Medical Health Articles

Evolution May Yield Most Abundant Traits, Not Best

Evolution may provide us with the most abundant phenotypes (observablegenetic characteristics) rather than the fittest, according to a newtheory published on July 18 in the open-access journal PLoSComputational Biology. That is, natural selection may beoptimal for choosing the most fit organism of the moment, butevolutionary biologists question if the process leads to the optimalorganisms in the long run. Researchers from The University of Texas atAustin, led by Drs. Matthew Cowperthwaite and Lauren Ancel Meyers,propose a new theory: life may not always be optimal.

Natural selection is driven by genetic mutations, and we usually canpredict and understand the short-term fate of a mutation. If a mutationmakes the organism more fit, it tends to last through the years; if themutation is harmful, it usually dies off with its host organism.Evolutionary biologists, however, do not have such a completeunderstanding of the long-term consequences of mutations. Is itpossible that what is good now may be not-so-good later?

To study this question, researchers modeled RNA molecules that evolvedby mutation and natural selection. RNA is similar to DNA and isnecessary in life processes. Additionally, RNA functions as geneticmaterial for viruses such as HIV and influenza.

The computer analysis revealed that long sequences of interactingmutations are often required for evolution to create the optimalorganism. Each mutation in the sequence, however, must arise by chanceand survive natural selection in the short run. Cowperthwaiteexplains that, “Some traits are easy to evolve - formed by manydifferent combinations of mutations. Others are hard to evolve - madefrom an unlikely genetic recipe. Evolution gives us the easy ones, evenwhen they are not the best.”

The analysis leads the group to conclude that it may be the easy traits- the abundant ones - that dominate life rather than the best ones.

The Ascent of the Abundant: How Mutational Networks ConstrainEvolution
Cowperthwaite MC, Economo EP, Harcombe WR, Miller EL, Ancel Meyers L
PLoS Computational Biology (2008). 4(7):e1000110.
doi:10.1371/journal.pcbi.1000110
ClickHere to View Article

About PLoS Computational Biology

PLoS Computational Biology (www.ploscompbiol.org)features works of exceptional significance that further ourunderstanding of living systems at all scales through the applicationof computational methods. All works published in PLoS ComputationalBiology are open access. Everything isimmediately available subject only to the condition that the originalauthorshipand source are properly attributed. Copyright is retained by theauthors. ThePublic Library of Science uses the Creative Commons Attribution License.

Stumble it!