TY - JOUR
T1 - The ascent of the abundant
T2 - How mutational networks constrain evolution
AU - Cowperthwaite, Matthew C.
AU - Economo, Evan P.
AU - Harcombe, William R.
AU - Miller, Eric L.
AU - Meyers, Lauren Ancel
PY - 2008/7
Y1 - 2008/7
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1000110
DO - 10.1371/journal.pcbi.1000110
M3 - Article
C2 - 18636097
AN - SCOPUS:48249115639
SN - 1553-734X
VL - 4
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 7
M1 - e1000110
ER -