Frontiers in Biology

, Volume 8, Issue 2, pp 247–259

Criticality, adaptability and early-warning signals in time series in a discrete quasispecies model

  • R. Fossion
  • D. A. Hartasánchez
  • O. Resendis-Antonio
  • A. Frank
Research Article

DOI: 10.1007/s11515-013-1256-0

Cite this article as:
Fossion, R., Hartasánchez, D.A., Resendis-Antonio, O. et al. Front. Biol. (2013) 8: 247. doi:10.1007/s11515-013-1256-0

Abstract

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.

Keywords

time series complexity early-warning signals quasispecies 1/f noise optimization 

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • R. Fossion
    • 1
    • 2
  • D. A. Hartasánchez
    • 2
    • 3
  • O. Resendis-Antonio
    • 4
  • A. Frank
    • 2
    • 5
  1. 1.Instituto Nacional de Geriatría, Periférico Sur No. 2767, Col. San Jerónimo LídiceDel. Magdalena ContrerasMéxico D.F.Mexico
  2. 2.Centro de Ciencias de la Complejidad (C3)Universidad Nacional Autónoma de MéxicoMéxico D.F.Mexico
  3. 3.Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra)Barcelona, CataloniaSpain
  4. 4.Systems Biology GroupInstituto Nacional de Medicina Genomica (INMEGEN)MexicoMexico
  5. 5.Instituto de Ciencias NuclearesUniversidad Nacional Autónoma de MéxicoMéxico D.F.Mexico