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Please use this identifier to cite or link to this item: http://repositorio.inger.gob.mx/jspui/handle/20.500.12100/17283
Title: Criticality, adaptability and early-warning signals in time series in a discrete quasispecies model
metadata.dc.creator: RUBEN YVAN MAARTEN FOSSION
DIEGO ANDRES HARTASANCHEZ FRENK
OSBALDO RESENDIS ANTONIO
ALEJANDRO FRANK HOEFLICH
Keywords: BIOLOGÍA Y QUÍMICA;Ciencias de la vida;Genética;Genética clínica;Fenómenos genéticos;Variación genética;Quiasispecies;1/f noise;Genetics;Genetic phenomena;Genetic variation
metadata.dc.date: 2013
Publisher: Springer Verlag & Higher Education Press
Description: Complex systems from different fields of knowledge often do not allow a mathematical description or modeling, because of their intricate structure composed of numerous interacting components. As an alternative approach, it is possible to study the way in which observables associated with the system fluctuate in time. These time series may provide valuable information about the underlying dynamics. It has been suggested that complex dynamic systems, ranging from ecosystems to financial markets and the climate, produce generic early-warning signals at the “tipping points,” where they announce a sudden shift toward a different dynamical regime, such as a population extinction, a systemic market crash, or abrupt shifts in the weather. On the other hand, the framework of Self-Organized Criticality (SOC), suggests that some complex systems, such as life itself, may spontaneously converge toward a critical point. As a particular example, the quasispecies model suggests that RNA viruses self-organize their mutation rate near the error-catastrophe threshold, where robustness and evolvability are balanced in such a way that survival is optimized. In this paper, we study the time series associated to a classical discrete quasispecies model for different mutation rates, and identify early-warning signals for critical mutation rates near the error-catastrophe threshold, such as irregularities in the kurtosis and a significant increase in the autocorrelation range, reminiscent of 1/f noise. In the present context, we find that the early-warning signals, rather than broadcasting the collapse of the system, are the fingerprint of survival optimization.
URI: http://repositorio.inger.gob.mx/jspui/handle/20.500.12100/17283
Appears in Collections:1. Artículos

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