There's a speed limit to the pace of evolution, say biologists
3 Nov 2009, 1521 Hrs

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Washington, November 3 Biologists at the University of Pennsylvania have developed a theoretical model that determines how quickly an organism will evolve using a catalogue of "evolutionary speed limits."

The model provides quantitative predictions for the speed of evolution on various "fitness landscapes," the dynamic and varied conditions under which bacteria, viruses and even humans adapt.

A major conclusion of the work is that for some organisms, possibly including humans, continued evolution will not translate into ever-increasing fitness.

Moreover, a population may accrue mutations at a constant rate - a pattern long considered the hallmark of "neutral" or non-Darwinian evolution - even when the mutations experience Darwinian selection.

Penn researchers presented a theory of how the fitness of a population will increase over time, for a total of 14 types of underlying landscapes or "speed limits" that describe the consequences of available genetic mutations.

These categories determine the speed and pattern of evolution, predicting how a population's overall fitness, and the number of accumulated beneficial mutations are expected to increase over time.

Researchers compared the theory to the data from a two-decades study of E. coli to investigate how the bacterium evolves.

Organisms of that simplicity and size reproduce more rapidly than larger species, providing 40,000 generations of data to study.

"We asked, quantitatively, how a population's fitness will increase over time as beneficial mutations accrue," said Joshua B. Plotkin, principal investigator and an assistant professor in the Department of Biology in Penn's School of Arts and Sciences.

His research focuses on evolution at the molecular scale.

"This was an attempt to provide a theoretical framework for studying rates of molecular evolution," said first-author Sergey Kryazhimskiy, also of the Department of Biology.

"We applied this theory to infer the underlying fitness landscape of bacteria, using data from a long-term bacterial experiment," he added.

According to the study, a population's fitness and substitution trajectories - the mutations acquired to achieve higher fitness - depend not on the full distribution of fitness effects of available mutations but rather on the expected fixation probability and the expected fitness increment of mutations.

This mathematical observation greatly simplifies the possible trajectories of evolution into 14 distinct categories.

Applying these methods to data from bacterial experiments allowed the researchers to characterize the evolutionary relationships mong beneficial mutations in the E. coli genome. (ANI)

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