COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 197
Along these lines, a particularly interesting work on the reconstruction of phylogenies was reported
in 2003 by Rokas et al.^110 One of the primary goals of evolutionary research has been understanding the
historical relationships between living organisms—reconstruction of the phylogenetic tree of life. A
primary difficulty in phylogenetic reconstruction is that different single-gene datasets often result in
different and incongruent phylogenies. Such incongruences occur in analyses at all taxonomic levels,
from phylogenies of closely related species to relationships between major classes or phyla and higher
taxonomic groups.
Many factors, both analytical and biological, may cause incongruence. To overcome the effect of
some of these factors, analysis of concatenated datasets has been used. However, phylogenetic analyses
of different sets of concatenated genes do not always converge on the same tree, and some studies have
yielded results at odds with widely accepted phylogenies.
Rokas et al. exploited genome sequence data for seven Saccharomyces species and for the outgroup
fungus Candida albicans to construct a phylogenetic tree. Their results suggested that datasets consisting of
a single gene or a small number of concatenated genes had a significant probability of supporting conflict-
ing topologies, but that use of the entire dataset of concatenated genes resulted in a single, fully resolved
phylogeny with the maximum likelihood. In addition, all alternative topologies resulting from single-gene
analyses were rejected with high probability. In other words, even though the individual genes examined
supported alternative trees, the concatenated data exclusively supported a single tree. They concluded
that “the maximum support for a single topology regardless of method of analysis is strongly suggestive
of the power of large data sets in overcoming the incongruence present in single-gene analyses.”
5.4.8.2.2 Modeling of Myxomatosis Evolution in AustraliaEvolution also provides a superb and easy-
to-understand example of time scales in biological phenomena. Around 1860, a nonindigenous rabbit
was introduced into Australia as part of British colonization of that continent. Since this rabbit had no
indigenous foe, it proliferated wildly in a short amount of time (about 20 years). Early in the 1950s
Australian authorities introduced a particular strain of virus that was deadly to the rabbit.
The data indicated that in the short term (say, on a time scale of a few months), the most
virulent strains of the virus were dominant (i.e., the virus had a lethality of 99.8 percent). This is not
surprising, in the sense that one might expect virulence to be a measure of viral fitness. However, in
the longer term (on a scale of decades), similar measurements indicate that these more virulent
strains were no longer dominant, and the dominant niche was occupied by less virulent strains
(lethality of 90 percent or less). The evolutionary explanation for this latter phenomenon is that an
excessively virulent virus would run the risk of killing off its hosts at too rapid a rate, thereby
jeopardizing its own survival. The underlying mechanism responsible for this counterintuitive
phenomenon is that transmission of the virus depended on mosquitoes feeding from live rabbits.
Rabbits that were infected with the more virulent variant died quickly, and thus, fewer were avail-
able as sources of that variant.
The above system was modeled in closed form based on a set of coupled differential equations; this
model was successful in reproducing the essential qualitative features described above.^111 In 1990, this
model was extended by Dwyer et al. to incorporate more biologically plausible features.^112 For ex-
ample, the evolution of rabbit and virus reacting to each other was modeled explicitly. A multiplicity of
(^110) A. Rokas, B.L. Williams, N. King, and S.B. Carroll, “Genome-scale Approaches to Resolving Incongruence in Molecular
Phylogenies,” Nature 425(6960):798-804, 2003.
(^111) S. Levin and D. Pimentel, “Selection of Intermediate Rates of Increase in Parasite-Host Systems,” The American Naturalist
117(3), 1981.
(^112) G. Dwyer, S.A. Levin, and L.A. Buttel, “A Simulation Model of the Population Dynamics and Evolution of Myxomatosis,”
Ecological Monographs 60(4):423-447, 1990.