Clone size distributions in networks of genetic similarity

E. Hernández-García*, A. F. Rozenfeld, V. M. Eguíluz, S. Arnaud-Haond, C. M. Duarte

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We build networks of genetic similarity in which the nodes are organisms sampled from biological populations. The procedure is illustrated by constructing networks from genetic data of a marine clonal plant. An important feature in the networks is the presence of clone subgraphs, i.e. sets of organisms with identical genotype forming clones. As a first step to understanding the dynamics that has shaped these networks, we point up a relationship between a particular degree distribution and the clone size distribution in the populations. We construct a dynamical model for the population dynamics, focussing on the dynamics of the clones, and solve it for the required distributions. Scale free and exponentially decaying forms are obtained depending on parameter values, the first type being obtained when clonal growth is the dominant process. Average distributions are dominated by the power law behavior presented by the fastest replicating populations.

Original languageEnglish (US)
Pages (from-to)166-173
Number of pages8
JournalPhysica D: Nonlinear Phenomena
Issue number1-2
StatePublished - Dec 2006

Bibliographical note

Funding Information:
This research was funded by a project of the BBVA Foundation (Spain), by project NETWORK (POCI/MAR/57342/2004) of the Portuguese Science Foundation (FCT) and by the project CONOCE2 (FIS2004-00953) of the Spanish MEC. S.A.H. was supported by a postdoctoral fellowship from FCT and the European Social Fund and A.F.R. by a post-doctoral fellowship from the Spanish Ministry of Education and Science.


  • Clonal growth
  • Genetic similarity network
  • Population dynamics
  • Seagrass
  • Size distribution

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Condensed Matter Physics
  • Applied Mathematics


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