Evolvability

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In order for a biological organism to evolve on a geological time scale, there must be a certain minimum probability that mutations and other changes be beneficial. Random mutations are expected to be mostly detrimental, but if the proportion of beneficial mutations is too small, then, in practice, evolution just can't happen. Biological genomes are structured in ways that make beneficial changes less unlikely than they would otherwise be. This has been taken as evidence that evolution has created not just fitter organisms, but organisms and communities of organisms that are better able to evolve.

Evolvability is a concept in evolutionary biology that tries to measure an organism's ability to evolve. Although several definitions are possible, most broadly evolvability is defined as the ability of a population of organisms to generate genetic diversity and evolve through natural selection.^[1]

Wagner (2005) describes two definitions of evolvability which have two main meanings. The first one is: A biological system is evolvable

• if its properties show heritable genetic variation,

and

• if natural selection can thus change these properties.

The second one is: A biological system is evolvable

• if it can acquire novel functions through genetic change, functions that help the organism survive and reproduce.

These definitions can be applied on all levels of biological organisation, from macromolecules to mammals. The two meanings are not synonymous. Not all systems that are evolvable in the first sense are evolvable in the second sense. An example is given by Wagner (2005).

Evolvability can also refer to a property of any organism characteristic that alters the ability of the organism to adapt or to the combined effect of all such characteristics on an organism's ability to adapt. Since the ability to adapt more rapidly or comprehensively would be of value to any organism under evolutionary pressure, characteristics that increased evolvability would tend to be selected and retained.

However, evolvability characteristics differ from classical fitness characteristics in that evolvability characteristics are generally individually adverse or neutral. In the example given above, a population that possessed more variation would possess more evolvability because variation increases evolvability. That population would be more able to adapt to changing conditions. A population that had less variation would be more fit. In the absence of evolutionary pressure, an individual would be more likely to possess optimum characteristics in a population with less variation. Therefore evolvability is a tradeoff with classical fitness. In addition to increasing variation, organism characteristics such as mating rituals that enhance selection by magnifying the functional difference between organisms having different characteristics can contribute to evolvability.

Evolvability provides explanations for many observed characteristics that are apparently individually (fitness) adverse such as self-limited life span (semelparous organisms), aging, sexual reproduction, and some behaviors.

Evolution of Variation

Darwin (1859) identified "natural" variation in inheritable characteristics between members of a species population as essential to the evolution process. Darwin believed that biological inheritance was an analogue, continuously variable process. Gregor Mendel^[2] (1866) described the discrete (not continuously variable) nature of inheritance. In their famous 1953 paper^[3] Watson and Crick revealed that inheritable organism characteristics are controlled by a digital method. Such characteristics are determined by the sequence in which four possible base molecules are assembled in DNA molecules to form a serial digital genetic code. Variation is not a natural property of digital information (see Common properties of all digital information). It subsequently became clear that the variation observed in populations of complex species was largely the result of an assortment of evolved characteristics such as genetic recombination during meiosis, including interchange of paired chromosomes, chromosomal crossover in which parts of chromosomes are exchanged, and many other processes. (A simple example: A behavioral trait that causes an animal to seek unrelated mates increases variation.) Although mutations are the ultimate source of variation, the mutations are processed and assembled by many evolved mechanisms. Organism characteristics that increase variation benefit the evolution process and thereby increase evolvability. Sexual reproduction (including above mentioned aspects) has enormous variation (and thereby evolvability) advantages relative to asexual reproduction. Sexual reproduction is otherwise substantially adverse relative to asexual reproduction (See Sex and recombination.)

Enhancement of Selection

Another category of evolvability characteristics are those that increase the effectiveness of the natural selection process and thereby enable the possessing populations to adapt more rapidly and comprehensively. The following are examples of such characteristics:

• Mating rituals that involve contests that would tend to select for desirable fitness characteristics.
• Characteristics that tend to increase the number of organisms participating in the natural selection process. Example: a limited life span prevents a relatively few older individuals from dominating the gene pool.
• Characteristics that increase generation rate: A limited life span increases the generation rate, increasing the rate at which generational increments in adaptation occur. An organism capable of maturing more rapidly could attain a higher generation rate thus enhancing selection.

Example

Consider an enzyme-coding gene that undergoes different mutations in different individuals of a population. Because of the mutations, the activity of the enzyme fluctuates among different individuals. If this mutation is heritable and influences fitness then natural selection can act on the enzyme’s activity. The enzyme’s activity is thus evolvable in the first sense. However, after millions of years, no mutation might give this enzyme a trait which might permit survival in a new environment. Thus, although the enzyme’s activity is evolvable in the first sense, that does not mean it is evolvable in the second sense. The opposite does not work. Every innovative, evolvable system can evolve by natural selection.

Organisms are incredibly complex, yet also highly robust to genetic change on all levels of organization. This robustness is one of a few aspects that can affect evolvability in the first and the second sense.

Robustness and evolvability

Robustness will not increase evolvability in the first sense. In organisms with a high level of robustness, mutations will have smaller phenotypic effects than in organisms with a low level of robustness. Thus, robustness reduces the amount of heritable genetic variation on which selection can act. One can see this conclusion in two ways: The first way is that robustness causes mutations to be neutral and therefore no innovation will occur. The second way gives neutral mutations an important function in innovation. Although many neutral mutations do not change primary functions, they can change other system features for future evolution. So, robustness can facilitate exaptation. From this point of view, robustness implies that many mutations are neutral and such neutrality promotes innovation.

See also

• Modularity (biology)
• Current research in evolutionary biology
• Characteristics of digital systems
• Evolution of ageing
• Altruism in animals
• Natural selection

References

• Altenberg L. 1995. Genome growth and the evolution of the genotype-phenotype map. In Evolution and Biocomputation: Computational Models of Evolution, ed. Wolfgang Banzhaf and Frank H. Eeckman. Lecture Notes in Computer Science vol. 899. Springer-Verlag, p205–259. ISBN 0387590463.
• Conrad, M. 1979. Bootstrapping on the adaptive landscape. BioSystems 11:167–182.
• Dawkins R. 1989. The evolution of evolvability. In C.G. Langton, editor, Artificial life, the proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. Addison-Wesley, Redwood City, CA.
• Goldsmith T. 2008. Aging, evolvability, and the individual benefit requirement. Journal of Theoretical Biology 252:764–768
• Eshel I. 1973. Clone-selection and optimal rates of mutation. Journal of Applied Probability 10: 728–738.
• Kirschner M. and J. Gerhart, 1998. Evolvability. PNAS 95(15): 8420–8427.
• Nehaniv C.L. 2003. Evolvability (Editorial, special issue on evolvability, dedicated to the memory of Professor Michael Conrad), BioSystems: Journal of Biological and Information Processing Sciences 69:77–81.
• Riedl R.J. 1977. A systems-analytical approach to macroevolutionary phenomena. Quarterly Review of Biology 52:351–370.
• Wagner A. 2005. Robustness and evolvability in living systems. (Princeton Studies in Complexity) Princeton University Press. ISBN 0691122407.
• Wagner, A., 2005. Robustness, evolvability and neutrality. FEBS Letters 579, 1772–1778.
• Wagner G.P. and L. Altenberg 1996. Complex adaptations and the evolution of evolvability. Evolution 50:967–976.