Getting There First: An Evolutionary Rate Advantage for Adaptive Loss-of-Function Mutations

There is growing evidence that much of the DNA in higher genomes is poly-functional, with the same nucleotide contributing to more than one type of code. Such poly-functional DNA should logically be multiply-constrained in terms of the probability of sequence improvement via random mutation. We describe a model of this relationship, which relates the degree of poly-functionality and the degree of constraint on mutational improvement. We show that: a) the probability of beneficial mutation is inversely related to the degree that a sequence is already optimized for a given code; b) the probability of beneficial mutation drastically diminishes as the number of overlapping codes increases. The growing evidence for a high degree of optimization in biological systems, and the growing evidence for multiple levels of poly-functionality within DNA, both suggest that mutations that are unambiguously beneficial must be especially rare. The theoretical scarcity of beneficial mutations is compounded by the fact that most of the beneficial mutations that do arise should confer extremely small increments of improvement in terms of total biological function. This makes such mutations invisible to natural selection. Beneficial mutations that are below a population's selection threshold are effectively neutral in terms of selection, and so should be entirely unproductive from an evolutionary perspective. We conclude that beneficial mutations that are unambiguous (not deleterious at any level), and useful (subject to natural selection), should be extremely rare.

Section: Discussionmentioning

confidence: 99%

Multiple Overlapping Genetic Codes Profoundly Reduce the Probability of Beneficial Mutation

Montanez¹,

Marks²,

Fernández³

et al. 2013

“…Recent reviews [8,10,99] have reported that the majority of studied adaptations involve the loss of traits and the reduction of genetic information. Whether a mutation is beneficial may depend critically upon the environment in which it arises (e.g., whether nutrients are available or antibiotics are present), meaning that the effect of a mutation on genetic information cannot be inferred from relative growth rates alone.…”

Section: Reducti Ve Evoluti Onmentioning

confidence: 99%

Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

Nelson¹,

Sanford

2013

Computational evolution experiments using the population genetics simulation Mendel's Accountant have suggested that deleterious mutation accumulation may pose a threat to the long-term survival of many biological species. By contrast, experiments using the program Avida have suggested that purifying selection is extremely effective and that novel genetic information can arise via selection for high-impact beneficial mutations. The present study shows that these approaches yield seemingly contradictory results only because of disparate parameter settings. Both agree when similar settings are used, and both reveal a net loss of genetic information under biologically relevant conditions. Further, both approaches establish the existence of three potentially prohibitive barriers to the evolution of novel genetic information: (1) the selection threshold and resulting genetic decay; (2) the waiting time to beneficial mutation; and (3) the pressure of reductive evolution, i.e., the selective pressure to shrink the genome and disable unused functions. The adequacy of mutation and natural selection for producing and sustaining novel genetic information cannot be properly assessed without a careful study of these issues.

“…A survey of lab experiments showed that the adaptations found in such scenarios fit the same picture [26]. Further discussion of adaptation by loss in biological scenarios can be found within these proceedings [27].…”

Section: Discussionmentioning

confidence: 81%

Tierra: The Character of Adaptation

Ewert¹,

Dembski²,

Marks³

2013

Tierra is a digital simulation of evolution for which the stated goal was the development of openended complexity and a digital " Cambrian Explosion." However, Tierra failed to produce such a result. A closer inspection of Tierran evolution's adaptations show very few instances of adaptation through the production of new information. Instead, most changes result from removing or rearranging the existing pieces within a Tierra program. The open-ended development of complexity depends on the ability to generate new information, but this is precisely what Tierra struggles to do. The character of Tierran adaptation does not allow for open-ended complexity but is similar to the character of adaptations found in the biological world.