal Dynamics Genetic Information Self-Replication Error Threshold Parasites Complementation Evolution Networks Model Populations
Citation
SARDANYES, J.; [et al] – Topological entropy of catalytic sets: Hypercycles revisited. Communications in Nonlinear Science and Numerical Simulation. ISSN: 1007-5704. Vol. 17, nr. 2 (2012), pp. 795-803
Publisher
Elsevier Science Bv