Virtual
beings boost evolutionary theory
By
Ted Smalley Bowen,
Technology Research NewsAnyone plagued by fruit flies can attest to their speedy proliferation. This trait, while a nuisance in the kitchen, has long been valued in the lab, where drosophila melanogaster usefully plays out genetic inheritance at a pace that quickly reveals generational changes.
Bacteria, whose populations can double in as few as 10 minutes in optimal environments, are faster still. But to really fast-forward the evolutionary process, researchers turn to digital organisms living out micro-lifespans in virtual environments.
Researchers from the California Institute of Technology and Michigan State University have used computer-generated organisms to confirm a theory of evolution known as quasi-species theory.
The researchers were aiming to shed more light on the fundamental evolutionary relationship between mutation and natural selection. Such experiments could flesh out research in fields like artificial life and evolutionary computation.
Mutations are random changes in genetic material. The theory of natural selection, also known as the survival of the fittest, holds that genes that are involved with traits that make animals more fit, or more likely to survive, will tend to increase in a population because the animals who have them are more likely to pass those genes on to the next generation. In this model, the key advantage is the speed with which organisms pass on their genes.
The quasi-species theory refines the strict Darwinian model to predict that when genetic material is affected by high rates of mutation, a group of animals with the highest average reproduction rate will be the most successful in propagating even if that group does not contain the fastest reproducing individual.
To test the theory, the researchers made populations of self-replicating digital organisms that mutated and evolved within a portion of their computer host’s memory, said Claus O. Wilke, a researcher at Caltech. The software organisms' main function was to replicate themselves, Wilke said. "Each program contains a little loop that examines itself, and writes a copy of itself into a new region in memory."
To establish their fitness, the digital organisms performed logical computations on numbers present in their virtual environment.
Within this digital scheme, an organism’s phenotype, meaning its particular group of genes, was “mainly the number and type of computations a program can do,” Wilke said. The pool of genes contained in a group of organisms is the group's genotype.
The environment distinguished among about 80 different logical computations, and rewarded organisms’ computational accomplishments by bestowing on them greater speed. Programs that completed a large number of computations ran faster, and hence out-competed programs that completed fewer computations, said Wilke.
The researchers created forty pairs of populations from 40 different ancestors, and programmed one of each pair to mutate four times faster than the other. In 12 cases, the dominant genotype that evolved at the lower mutation rate replicated more than one and a half times faster than its counterpart.
Working with these 12 pairs over 50 generations, the researchers varied the mutation rates, and found that at higher rates, the genotype with lower individual fitness rates but higher group rates won out.
Though less prolific, genotypes that were clustered in flatter regions of a fitness graph - meaning their traits were more redundant - were better able to withstand the effects of mutation and therefore were more able to perpetuate themselves than groups with one or several extremely fast reproducers, but a lower overall average rate, according to Wilke.
“If you have an extremely optimized organism, it is quite likely that any mutation will seriously impair its replication rate,” Wilke said.
This is because more highly optimized organisms tend to have less redundancy. For example, in a more finely tuned organism with 100 genes, "all the genes are different, in order to get the maximum gain out of the 100 genes. In [a more balanced] organism, there are always several genes that do the same thing, so that the true number of genes is lower," said Wilke.
"If you hit these genes with mutations, the first organism will feel the hit immediately, after the first mutation, whereas in the second organism you can knock out a number of genes before anything serious happens,” Wilke said. “In other words, under high stress, redundancy is more important than optimization.”
The experiments showed that a population with a more balanced distribution of characteristics could out-vie populations with some much fitter individuals when conditions spur greater mutation. The ability of a population to withstand mutation could trump another population that had individuals with as much as a 12-fold replication speed advantage, according to Wilke.
Mutation rates on the order of one per genome per generation, which is similar to those of some types of viruses, were sufficient to tilt the evolutionary field away from fast reproducers, he said.
