The Evolutionary Origin of Complex Features

Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA.
Nature (Impact Factor: 41.46). 06/2003; 423(6936):139-44. DOI: 10.1038/nature01568
Source: PubMed
A long-standing challenge to evolutionary theory has been whether it can explain the origin of complex organismal features. We examined this issue using digital organisms--computer programs that self-replicate, mutate, compete and evolve. Populations of digital organisms often evolved the ability to perform complex logic functions requiring the coordinated execution of many genomic instructions. Complex functions evolved by building on simpler functions that had evolved earlier, provided that these were also selectively favoured. However, no particular intermediate stage was essential for evolving complex functions. The first genotypes able to perform complex functions differed from their non-performing parents by only one or two mutations, but differed from the ancestor by many mutations that were also crucial to the new functions. In some cases, mutations that were deleterious when they appeared served as stepping-stones in the evolution of complex features. These findings show how complex functions can originate by random mutation and natural selection.


Available from: Christoph Adami
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    • "" Recurring signaling patterns have been widely studied in gene regulatory networks as well as other real-world complex systems scenarios [18], because of their central role in driving regulatory responses by specific functions [2]. This assumption is based on the expectation that designs with higher modularity have higher adaptability and therefore higher survival rates [19], thus suggesting that modularity can spontaneously arise under changing environments [20], which eventually results in extremely complex systems made of simple basic building blocks [19]. Since CyTRANSFINDER has been designed to support exploratory analysis, it does not rely on expression data. "
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    • "This is a similar calculation in concept to a measure of phenotypic complexity used previously [5] in population genetics. To examine why certain population sizes evolved larger genomes, we examined the " line of descent " (LOD) of the dominant type [73]. An LOD contains every intermediate genotype between the final dominant individual and the ancestral genotype that initialized each population. "
    [Show abstract] [Hide abstract] ABSTRACT: A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large---but not intermediate-sized---populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.
    Preview · Article · Apr 2016
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    • "For regressive optic flow (negative angular velocities ), the average velocity during each event is less than maximum and for extreme angular velocities, it needs to stop for shorter durations to avoid collisions. In order to quantitatively analyze how using regressive motion as a collision cue benefits agents to gain more fitness, we traced this particular agent's evolutionary line of descent (LOD) by following its lineage backward for 20,000 generations mutation by mutation until we reached the random agent that we used to see the initial population (see Lenski et al. 2003 for more details on how to construct evolutionary lines of descent for digitals). Figure 8shows the fitness and the RCC value vs. generation for this agent's LOD. "
    [Show abstract] [Hide abstract] ABSTRACT: Flies that walk in a covered planar arena on straight paths avoid colliding with each other, but which of the two flies stops is not random. High-throughput video observations, coupled with dedicated experiments with controlled robot flies have revealed that flies utilize the type of optic flow on their retina as a determinant of who should stop, a strategy also used by ship captains to determine which of two ships on a collision course should throw engines in reverse. We use digital evolution to test whether this strategy evolves when collision avoidance is the sole penalty. We find that the strategy does indeed evolve in a narrow range of cost/benefit ratios, for experiments in which the "regressive motion" cue is error free. We speculate that these stringent conditions may not be sufficient to evolve the strategy in real flies, pointing perhaps to auxiliary costs and benefits not modeled in our study
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