Slashdot Mirror
Convergence of Biology and Computers?
Pankaj Arora asks: "This summer I am working on both Bioinformatics and Molecular Biology research projects at the Mayo Clinic Rochester. Being an MIS major with a heavy CS background, I've been learning about biochemistry performing polymerase chain reactions (PCRs) and RNA retranslation among other things. I've learned biology works a lot like computers; binary has 1s and 0s, DNA has nucleotides: A, T, C, and G. Binary has 8 bits to a byte, DNA has 3 nucleotides to a codon. Computers and biology seem to have a natural fit; information is encoded and represented 'digitally' in a sense. I was wondering what people thought about the future of biology-based and genetics-based computing due to the immense efficiencies that lie in nature. This has been discussed to an extent here, but there were some specific aspects that I feel are quite important and were not discussed thoroughly, thus I have a few questions to pose to the Slashdot community."
"The aspects I would like discussed are as follows:
- In the long run, will biology rewrite computing or will modern day technology concepts and theory be integrated into biology? If both are true, which will have the greater effect? I understand long run is ambiguous in this question, but Iâ(TM)m interested in all thoughts using any applicable definition.
- Tied to the first question: How will the nature of computing, and how we perceive it, change due to biology integration? More to the point, how much of the theory we learn today may change?
- What will be the biggest issue determining the success of the adoption of biology-integrated computing? Will it be technology factors or will it be societal factors (e.g., rebellion by the Right Wing), or something else? What things must hold true to make the idea succeed?
- And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences?
Take a look at some of the work by Richard Feynmann and Freeman Dyson - the two of them discuss(ed) biology-based computation at great length, and although they were not completely encumbered by modern methods and capabilities, their insights into the theory are pretty valuable. In addition, check out Douglas Hofstadter - I believe that _Metamagical Themas_ had an article or two about this.
-David Barak
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Phi = (1+sqrt(5))/2, about 1.618 This number appears all over the place in nature, and, most interestingly, in the structure of DNA: One rung of the DNA ladder has two golden mean pentagrams, two hexagons, and a golden mean rectangle in the middle, more or less. Also, the helix of the DNA molecule advances by a vertical increment of 1.618 per turn. How's that?
You've asked some very broad questions which delve into both technical and social issues. I'm not much of a social theorist, but I do know something about computing and biotechnology. I'm a postdoc in a lab that studies genomics and biological regulatory networks using computational methods. There are two basic approaches to merge bio and computing: 1) You try to improve computing by using ideas or techniques from bio, and 2) You try to do something interesting in bio by using ideas from computing. Examples of (1) trying to improve computing by using bio would be such things as DNA computing or doing massive combinatorial searches in chemical solutions. In DNA computing, you use various enzymes or chemical agents to modify a DNA string. Think of it as a turing machine acting on a strip, except the strip is now a piece of DNA. Since the DNA strip is modified over the procedure, the "state function" is partially encoded in the data strip, not just internally in the chemical agent. The great advantage of DNA as a computing medium is that there are methods for selectively replicating DNA based on its "state". So you can run your chemical procedure over many different possible DNA sequences simultaneously and then only replicate the particular sequence with the desired state, which gives your answer. At the moment, DNA computing is most useful for search problems. For example, several years ago, the traveling salesman problem was tackled in a DNA system. There is a lot of research now into new operations that can be performed on DNA strings (e.g. ways of doing multiplication or addition using various enzymes and data encodings) to broaden the types of problems that can be tackled. Anyway, this is one way people are using bio to improve computing, broadly defined. In a lot of ways, this isn't really bio anymore. Scientists discovered DNA and enzymes in cells, but now we're just using them as materials for computation. People also use similar search techniques with non-biological molecules. Some similar search and amplification procedures are used to make synthetic organic compounds in drug discovery. DNA, however, is particular useful because it's a long molecule so a lot of operations can be performed on it. As far as how DNA will affect computing in the long run, I don't know. We're still very far from making a dna computer that can achieve anything like what silicon-based systems can. But there could be big technological advances eventually. I don't know of any ways that bio systems have affected our ideas about computing at a software level -- except to perhaps funnel more interest towards massive parallelism. Again, I don't want to imply pessimism about what could be invented. As for (2) how computing could affect biology, this is much less concrete. I'll interpret this to mean that one is trying to program biological systems to do something. i.e. if we give a well-defined instruction set, can we get a cell, organ, or organism to yield a particular output? This to me is just the basic problem of science -- trying to understand how stuff works. We'll be able to "program" cells, organs, or organisms if we understand them as well as we now understand the chemical properties of DNA, or even better, as well as we understand silicon-based semiconductors.