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| Natural Selection May Produce The Best Fit But Not The Best Organisms |
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| SciMed - Evolution | |||
| TS-Si News Service | |||
| Monday, 21 July 2008 17:00 | |||
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However, as a late Darwin metaphor, it generally is not used in modern times by evolutionary biologists, who almost exclusively use the more scientifically accurate natural selection. While natural selection generally favors the organism that most nearly fits a given niche, evolutionary biologists have long wondered whether this leads to the best possible organisms in the long run. A team of researchers at The University of Texas at Austin (UT) [N2] has developed a new theory, which suggests that life may not always be optimal. The researchers have shown that what may be good in the short run, may hinder evolution in the long run. Genetic mutations create the raw material that natural selection acts upon. The short-term fate of a mutation is often quite clear. Mutations that make organisms more fit tend to persist through generations, while harmful mutations tend to die off with the organisms that possess them.
The long-term consequences of mutations, however, are not well understood by evolutionary biologists. A team led by Dr. Matthew C. Cowperthwaite and Dr. Lauren Ancel Meyers [N2-5] developed computer models of RNA molecules evolving by mutation and natural selection.
RNA molecules, which are very similar to DNA, play key roles in essential life processes and serve as the genetic material for some of our deadliest viruses, including influenza and HIV. Their computer models show that the evolution of optimal organisms often requires a long sequence of interacting mutations, each arising by chance and surviving natural selection. As Cowperthwaite explains,
The group's analysis of RNA molecules from a wide variety of species suggests that life is indeed dominated by the "easy" traits, perhaps at the expense of the best ones. Notes[N1] Evolutionary biologists criticize how non-scientists, such as the proponents and opponents of Social Darwinism, mistakenly apply natural selection to imply unrestrained competition.
Heritability via reproductive success is a key requirement for natural selection. In modern biology, the term fitness measures reproductive success and is not explicit about the specific ways in which organisms can have characteristics that enhance survival and reproduction. [N2] This research was supported by a grant from The James F. McDonnel Foundation to LAM, NSF IGERT graduate training fellowships to MCC, EPE, and ELM, and a Graduate Research Fellowship from the US National Science Foundation (NSF) to EPE and WRH. [N3] Matthew C. Cowperthwaite recently received his Ph.D. from the The University of Texas at Austin (UT) and is the research director of the NeuroTexas Institute Center for Computational Neuroscience at St. David's Medical Center. [N4] Lauren Ancel Meyers is an associate professor in the Section of Integrative Biology at the UT. [N5] Other co-authors on the paper include Evan Economo, William Harcombe, and Eric Miller who are graduate students in the Ecology, Evolution, and Behavior program at the UT. CitationThe Ascent of the Abundant: How Mutational Networks Constrain Evolution. Matthew C. Cowperthwaite, Evan P. Economo, William R. Harcombe, Eric L. Miller, Lauren Ancel Meyers. PLoS Comput Biol 4(7): e1000110. doi: 10.1371 / journal.pcbi.1000110
Download PDF Abstract Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance — the number of genotypes producing a particular phenotype — varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an “ascent of the abundant” hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit. Author Summary Evolutionary biology tells us much about the immediate fate of a mutation once it appears, but relatively little about its long-term evolutionary implications. Major evolutionary transitions from one trait to another may depend on a long sequence of interacting mutations, each arising by chance and surviving natural selection. In this study, we characterize the network of mutations that connect diverse molecular structures, and find that this network biases evolution toward traits that are readily produced by one or a short sequence of mutations. This bias may prevent the evolution of optimal traits, a phenomenon they call the “ascent of the abundant.”
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