Posted on 05/08/2003 10:11:06 AM PDT by Nebullis
Arlington, Va.—If the evolution of complex organisms were a road trip, then the simple country drives are what get you there. And sometimes even potholes along the way are important.
An interdisciplinary team of scientists at Michigan State University and the California Institute of Technology, with the help of powerful computers, has used a kind of artificial life, or ALife, to create a road map detailing the evolution of complex organisms, an old problem in biology.
In an article in the May 8 issue of the international journal Nature, Richard Lenski, Charles Ofria, Robert Pennock, and Christoph Adami report that the path to complex organisms is paved with a long series of simple functions, each unremarkable if viewed in isolation. "This project addresses a fundamental criticism of the theory of evolution, how complex functions arise from mutation and natural selection," said Sam Scheiner, program director in the division of environmental biology at the National Science Foundation (NSF), which funded the research through its Biocomplexity in the Environment initiative. "These simulations will help direct research on living systems and will provide understanding of the origins of biocomplexity."
Some mutations that cause damage in the short term ultimately become a positive force in the genetic pedigree of a complex organism. "The little things, they definitely count," said Lenski of Michigan State, the paper's lead author. "Our work allowed us to see how the most complex functions are built up from simpler and simpler functions. We also saw that some mutations looked like bad events when they happened, but turned out to be really important for the evolution of the population over a long period of time."
In the key phrase, "a long period of time," lies the magic of ALife. Lenski teamed up with Adami, a scientist at Caltech's Jet Propulsion Laboratory and Ofria, a Michigan State computer scientist, to further explore ALife.
Pennock, a Michigan State philosopher, joined the team to study an artificial world inside a computer, a world in which computer programs take the place of living organisms. These computer programs go forth and multiply, they mutate and they adapt by natural selection.
The program, called Avida, is an artificial petri dish in which organisms not only reproduce, but also perform mathematical calculations to obtain rewards. Their reward is more computer time that they can use for making copies of themselves. Avida randomly adds mutations to the copies, thus spurring natural selection and evolution. The research team watched how these "bugs" adapted and evolved in different environments inside their artificial world.
Avida is the biologist's race car - a really souped up one. To watch the evolution of most living organisms would require thousands of years – without blinking. The digital bugs evolve at lightening speed, and they leave tracks for scientists to study.
"The cool thing is that we can trace the line of descent," Lenski said. "Out of a big population of organisms you can work back to see the pivotal mutations that really mattered during the evolutionary history of the population. The human mind can't sort through so much data, but we developed a tool to find these pivotal events."
There are no missing links with this technology.
Evolutionary theory sometimes struggles to explain the most complex features of organisms. Lenski uses the human eye as an example. It's obviously used for seeing, and it has all sorts of parts - like a lens that can be focused at different distances - that make it well suited for that use. But how did something so complicated as the eye come to be?
Since Charles Darwin, biologists have concluded that such features must have arisen through lots of intermediates and, moreover, that these intermediate structures may once have served different functions from what we see today. The crystalline proteins that make up the lens of the eye, for example, are related to those that serve enzymatic functions unrelated to vision. So, the theory goes, evolution borrowed an existing protein and used it for a new function.
"Over time," Lenski said, "an old structure could be tweaked here and there to improve it for its new function, and that's a lot easier than inventing something entirely new."
That's where ALife sheds light.
"Darwinian evolution is a process that doesn't specify exactly how the evolving information is coded," says Adami, who leads the Digital Life Laboratory at Caltech. "It affects DNA and computer code in much the same way, which allows us to study evolution in this electronic medium."
Many computer scientists and engineers are now using processes based on principles of genetics and evolution to solve complex problems, design working robots, and more. Ofria says that "we can then apply these concepts when trying to decide how best to solve computational problems."
"Evolutionary design," says Pennock, "can often solve problems better than we can using our own intelligence."
You presume to know more about the implimentation details than I do. I expect there is not a "list" of mutations. Rather, an algorithmic mechanism for mutations which, unsurprisingly, employs somewhere at it's heart, a random number generator to decide which direction to tweak a mutation, not from a list, but from a primative understanding of what it means, in detail, to mutate.
There have been experiments that have proceeded as you are imagining, but they weren't nearly as interesting. The presumption that you know what all the acceptable mutations might be ahead of time, being the simplifying assumption that makes such a list possible to contemplate.
Of course they do. Plain as the nose on your face. Just as they both differ dramatically from octopi and bats. Plain as the nose on your face.
What these evolutionists do is they point at the bones and go, "you see this little ridge here? This other animal has a little ridge over here, so they must be related!" or "Look, they both have holes in somewhat close parts of their head, so they must have a common ancestor!".
Yes. Just as stellar evolutionists do when they point out the similarities of star's characteristics and map them on the Hertzsprung-Russell diagram, and predict their fates therefrom. Just as geologists do when they tell you stories about where to find oil from gazing at a few core samplings and some surface rocks.
Apparently, your problem is with inductive reasoning--which somehow doesn't surprise me.
- Brockton Paloma, 1914 from "My Time Alone With Piquo"
True?
What would you accept as proof?
- Ted Maximil, 1954 film "Beware of Alternate Earth, Part 15"
I am trying to understand the program based on the article. I have no knowledge of the program beyond what is stated there. As I understand the article, simpler functions mutate into more complex functions in the program. The article mentions these "simple functions" in several places without going into detail. I would think the programmers had to program some functionality into those simpler functions and also create a routine that generates the mutation or allows the mutation to occur (yielding the "random mutations"). That's what I'm trying to understand.
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