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?

I'll have some experts from Mayo Clinic contribute some of their expertise to this discussion."

Comments

Speaking as a cyborg

[Anonymous Coward]

(I have an insulin pump) It really hasn't changed my life much yet. Still have to program the thing, refill it, etc. Maybe one day when it's internal and self-regulating, but for now, it's a fancy needle/pda.

equation

[chef_raekwon]

it all boils down to this:

binary + DNA = phi

(try and figure that one out ;)

Speed

[snitty]

The major advantage and disadvantage to biological computing right now is speed. While it can solve some problems much faster than normal computers (due to it's massive parallel computing capabilities), making the DNA to solve the problem, and finding the answer take a long time as well. While both those are speeding up, it will be sometime before it is economically sound to do DNA calculations in anything other than a laboratory environment.

Re:Speed

[FuegoFuerte]

DNA in and of itself can't do calculations (well, that I know of... show me how and I'll believe you). The brain can do massively parallel computations (think facial/object/voice recognition in microseconds, and at the same time). Here's one big problem in taking advantage of that kind of thing (other than ethical issues). Say you have taken some head and hooked it up to a computer. It may recognize things just fine and many times faster than any normal computer, but how to get that information back to the computer? Sure you can interface with specific neurons, etc. but which ones? Do you tap into Wernicke's or Broca's area (the parts responsible for speech/word comprehension)? How do you interpret the signals coming from that area? If you interface anywhere else, you likely wouldn't have any kind of word/name/etc, because Wernicke's is responsible for all speech comprehension (without it there's no giving meaning to the words one hears or reads, and no putting words to actions, feelings, anything else).

So what biological computing has to offer in speed is basically countered by the difficulty in gaining access to the information, unless MAJOR advances are made. And for simple math-type computing problems, biological processing would probably never catch up to what we have now in electronic computers.

Just my 2 bits worth.

GEB chats all about the overlaps

[lysander]

Godel, Escher, Bach talks all about the overlaps and comparisons between biology and computers. In particular, Hofstadter details a one-to-one correspondence from the Central Dogma to Godel's Incompleteness Theorem. It's dense, but it's great stuff.

Jumping the gun here, buddy

[jeeves99]

As one of the chosen few attempting to understand the fundamentals of protein folding, I can say that we are still a long way off from understanding how these "few" 20 amino acids fold into highly-specific structures. There are people with access to super computing centers (ala: UCSD super computing center, IBM's Gene Blue) who still cannot devise a simulation that accurately reproduces biological systems. The amount of atomic and subatomic properties that must be taken into account is just overwhelming. It can take a 64cpu cluster of computers a week to reproduce what nature does in 1 nanosecond!

So how can we restructure our current computing system to a model that is based upon something that we understand only at basic level? We can't. While I agree that a biologically-derived computing architecture could be quite powerful indeed, we are still a LONG way off from the level of understanding needed to even put this idea on the drawing board.

Michael Caudy on biology...

[pen]

In biology the past 50 years have seen both revolutions and evolutions that have brought biology to an even par with physics, which had been the "queen of science" up through the first half of this century.

An answer from a different perspective

[Brown Eggs]

If I can do a slightly different interpretation of the questions being asked - can biology inspire changes in computing? The answer is yes - it already has. Many of our ideas of aritificial intelligence or computer learning have come from neural network-type studies of brain structures. At some point, the equivalent circuit in silicon may precisely reproduce what the neuron is doing. Aside from the time issue (nerve conduction is blazingly fast), you would serve your function staying in silicon.

computational ecology and techniques

[RevAaron]

I'm an aspiring computational ecologist, majoring in biology, minoring in CS. (for the uninformed- ecology != environmentalism or anything of that sort) I'm in Minnesota, although at the other end of the state.

I don't think biology will rewrite CS. It will influence it, for sure, but there isn't anything fundamentally different between a biological solution and a technological one. I think as we learn more of the bigger picture in various biological fields, when we truly understand it, we will integrate that knowledge into applied CS. We've been reading the book for some time now, but we really don't know enough about the subject matter to really apply it.

I think there is a lot of use for biomimicry in computing. I think integration of biological elements into our computers is quite a bit far off and perhaps a bit sci-fi-ish for now, but taking ideas (algorithm would often be an understatement) that work well in biological systems and using them in computing is something we can do now with some success.

Some thoughts

[Otter]

As a molecular biologist who is relatively knowledgeable about computing, here are my impressions:

• The demonstrations of DNA-based computing that have been made are extremely clever and elegant. But they involve spending enormous amounts of money and effort on primer synthesis before and sequencing after the very quick "calculation" step that they hype?
• Who knows? Maybe as genomics technology gets cheaper, DNA-based methods will have practical value in occasional applications that would require enormous brute force for a traditional solution. But I'll be astonished if they become common in general computing.
• No offense, but your bit about "rebellion by the Right Wing" comes across more as ignorant prejudice on your part than as any realistic understanding of the concerns of people unlike you.

