Biomedical subjects

Charles Ofria

Publications and source records attributed to Charles Ofria.

8 recordsLinked to original sources

PubMed2006

Ecological specialization and adaptive decay in digital organisms.

The transition from generalist to specialist may entail the loss of unused traits or abilities, resulting in narrow niche breadth. Here we examine the process of specialization in digital organisms--self-replicating computer programs that mutate, adapt, and evolve. Digital organisms obtain energy by performing computations with numbers they input from their environment. We examined the evolutionary trajectory of generalist organisms in an ecologically narrow environment, where only a single computation yielded energy. We determined the extent to which improvements in this one function were associated with losses of other functions, leading to organisms that were highly specialized to perform only one or a few functions. Our results show that as organisms evolved improved performance of the selected function, they often lost the ability to perform other computations, and these losses resulted most often from the accumulation of neutral and deleterious mutations. Beneficial mutations, although relatively rare, were disproportionately likely to cause losses of function, indicating that antagonistic pleiotropy contributed significantly to niche breadth reductions in this system. Occasionally, unused functions were not lost and even increased in performance. Here we find that understanding how the functions were integrated into the genome was crucial to predictions of their maintenance.

Adaptation, Biological↗

PubMed2006

Sexual reproduction reshapes the genetic architecture of digital organisms.

Modularity and epistasis, as well as other aspects of genetic architecture, have emerged as central themes in evolutionary biology. Theory suggests that modularity promotes evolvability, and that aggravating (synergistic) epistasis among deleterious mutations facilitates the evolution of sex. Here, by contrast, we investigate the evolution of different genetic architectures using digital organisms, which are computer programs that self-replicate, mutate, compete and evolve. Specifically, we investigate how genetic architecture is shaped by reproductive mode. We allowed 200 populations of digital organisms to evolve for over 10 000 generations while reproducing either asexually or sexually. For 10 randomly chosen organisms from each population, we constructed and analysed all possible single mutants as well as one million mutants at each mutational distance from 2 to 10. The genomes of sexual organisms were more modular than asexual ones; sites encoding different functional traits had less overlap and sites encoding a particular trait were more tightly clustered. Net directional epistasis was alleviating (antagonistic) in both groups, although the overall strength of this epistasis was weaker in sexual than in asexual organisms. Our results show that sexual reproduction profoundly influences the evolution of the genetic architecture.

Biological Evolution↗

PubMed2004

Adaptive radiation from resource competition in digital organisms.

Species richness often peaks at intermediate productivity and decreases as resources become more or less abundant. The mechanisms that produce this pattern are not completely known, but several previous studies have suggested environmental heterogeneity as a cause. In experiments with evolving digital organisms and populations of fixed size, maximum species richness emerges at intermediate productivity, even in a spatially homogeneous environment, owing to frequency-dependent selection to exploit an influx of mixed resources. A diverse pool of limiting resources is sufficient to cause adaptive radiation, which is manifest by the origin and maintenance of phenotypically and phylogenetically distinct groups of organisms.

Adaptation, Biological↗

PubMed2004

Using Avida to test the effects of natural selection on phylogenetic reconstruction methods.

Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent.

Phylogeny↗

PubMed2004

Avida: a software platform for research in computational evolutionary biology.

Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed.

Biological Evolution↗

PubMed2003

Selective pressures on genomes in molecular evolution.

We describe the evolution of macromolecules as an information transmission process and apply tools from Shannon information theory to it. This allows us to isolate three independent, competing selective pressures that we term compression, transmission, and neutrality selection. The first two affect genome length: the pressure to conserve resources by compressing the code, and the pressure to acquire additional information that improves the channel, increasing the rate of information transmission into each offspring. Noisy transmission channels (replication with mutations) give rise to a third pressure that acts on the actual encoding of information; it maximizes the fraction of mutations that are neutral with respect to the phenotype. This neutrality selection has important implications for the evolution of evolvability. We demonstrate each selective pressure in experiments with digital organisms.

Animals↗

PubMed2003

The evolutionary origin of complex features.

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.

Algorithms↗

PubMed2003

Perspective: Evolution and detection of genetic robustness.

Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness--genetic robustness--and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness: and (3) exploring the implications of genetic robustness for future evolution.

Adaptation, Biological↗