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?
What needs to happen is a happy medium, biology chaning the face of computing, and computing doing the same for biology. Advances will be gained in both this way.
"biology rewrite computing or will modern day technology concepts and theory be integrated into biology"
.eom
Modern day technology concepts and biology will both one day become so advanced that they are are... indistinguishable.
(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.
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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it all boils down to this:
;)
binary + DNA = phi
(try and figure that one out
We're like rats, in some experiment! -- George Costanza
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.
Modular Redundancy--Because 4 out of 5 Nodes agree
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.
GET YOUR WEAPONS READY! --DR.LIGHT
You must be new around here...
In the long run, will biology rewrite computing or will modern day technology concepts and theory be integrated into biology?
Where did all that modern day technology come from? Biology has already written computing as our biology lead to our intelligence that lead to our computers.
If we can finally assimilate that pesky planet at sector 001, then we will consider ourselves to be a success.
I don't remember the artcile, or the location of the reference [ http://www.nature.com/nsu/000113/000113-10.html thanks google]...
Well anyways, the travelling salesman problem was solved using specially crafted DNA sequences.
Actually, there has already been a large scale integration of biology and computing. You can see a summary of the work here. In fact they've already done a follow up experiment, and I here that there's a third project in the works.
Binary has 8 bits to a byte, DNA has 3 nucleotides to a codon.
I got a big codon while I was reading the linux kernel source.
Compared to war, all other forms of human endeavor shrink to insignificance. God, how I love it. - Gen. George Patton
As someone who works in bioinformatics research coming from the computer side I think your mixing issues.
;-)
There's using computing to forward and analyize biological questions, that's one field. (and the one I'm in)
The other is using biology to build things like nanotech and other molecular circuitry.
Both of these are using one as a tool to forward the other, it's not a straight integration like putting chocolate and peanut butter together, and never will be.
Each field will simply adapt and use tools from other fields. Just as in molecular biology physics and chemistry concepts are used to help understand biological mechanisms. Don't look for a Unifying Theory for all these fields.
Anyhow, that's my opinion, my boss will probably say I'm completely wrong
It's easy to see why DNA is digital; it means that copies can be made with 100% fidelity. You don't want random mutations every time a cell divides.
This forces some processes to be essentially digital, but most of biology is an unbelieveably messy analogue nightmare for anybody trying to figure out what's going on.
I'd take a look at Laura Landweber's group at Princeton. They've put out a bunch of papers about DNA computing. I also seem to recall that Nature, Science and PNAS may have had some articles within the past couple of months (certainly less than a year).
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.
The first things that come to mind is, "What time frames are you speaking in for this technology?" and "What application are you talking about?" Each of these are very important.
If you are talking raw number crunching, it might end up having some problems with competition with rival technologies. The High Productivity Computational Systems Effort @ DAPRA is intended to bridge the gap between current supercomputers and quantum computers in capability. If the realistic xpectations for quantum computers are realized, and not the hype, then it might end up making the biological tech a case of an 'also ran' much like gallium arsenide seems to have become. Unless there is something that biotech processors do better than the traditional architectures and the projected quantums, then it might remain a lab curiousity.
On the other hand, if you mean something else, like revolutionary computer-human interfaces, or AI work, or something I'm not thinking of, then we might see something generated from this indeed.
If you could be more specific about what you have intended this technology applied to...
Do you know why the road less traveled by is littered with the bones of the unwary?
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.
Trolling logged-in, at +2, and a subscriber! Truly worthy of the admiration of the entire trolling community!
I for one, look forward to the days when Microsoft try running their wonderful code in my DNA. I mean, imagine all the potential:
"I'm sorry, your DNA has just crashed. You're experiencing the blue goop of death."
Of course, all the geeks would run their DNA on Linux. They'd be capable of doing many things faster, they'd live forever compared to their microsoft bretherin and the vast majority of society would never, ever, want to interact with them. So no change there then.
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.
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.
Working toward a usable PDA environment in the spirit of Newton OS: Dynapad
All this talk brings to mind new meaning for the term "computer virus".
What I'm listening to now on Pandora...
The ARMY has live soldiers and dead soldiers
Binary has 8 bits to a byte, DNA has 3 nucleotides to a codon.
The ARMY has 8 to 10 soldiers to a squad.
Computers and biology seem to have a natural fit;
The ARMY also seems to fit the computer model using the same criteria. Does that make it a computer?
-- Thou hast strayed far from the path of the Avatar.
I work there too this summer as does my brother. I'd bet you're in Guggenheim. We should have lunch some time.
I think there will be great crossover between the two fields. In many unforseen ways.
DNA computing will probably be a ways off, because prepairing it seems to take so long that its barely worth tring today, but some day it may be useful. We would need a much faster and more automatic way of using DNA than we do now.
