Digital Biology

Peter Wayner writes: "Metaphors drawn from biology have always fascinated computer scientists. No one speaks of subroutines that cp themselves through undocumented remote procedure calls because talk of 'computer viruses' carries all of the portent and weight of polio, anthrax, German Measles and tuberculosis. Invoking these mysterious and deadly images is more colorful than tech speak, even if most of the so-called viruses are closer to the common cold than the black plague. Why use a three-letter acronym when a biological metaphor is available?" Wayner wrote the following review of Peter J. Bentley's book Digital Biology, which may just answer that question. Digital Biology author Peter J. Bentley pages 272 publisher Simon and Shuster rating 7.5 reviewer Peter Wayner ISBN 0-7432-0447-6 summary Does a good job of bridging the analogical gap between the worlds of computers and biology; may not be deep but will probably enlighten readers with an interest in either or both of these fields.

It should come as no surprise that the infatuation is requited because some biologists are just as fascinated with the bits that live in computers. They love to wonder whether the software crosses the line and become a sentient being, whatever that may be. They want to know whether a programmer can play Dr. Frankenstein and create life or at least an indistinguishable imitation. They are entranced with the computer's ability to boil vast amounts of data into a coherent answer and they want to harness this power to solve problems about truly organic creatures.

Peter J. Bentley's new book, Digital Biology is a lively tour through some of the research that joins both of these worlds. It's a quickly paced, colorful examination of how computer scientists and biologists can share metaphors like "the immune system" or "growth." If both groups sit down and compare metaphors, computer scientists may learn something about building robust, self-healing, self-reproducing software from looking at carbon- based creatures while biologists will learn something about creatures by studying them with silicon-based software software.

The book is aimed at the same market that embraced the meme of "Chaos" through reading James Gleick's book. The book is light on equations and heavy on showmanship. In many cases, this is more than satisfying. One description of digital flocks of birds describes how three simple rules can keep the birds floating and swarming with all of the coordinated rolling and swooping. There's no need to invoke numbers or distance measurements to convey what's happening.

At other times, the examples can be so tantalizing that the lack of depth can be a bit frustrating. Bentley promises "The number of different applications that we have successfully used evolution for is immense." To explain this, he offers an example of a coffee table designed by a computer program mixing, matching and cross-breeding varieties. After each generation, the computer cuts some desks apart, creates new combinations and then uses an equation to find the most fit and desirable desks. Eventually, a reasonable candidate emerges. After explaining that genetic algorithms may find patterns of credit card fraud and help us find better jet turbine blades, there's no space to tell us the finer details. We do learn that stunning results can emerge when computer programmers mix the three principles of inheritance, variation and selection. But no book can include everything.

While the book is aimed at a broad market, it does not come with many of the traditional flourishes of journalism. Bentley is research fellow at University College in London, not a newspaper hack who churns out stories for a living. So when he introduces other researchers and colleagues, he doesn't bother with dressing them up with details about their homes, their wives, or the usual chestnuts journalists offer in the hope of humanizing the subjects. The book focuses on the ideas and metaphors themselves and doesn't bother with the window dressing. The names are just incidental markers to give credit and a pointer for further research. Scientists will love the lack of distraction, but casual readers looking for colorful anecdotes about the wacky geniuses in labcoats will need to look elsewhere.

The book, as expected, is generally enthusiastic and heavily invested in the field. Software modeled on biological systems, we are told, will, "detect crime for us, identify faults, ... design new products for us, create art, and compose music."

Despite this partisan flavor, the book shines in the few paragraphs where Bentley pauses to discuss some of the limitations of the systems. "We cannot prove that evolution will find us a good solution-- but it almost invariably does. And we certainly cannot predict that solutions that evolution generates," he notes as a caveat to everyone planning to use genetic programming to solve world peace.

At one point, he discusses one of the principle criticisms of the entire area. After describing flourishing digital forests filled with fractal ferns, problem solving viruses, and swooping swarms of evolving birds and insects, he pauses and offers this quote from another biologist: "Where's the experiment?" He notes that most of these creatures are flights of our imagination untested in the lab against real ferns, viruses or birds. Nor is there any real way to test a fern hypothesis. The digital versions look real, but there's little gritty lab work to establish them as true metaphors for sussing out the secret laws of nature. Is looking real enough? Can you measure verisimilitude? Do any traditional experiments measure anything better than the quality of a simulacrum? Is appearance enough or is it only skin deep? After a pause, though, the book is on to more talk of big payoff and grand promises.

In its heart, the book is more a document that shows evolution of problem solving techniques. If you want to get the sales pitch from the computational biology world, you can turn to this book. When there were no machines, scientists used symbols, algebra, calculus and other mathematics to describe the world. Biologists have long employed differential equations to describe the booms and bust in ecologies of predators and prey. Now that we have computers capable of billions of operations in a second, we don't need the old school of mathematics to provide a closed-form solution. The computers can just simulate the world itself. There's no need to struggle for a set of equations that is both easy-to-solve and appropriate. We can just use little worlds of sims creatures, sim fronds, sim viruses, and sim antibodies.

Bentley's book is an ideal way to learn just how and why some biologists are absolute enraptured with the new powers discovered by these computer simulations of genetics, growth, flocking and other natural phenomenon. These models don't offer the kind of concrete certainty of mathematical models, but there's no denying that something is somehow there. Is it as much a breakthrough as Bentley believes? Well, maybe you the reader can create a genetic experiment to cross fertilize the ideas from the book with the ideas in your experience. After a few generations of thought, perhaps a few generations of beer, an answer might evolve.

Peter Wayner is the author of Free for All, a book on the open source software movement and Disappearing Cryptography , the second edition of a book on steganography expected to appear later this spring. He is also the author of several articles on simulation including studies of studies of the relationship between sex and AIDS , segregation , and the length of baseball games. (Each of these links includes a Java applet so you can run the simulator from your browser.) You can purchase Digital Biology from Fatbrain. Want to see your own review here? Just read the book review guidelines, then use Slashdot's handy submission form.

Comments

Re:Warning

[boaworm]

For those who (inexplicably, through brainwashing or other) don't believe that evolution happens, computers (and their software) provide an unequaled example of evolution in progress

Indeed. For those familiar with Artificial Intelligence, Genetic Algorithms and Genetic Programming, this should already be familiar.. but to enlighten the rest :-)

When talking about AI you have to make a differentiation between the "body" and the "brain". In a computer simlulation you can say that the simulation environment is the body and the "genome" (phenotype) is the brain. Intelligence does not lie in either, but in the cooperation between them. Rather simple.. how much is your brain worth without eyes, arms, legs, nerves etc ? And the other way around.. what to do with your body if you cannot process the data.

So, back to the reflection on the comment above, most people tend to say that humans evolve through the brain, while it is more true to say that the brain and the body coevolve. The same goes for computers, both software and hardware coevolves, keeps getting better and matching each other.

The interesting part here is that if we can understand the body (not that hard, just molecular biology and stuff), then we have one of two keys to human intelligence. That is why biology and computer interaction is so interesting, if we can simulate biology (body) on a computer (body), then we have an increased ability to learn about the brain. A few months ago, an Israeli company successfully performed calculations on human cells. This is the reversed way, using biology as the "body" and computer algorithms as the "brain". Very interesting results, and very promising. This generation or the next should have a fairly good chance of screwing up this planet totally :-)