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The effect of genetic robustness on evolvability in digital organisms.

Elena SF, Sanjuán R - BMC Evol. Biol. (2008)

Bottom Line: Here, we use the Avida digital evolution platform to explore the effects of genetic robustness on evolvability.For more complex environments, however, results are less conclusive.A likely scenario is that, in the short-term, genetic robustness hampers evolvability because it reduces the intensity of selection, but that, in the long-term, relaxed selection facilitates the accumulation of genetic diversity and thus, promotes evolutionary innovation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Biología Molecular y Celular de Plantas, CSIC-UPV, València, Spain. sfelena@ibmcp.upv.es

ABSTRACT

Background: Recent work has revealed that many biological systems keep functioning in the face of mutations and therefore can be considered genetically robust. However, several issues related to robustness remain poorly understood, such as its implications for evolvability (the ability to produce adaptive evolutionary innovations).

Results: Here, we use the Avida digital evolution platform to explore the effects of genetic robustness on evolvability. First, we obtained digital organisms with varying levels of robustness by evolving them under combinations of mutation rates and population sizes previously shown to select for different levels of robustness. Then, we assessed the ability of these organisms to adapt to novel environments in a variety of experimental conditions. The data consistently support that, for simple environments, genetic robustness fosters long-term evolvability, whereas, in the short-term, robustness is not beneficial for evolvability but may even be a counterproductive trait. For more complex environments, however, results are less conclusive.

Conclusion: The finding that the effect of robustness on evolvability is time-dependent is compatible with previous results obtained using RNA folding algorithms and transcriptional regulation models. A likely scenario is that, in the short-term, genetic robustness hampers evolvability because it reduces the intensity of selection, but that, in the long-term, relaxed selection facilitates the accumulation of genetic diversity and thus, promotes evolutionary innovation.

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Related in: MedlinePlus

A) Rate of adaptation to a novel 8-task environment as a function of mutation rate, for fragile F (white) and robust R (black) genotypes. Adaptation was measured as the difference in log fitness of the evolved and ancestral organisms. Fitness was first averaged over all organisms in a population, then log transformed, then averaged over the 50 replicate lineages for each mutation rate. Bars represent standard errors of the mean. B) and C) Fitness trajectories for lineages evolved from F (red) and R (blue)for two different mutation rates and timescales.
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Figure 1: A) Rate of adaptation to a novel 8-task environment as a function of mutation rate, for fragile F (white) and robust R (black) genotypes. Adaptation was measured as the difference in log fitness of the evolved and ancestral organisms. Fitness was first averaged over all organisms in a population, then log transformed, then averaged over the 50 replicate lineages for each mutation rate. Bars represent standard errors of the mean. B) and C) Fitness trajectories for lineages evolved from F (red) and R (blue)for two different mutation rates and timescales.

Mentions: For most mutation rates tested, R was more evolvable than F (Fig. 1A), and the difference increased with mutation rate (ρ = 0.521, 12 d.f., P = 0.046). Indeed, F only showed a better ability to adapt for U ≤ 0.1. At face value, this could lead one to conclude that the benefit of robustness was directly dependent on the mutation rate. However, within the explored parameter range, the rate of evolution increased with mutation rate (Fig. 1A). Therefore, it is possible that robustness conferred an adaptive advantage only in the long-term, and that such advantage would appear to be greater at higher mutation rates. Two observations clearly supported to this possibility. First, as shown in Fig. 1B for U = 0.3, R was less evolvable in the short-term (update < 500), whereas in the long-term (update > 500), the situation was reversed. The same pattern was observed for mutation rates within the range 0.3 – 3 (not shown). Second, at low mutation rates (U ≤ 0.1), the short-term fitness advantage of F was lost after sufficiently long evolutionary times. For instance, for U = 0.03, R evolved higher fitness than F beyond update 6500 (Fig. 1C).


The effect of genetic robustness on evolvability in digital organisms.

Elena SF, Sanjuán R - BMC Evol. Biol. (2008)

A) Rate of adaptation to a novel 8-task environment as a function of mutation rate, for fragile F (white) and robust R (black) genotypes. Adaptation was measured as the difference in log fitness of the evolved and ancestral organisms. Fitness was first averaged over all organisms in a population, then log transformed, then averaged over the 50 replicate lineages for each mutation rate. Bars represent standard errors of the mean. B) and C) Fitness trajectories for lineages evolved from F (red) and R (blue)for two different mutation rates and timescales.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2588588&req=5

Figure 1: A) Rate of adaptation to a novel 8-task environment as a function of mutation rate, for fragile F (white) and robust R (black) genotypes. Adaptation was measured as the difference in log fitness of the evolved and ancestral organisms. Fitness was first averaged over all organisms in a population, then log transformed, then averaged over the 50 replicate lineages for each mutation rate. Bars represent standard errors of the mean. B) and C) Fitness trajectories for lineages evolved from F (red) and R (blue)for two different mutation rates and timescales.
Mentions: For most mutation rates tested, R was more evolvable than F (Fig. 1A), and the difference increased with mutation rate (ρ = 0.521, 12 d.f., P = 0.046). Indeed, F only showed a better ability to adapt for U ≤ 0.1. At face value, this could lead one to conclude that the benefit of robustness was directly dependent on the mutation rate. However, within the explored parameter range, the rate of evolution increased with mutation rate (Fig. 1A). Therefore, it is possible that robustness conferred an adaptive advantage only in the long-term, and that such advantage would appear to be greater at higher mutation rates. Two observations clearly supported to this possibility. First, as shown in Fig. 1B for U = 0.3, R was less evolvable in the short-term (update < 500), whereas in the long-term (update > 500), the situation was reversed. The same pattern was observed for mutation rates within the range 0.3 – 3 (not shown). Second, at low mutation rates (U ≤ 0.1), the short-term fitness advantage of F was lost after sufficiently long evolutionary times. For instance, for U = 0.03, R evolved higher fitness than F beyond update 6500 (Fig. 1C).

Bottom Line: Here, we use the Avida digital evolution platform to explore the effects of genetic robustness on evolvability.For more complex environments, however, results are less conclusive.A likely scenario is that, in the short-term, genetic robustness hampers evolvability because it reduces the intensity of selection, but that, in the long-term, relaxed selection facilitates the accumulation of genetic diversity and thus, promotes evolutionary innovation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Biología Molecular y Celular de Plantas, CSIC-UPV, València, Spain. sfelena@ibmcp.upv.es

ABSTRACT

Background: Recent work has revealed that many biological systems keep functioning in the face of mutations and therefore can be considered genetically robust. However, several issues related to robustness remain poorly understood, such as its implications for evolvability (the ability to produce adaptive evolutionary innovations).

Results: Here, we use the Avida digital evolution platform to explore the effects of genetic robustness on evolvability. First, we obtained digital organisms with varying levels of robustness by evolving them under combinations of mutation rates and population sizes previously shown to select for different levels of robustness. Then, we assessed the ability of these organisms to adapt to novel environments in a variety of experimental conditions. The data consistently support that, for simple environments, genetic robustness fosters long-term evolvability, whereas, in the short-term, robustness is not beneficial for evolvability but may even be a counterproductive trait. For more complex environments, however, results are less conclusive.

Conclusion: The finding that the effect of robustness on evolvability is time-dependent is compatible with previous results obtained using RNA folding algorithms and transcriptional regulation models. A likely scenario is that, in the short-term, genetic robustness hampers evolvability because it reduces the intensity of selection, but that, in the long-term, relaxed selection facilitates the accumulation of genetic diversity and thus, promotes evolutionary innovation.

Show MeSH
Related in: MedlinePlus