Digital evolution: Insights for biologists

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Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes

The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms

A Comparison of the Effects of Random and Selective Mass Extinctions on Erosion of Evolutionary History in Communities of Digital Organisms

The effect of mass extinctions on phylogenetic diversity and branching history of clades remains poorly understood in paleobiology. We examined the phylogenies of communities of digital organisms undergoing open-ended evolution as we subjected them to instantaneous “pulse” extinctions, choosing survivors at random, and to prolonged “press” extinctions involving a period of low resource availability. We measured age of the phylogenetic root and tree stemminess, and evaluated how branching history of the phylogenetic trees was affected by the extinction treatments. We found that strong random (pulse) and strong selective extinction (press) both left clear long-term signatures in root age distribution and tree stemminess, and eroded deep branching history to a greater degree than did weak extinction and control treatments. The widely-used Pybus-Harvey gamma statistic showed a clear short-term response to extinction and recovery, but differences between treatments diminished over time and did not show a long-term signature. The characteristics of post-extinction phylogenies were often affected as much by the recovery interval as by the extinction episode itself

Evolution of genetic codes

In this thesis, I use analytical and computational techniques to study the development of codes in evolutionary systems. We only know of one instance of such a genetic code in the natural world: our own DNA. However, the results from my work are expected to be universally true for all evolving systems. I use mathematical models and conduct experiments with avida, a software-based research platform for the study of evolution in "digital organisms." This allows me to collect statistically powerful data over evolutionary timescales infeasible in a biological system. In the avida system, Darwinian evolution is implemented on populations of self-replicating computer programs. A typical experiment is seeded with a single ancestor program capable only of reproduction. This ancestor gives rise to an entire population of programs, which adapt to interact with a complex environment, while developing entirely new computational capabilities. I study the process of evolution in this system, taking exact measurements on the underlying genetic codes, and performing tests that would be prohibitively difficult in biological systems. I have focused on the following areas in studying the evolution of genetic codes: Information Theory: I treat the process of reproduction as a noisy channel in which codes are transmitted from the parent's genome to the child. Unlike most channels, however, evolution actively selects for codes received with a higher information content, even if this increased information was introduced via noise. A genetic code consists of information about the environment surrounding the organism. As a population adapts, this information increases, and can be approximated through measuring the reduction of per-nucleotide entropy - in effect sites freeze in place as they code for useful functionality. In the avida system, we know the sequence of all genomes in the population, and new computational genes can be identified as they are formed. The Evolution of Genetic Organization: Organisms incapable of error correction (such as viruses) develop strong code compaction techniques to minimize their target area for mutations, the most prominent of which is overlapping genes. Higher organisms, however, are capable of reducing their mutational load and will explicitly spread out their code, cleanly segregating their genes. I investigate the pressures behind overlapping or segregation of genes, and demonstrate that overlaps have a side effect of drastically reducing the probability of neutral mutations within a gene, and hence hindering continued adaptation. Further, in a changing environment, overlapping genes have a significantly reduced ability to adapt independently. I compare overlapping and singly expressed sections of code in avida, and show a significant (two-fold) difference in the average per-site variation. I also demonstrate the evolutionary pressure for organisms to segregate their genes in a fluctuating environment to improve their adaptive abilities. Evolving Computer Programs: I explore evolution in digital genetic codes, and isolate some of those features of a programming language that promote continuous adaptation. In the biological world evolution gives rise to complex organisms robust to changing situations in their environment. This increase in complexity and "functionality" of the organisms typically generates more stable systems. On the other hand, as computer programs gain complexity, they only become more fragile. If two programs interact in a way not explicitly designed, the results are neither predictable nor reliable. In fact, computer programs often fail even when put to the use for which they were explicitly intended. Computational organisms, however, have a level of robustness more akin to their biological counterparts, not only performing computations, but often doing so in a manner beyond the efficiency that a human programmer could produce. Finally, all of this work is tied together, and future directions for its continuation are explored

The genotype-phenotype map of an evolving digital organism

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable

Data from: Genetically integrated traits and rugged adaptive landscapes in digital organisms

Background: When overlapping sets of genes encode multiple traits, those traits may not be able to evolve independently, resulting in constraints on adaptation. We examined the evolution of genetically integrated traits in digital organisms—self-replicating computer programs that mutate, compete, adapt, and evolve in a virtual world. We assessed whether overlap in the encoding of two traits – here, the ability to perform different logic functions – constrained adaptation. We also examined whether strong opposing selection could separate otherwise entangled traits, allowing them to be independently optimized. Results: Correlated responses were often asymmetric. That is, selection to increase one function produced a correlated response in the other function, while selection to increase the second function caused a complete loss of the ability to perform the first function. Nevertheless, most pairs of genetically integrated traits could be successfully disentangled when opposing selection was applied to break them apart. In an interesting exception to this pattern, the logic function AND evolved counter to its optimum in some populations owing to selection on the EQU function. Moreover, the EQU function showed the strongest response to selection only after it was disentangled from AND, such that the ability to perform AND was lost. Subsequent analyses indicated that selection against AND had altered the local adaptive landscape such that populations could cross what would otherwise have been an adaptive valley and thereby reach a higher fitness peak. Conclusions: Correlated responses to selection can sometimes constrain adaptation. However, in our study, even strongly overlapping genes were usually insufficient to impose long-lasting constraints, given the input of new mutations that fueled selective responses. We also showed that detailed information about the adaptive landscape was useful for predicting the outcome of selection on correlated traits. Finally, our results illustrate the richness of evolutionary dynamics in digital systems and highlight their utility for studying processes thought to be important in biological systems, but which are difficult to investigate in those systems

Data from: Selective press extinctions, but not random pulse extinctions, cause delayed ecological recovery in communities of digital organisms

A key issue concerning recovery from mass extinctions is how extinction and diversification mechanisms affect the recovery process. We evolved communities of digital organisms, subjecting them to instantaneous “pulse” extinctions, choosing survivors at random, or to prolonged “press” extinctions involving a period of low resource availability. Functional activity at low trophic levels recovered faster than at higher levels, with the most extensive delays seen at the top level. Postpress communities generally did not fully recover functional activity in the allotted time, which equaled that of their original diversification. We measured recovery of phenotypic diversity, observing considerable variation in outcomes. Communities subjected to pulse extinctions recovered functional activity and phenotypic diversity substantially faster than when subjected to press extinctions. Follow‐up experiments tested whether organisms with shorter generation times and low functional activity contributed to delayed recovery after press extinctions. The results indicate that adaptation during the press episode degraded the organisms’ ability to reevolve preextinction functionality. There are interesting parallels with patterns from the paleontological record. We suggest that some delayed recoveries from mass extinction may reflect the need to both reevolve biological functions and reconstruct ecological interactions lost during the extinction. Adaptation to conditions during an extended disturbance may hinder subsequent recovery