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Biomedical subjects

Laurie A Dries

Publications and source records attributed to Laurie A Dries.

2 recordsLinked to original sources

Peering through the looking glass at a sexual parasite: are Amazon mollies red queens?

The gynogenetic Amazon molly (Poecilia formosa) is a clonal, all-female lineage of livebearing fish that faces an unusual obstacle to evolutionary persistence. Sperm from heterospecific males (either sailfin, P. latipinna, or Atlantic, P. mexicana, mollies) is necessary to trigger embryogenesis. However, none of the male's genes are incorporated into the genome of the gynogenetic offspring. Some investigators have proposed that the evolution of male mate discrimination is a result of this cost, leading to a coevolutionary arms race between male avoidance of P. formosa and P. formosa attractiveness. Given that P. formosa successfully reproduces and has not yet gone extinct, it is clear there are mechanisms by which they attract the sexual attention of males. Although a Red Queen coevolutionary process in typical host/parasite systems has been shown to favor the persistence of sexual species, in this system an arms race has been invoked to explain the reverse. Here I present behavioral data supporting a more parsimonious scenario: that mechanisms of attraction in P. formosa are simply a consequence of its hybrid origin. Poecilia latipinna and P. mexicana males do not discriminate between gynogenetic P. formosa females and first generation sexual hybrid females, and females do not differ in agonistic behaviors associated with competition for mates. Both results contradict predictions from the Red Queen hypothesis. Therefore, coevolution is not necessary to explain the apparent evolutionary persistence of P. formosa.

Animals↗

Genetic algorithms and parallel processing in maximum-likelihood phylogeny inference.

We investigated the usefulness of a parallel genetic algorithm for phylogenetic inference under the maximum-likelihood (ML) optimality criterion. Parallelization was accomplished by assigning each "individual" in the genetic algorithm "population" to a separate processor so that the number of processors used was equal to the size of the evolving population (plus one additional processor for the control of operations). The genetic algorithm incorporated branch-length and topological mutation, recombination, selection on the ML score, and (in some cases) migration and recombination among subpopulations. We tested this parallel genetic algorithm with large (228 taxa) data sets of both empirically observed DNA sequence data (for angiosperms) as well as simulated DNA sequence data. For both observed and simulated data, search-time improvement was nearly linear with respect to the number of processors, so the parallelization strategy appears to be highly effective at improving computation time for large phylogenetic problems using the genetic algorithm. We also explored various ways of optimizing and tuning the parameters of the genetic algorithm. Under the conditions of our analyses, we did not find the best-known solution using the genetic algorithm approach before terminating each run. We discuss some possible limitations of the current implementation of this genetic algorithm as well as of avenues for its future improvement.

Algorithms↗