In the real world, high mutation rates are mainly present in RNA viruses, said Wilke. This is because when a cell divides and duplicates its RNA or DNA, the duplication often includes
mistakes. RNA and DNA molecules contain long strings of base pairs grouped into genes, which store information on how to make proteins. These proteins facilitate many of life's processes.
Most organisms have error correction mechanisms that catch most of those genetic mistakes. "These viruses have such a high mutation rate... because they don't have error correction," Wilke said.
Although the study's results were expected, it does advance the use of digital simulations in understanding evolution, said Lee Spector, an associate professor of computer science and cognitive science at Hampshire College. “Studying evolutionary dynamics by experimenting with digital organisms has enormous potential both for evolutionary theory and for practical applications," Spector said.
It is also difficult. The complexity of even the simplest biological organism is well beyond current digital simulations, which in turn are more complex than analytical models of evolutionary processes, Spector said. “Digital organisms are abstractions and one must be careful in generalizing the results of experiments with them."
The study’s methodology involved some key trade-off’s, said Spector. It used organisms that reproduced asexually, and controlled mutation rates as an external function, which allowed them to systematically vary mutation rate, but sacrificed some realism, he said.
Digital organisms are far removed from real-world counterparts, said Jeffrey Horn, an assistant professor of mathematics and computer science at Northern Michigan University. "However, they do allow us to exactly control the environment, which is impossible with any real organisms. This control allows us to vary any single environmental variable, to fully analyze the dynamics in every detail [and] to run as many independent trials as we like." The drawback is that it is difficult to tell if the relationships the research can isolate and spotlight this way are actually important in nature, he said.
There's also the possibility that effects in digital populations are not due to the general dynamics of evolution, but result "from very specific choices that we have made in the design of our system,” Wilke said.
This can be compensated for by posing questions at a more general level that do not depend on those microscopic details, said Wilke. The Caltech-Michigan State researchers did this by looking only at broad categories of replication rate and robustness, and staying away from detailed conclusions like how the organisms came to replicate at a certain rate, according to Wilke.
There are some useful conclusions that can be drawn from the research, said Spector. “The moral... is that robust replicators will sometimes out-compete more efficient, but less robust replicators, particularly in disruptive environments,” he said. “So a computer worm that makes only a few copies of itself, but does so in the context of a wide range of operating systems and security measures, may spread faster than a worm that makes hundreds of copies of itself but fails in many environments.”
"The research shows that we must be careful not to turn mutation rates up too high, or we might evolve robust but suboptimal answers," said Horn. "Conversely, for artificial life simulations [like] simulated worlds [and] computer games, we might be encouraged to try very high mutation rights in the hopes of co-evolving multiple, related, robust species whose evolvability is more important than their objective fit to the environment," he said.
The researchers next steps include a study of the same evolutionary effect observed in digitally simulated RNA sequences. They also plan to look at the evolution of more complex features, according to Wilke. Experiments that yield significant results might lead to more focused tests on biological organisms, he said.
While there's no clear-cut commercial application for the digital evolutionary testbed, this type of experimentation is likely to increase in the next decade, Wilke said. "I expect a significantly increased number of studies involving digital organisms over the next five to ten years."
Wilke’s research colleagues were Jia Lan Wang, Charles Ofria, and Christoph Adami at Caltech, and Richard E. Lenski at Michgan State. They published the research in the July 19, 2001 issue of the journal Nature. The research was funded by the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA).
Timeline: 5-10 years
Funding: Government
TRN Categories: Artificial Life and Evolutionary Computing
Story Type: News
Related Elements: Technical paper, “Evolution of Digital Organisms at High Mutation Rates Leads to Survival of the Flattest” Nature, July 19, 2001.
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October 10, 2001
Page One
Tiny tubes make logic circuits
Mobile radios make intranet
Quantum code splits secrets
Computer tells convincing story
Virtual beings boost evolutionary theory
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