However, cyborgs, implants, and using whole cells connected to chips may be here sooner. For example I've heard of using cells connected to chips as detectors, for things like chemical weapons.
On the reverse side there will be biological techiniques used in computing. Just look at artificail neural nets, and genetic algorithms. The neural nets people may give up on, but I've heard good things about genetic algorithms.
Um, no.
Computing can potentially take place on a biological platform. There's already been some work on this. Very preliminary, but you have to start somewhere.
And DNA is an encoding system. It stores information. RNA copies it.
(high powered robo-wang), I agree also.
The mere speed and size of biological communication and information storage gives modern computing technology much to reach for. We are talking about nanometer size particles that store an incomparable amount of information, and when something needs to be done it isn't more than a phosphorylation (occurring in what, 10^-14 seconds) seconds away.
Everything technology attempts to mimic everything natural, like your monitor for example. It is a visual representation of the world and the information therein. The ultimate monitor would be in fact one that lays over your whole visual system giving you endless possiblities as far as resolution and "frames per second" type things.
A keyboard is just the liason between your brain and the computer. If your brain talked directly to your computer, now that would be fast and much less labor intensive. That is what we are all aiming for, thus the "wearable computers" and things like Dasher (pretty cool IMO). Input needs to be faster because our brains are fast.
Technology is not morally or societally neutral, despite what we would like to think. A very simple example of this is the car: cars, in order to maximize their utility, require a vast network of roads, parking spaces, and gas stations. This network is expensive to society for environmental reasons and has definite social and economic effects (such as time lost in commuting and traffic jams). These are unavoidable if we wish to use the technology of cars.
I have an essay in progress on this topic: The Analysis of Technologies - its got some stuff that is quite out of date since I started working on the essay eight years ago :-)
A really great book on the subject of analyzing the future effects of technology is "In the Absense of the Sacred" by Jerry Mander. This book is very much slanted politically to the "small/simple is beautiful" outlook, but provides a very substantial wealth of logical arguments and academic studies to demonstrate some of the necessary principle of analyzing technologies.
As for your specific questions, one obvious effect will be that in our commercial environment, not everyone will have equal access to the benefits that may be provided by the integration of computational and biological technologies.
Since it will not be genetic engineering in the "traditional" sense, this technology may be used as a backdoor for creating designer babies without actually modifying a zygote's genetic material.
Helping with organizational effectiveness is our job.
The way these questions are structured, it really sounds like this dude is fishing for answers on a essay he needs to write for a summer course.
No one answer him! Make him figure this stuff out for himself!
Who wants to put some money down on a wager that the first significant merger of biology and computers will be accomplished by the pr0n industry?
"Ask not what your country can do for you." --John F. Kennedy
We already know about the convergence of computing and biology. ;-)
I'd rather be a conservative nutjob than a liberal with no nuts and no job.
The biggest problem right now is that the technology isn't there yet. Simply decoding a single strand of DNA is a long process fraught with the use of various enzymes and chemicals to find out what the actual composition is. If and when we develop better ways of dealing with bio material (nano-bots?) biological computers could be a very good choice. The advantages in parallel computing alone would completely revolutionize computing as we know it.
Of course, there is a downside. Massive parallelism means that programming will become orders of magnatude more difficult. People today can barely wrap their heads around out of order instructions and code that works well with superscaler architectures. What happens when we increase this complexity by a million fold?! I'm thinking that bio computing could produce some rather interesting advances in the way we communicate/program computers.
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Well, bioinformatics is certainly the hot field right now (for those who want a little background I wrote a little introduction to bioinformatics here), (although it is biased towards Macs in bioninformatics).
To answer part of your question, there are many parallels between biology and computers, however some biological systems are much more complex and can only be modeled to a limited extent right now. Some systems are more easily examined in terms of circuitry, but we are still only half way to knowing what the components are and how they are wired (in the retina for example). Eventually, there will be hybrid bionic systems that can function as computers for certain tasks, but we are a long ways away from understanding all of the molecular paths as well. So the question in this case really becomes, at what level are you talking of integration? One could examine the molecular level using DNA and its associated proteins as a computational tool, or you could talk about integrating things at the systems level such as with a hybrid bionic/biological vision replacement device.
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Your questions are kind of murky.
The study of DNA uses computers. It's a lot
of data, there's a lot of number crunching.
Humans using DNA as a substitute for computers
for storing data is a cute, science fiction idea.
Don't sell your seagate stock and go long on
Amgen based on this.
Yes, DNA encodes data. DNA make RNA which
makes various proteins. Unlike computer data,
this DNA sequencing does not generally change.
Changes are called "evolution" and "cancer".
Only rarely do changes produce good results.
Are computers and the living CELL combining or
worthy of study under a unified discipline?