Answers to questions

[56ker]

Q. 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.

Biology will (extremely slowly) be integrated into modern day technology. There will be some technology ---> biology transition too. However biology is far more adaptable. It's not a case of rewriting - it's just a case of historical progression.

In answer to your second question - technology concepts, computing etc as they're designed by biology are already in mainstream use eg:-

computer
phone
automobile etc

Biology affecting technology has had less of an effect eg Velcro - however the balance will change over the next few decades. Biotech is already advancing in great strides.

There isn't any definition as such - predicting the future is all guesswork. You can use statistics - all kinds of methods - in the end it comes down to a gut reaction.

Q. How will the nature of computing, and how we perceive it, change due to biology integration?

It'll become easier for biology to use eg:-

handwriting recognition
voice recognition
etc etc etc (all fifth-generation tasks - read up on sixth-generation if you like)

This is due to technology "evolving" to become more link biology though. The change'll happen too slowly to perceive.

Q. More to the point, how much of the theory we learn today may change?

The fundamentals still remain the same - like mathematics though - it just gets more complicated. ;o) If we jumped forward a hundred years - what we know now would be seen as primitive and childlike dabblings at it. Look at how old fashioned 1903 seems now (when cars were "modern technology").

Q. What will be the biggest issue determining the success of the adoption of biology-integrated computing?

Economics. When computers cost millions of dollars only governments and large organisations could afford them. The second problem is marketing (read persuading people they need them). It'd take years though - look at the computer mouse as an example.

Q. Will it be technology factors or will it be societal factors (e.g., rebellion by the Right Wing), or something else?

It'll just happen - although factors will influence how slowly/ quickly certain parts of it do. Technology in the end comes down to ideas + money.

Q. What things must hold true to make the idea succeed?

That we can understand biology & manipulate it to serve us (probably other things too).

Q. And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue?

Not in my opinion - although all technological advances bring ethical dilemnas - who do you sell it to etc? What (out of many) uses do you put it to?

Q. What may be some of the consequences?

A lot of them have already happened or are in the process of happening. ;o)

A society that suffers from greater obesity, global communication, increasing reliance on power production etc etc etc

Cells aren't simply complex computers

[enkidu]

I'd like to confront your basic thesis, that computing and genetic biology are similiar enough to influence each other. Sure the basic building blocks may look similar as you have pointed out, but there is no comparing a modern cell to anything we consider a "computer". We may know some of the basics of how a cell works, but we're still a long way from coding anything in DNA. Genetic code is massively parallel and distributed (and operates in both the genetic and bio-chemical realm simultaneously) and (through evolution) has been both obfuscated and optimized. Most, if not all, of our current state of genetic knowledge consists of "let's break this piece and see what happens" and "this stuff over here looks like that stuff over there" comparison. Call me an old stick-in-the-mud, but having "decoded" the human genome doesn't mean squat until we know what all of the instructions do (and we don't, because we are only looking at the genetic side of things, not the bio-chemical operations which result from the genetic code). Progress will be made, but it will be made through hard slogging over trenches, marshes and mountains, not on a high speed railroad.

I think that biology will push computing into interesting directions, not through application of any biological principals we discover, but through the demands of biological investigation. Biological systems are too interconnected to be adapted to building software or computers. I take that back, the details of biological systems are too interconnected to be adapated to building software or computers, but the gross principals (e.g. the immune system: T-cells, B-cells etc.) will be increasingly copied in software and computer design.

I believe that eventually we will be able to write complex organisms from scratch. These may not be as robust as what nature produces, but will be useful to us in many fields. Starting with the medical and spreading through the agricultural and even industrial area. I dream of trees which produce a sap, which is easily refined into methane or natural gas. But it's going to take much longer than most people seem to think.

Your mistake & my Thesis paper

[blach]

Hi there,

As a medical student with an undergraduate degree in Mathematics, I'm really pleased to see that other scientists are getting excited about the convergence of Mathematics/Compuation and molecular genetics.

First let me correct the slight error in your Ask Slashdot submission: we say that there are three nucleotide bases in an mRNA codon (not DNA codon). If you want a review of how DNA becomes RNA becomes proteins, you can check out the intro to my undergraduate thesis paper (link below).

In fact, I would encourage you to read through my paper in any case, as it may stimulate your thinking or open you up to new areas of bioinformatics research. The paper focuses mostly on a survey of analytic techniques of gene-expression microarrays, but is highly accessible to well-read / intelligent persons (it is light on technical mathematics by design).

Please let me know what you think of it (my email address should be easily inferrable from my website address), and you get a high-five from me if you can find the glaring mathematical error that I didn't get fixed before my defense.

http://blachly.net/james/documents/thesis.html

The best,
James