No. Some of the analogies are cute, but they
are two different "beasts".
and we know there no corrolation between chemistry, physics, logic and mathmatics.
The Kruger Dunning explains most post on
Haven't any of you seen Star Trek? Once technology and biology merge individuality will be a thing of the past. In all honestly I am in the IT field and love new technology, but I also have a fear of privacy and individuality being wiped out by it. If the current technology trends continue it is only a matter of time before we have chips implanted in us that will handle all financial matters and keep track of our locations at all times. Biotechnology can due more good then we can currently imagine, but we need to be very careful with how it is used.
Computers, at least in their present form, and biology are very fundamentally different. That long-term biological information is stored "digitally" in DNA is hardly enough to draw a parallel between computers and biology. Computers process information in digital form using very fast simple operations. In live beings information is processed by means of much more complex slower, chemical reactions, and I'd bet 99% of the time information is stored in more analogic ways such as energy levels, mollecular "shapes", etc. No foreseeable convergence between computers and biology by the moment.
-- Repeat with me: "There is no right to profits".
Quantum Computing is also in the queue.u el/comp/ comp.html
http://www.informatics.bangor.ac.uk/~schm
It looks like we are entering an Information/Biology renaissance.
This will be exciting.
Regarding your first question, some applications combining our knowledge of computing in biology is already being considered. See the following link DNA Computing
If an DNA program that checks the stock ticker happens to be a really deadly virus...
course the pr0n industry will love the crossover applications...
meh
Life is an information processing machine, this should be obvious from the fact that it starts and ends with genetics. Biology is the expression of life...
It's the timescale which throws us off. We are not used to seeing problems solved by the application of time rather than brute force. And yet many computational problems also evolve solutions slowly... take the development of simple yet subtle technologies like XML. Decades to arrive at a nice design, with something very similar to biological competition selecting mutations for success.
Sig for sale or rent. One previous user. Inquire within.
Am I the only one who was reminded of that old 'toon, 'Mask'? "Part man, part machine!"
-
ping -f 255.255.255.255 # if only
...Haven't any of you seen Star Trek?...
/. not fratboyzRoXort[dot]com...these ppl dress the part, speak Klingon, and want to bone Beverly Crusher...
;-)
Dude, you're on
...oh wait, i'm guilty of the third one too...
What will be the biggest issue determining the success of the adoption of biology-integrated computing?
Well, lifeforms have certain weaknesses that rocks and electrons alone do not. Among them are:
-A lifespan
-Virus vulnerability (no pun intended)
-Nutrition requirements
(your typical cell needs things that are harder
to mass-transport than electrons. Water comes to mind)
-Amalcon
The first hour or two of disassembling was figuring out where the code was, and where the data was.
The next day or so of poring over those printouts were spent mapping out where the entry/exit points for subroutines were.
I got to the point where I could guess where game graphics were, just by looking for oddly repeating patterns in the "data" areas. (Yup, in binary, those 8-byte sequences make up the bitmaps for the characters "A", "B", "C"...")
"Oh, XX AA XX BB XX CC, somewhere near XXDD in memory space. Must be a list of pointers to something."
"Oh, XXAA, XXBB and XXCC all start with the same byte, and that byte is XXAA minus XXBB (or XXBB minus XXCC). Now I know how big each element in the structure is."
And so on. The first day or two of hacking would result in me figuring out about 10% of what the data was for.
The other 90% was the hard part, typically requiring running some coke, poking at the data, and running the code again to see what changed. "Maze wall moved here, then things crashed when I tried to walk through it."
Sure, 99% of our genome might be junk. There were plenty of areas of address space that contained "data" that was never accessed, even with the tight code written in the 8-bit days.
But when I found a string of bytes I didn't understand, the working assumption that usually went better for me was that "I don't know what this stuff does", not "these bytes are random".
I'll bet that 90% of the genome is never executed nor referenced as data. (Evolution's a messier programmer, and there's 4.5 billion years of cruft!) But I'll bet that a lot of that "junk" is just code we haven't reverse-engineered yet.
Ramblings over - to the poster, all of the ideas in this post are probably ancient history (and poorly-written at that - you can tell I have no bio background), but it's nice to see I'm not completely off my rocker.
I went the CS route because when they taught biology in high school, it was seen as preparation for "become a doctor". Nothing wrong with doctors, but I was interested in more interested in hacking and figured it would be a long time, if ever, before we could manipulate DNA the way I could manipulate bits on a machine. (I've been pleasantly surprised with the way things turned out, though! :)
CS grads are a dime a dozen in the job market; I like my job, but career-wise, the field's been played out. If you're about to go into college, and especially if you like to reverse-engineer stuff "because it's fun", get into bioinformatics, computational biology, and do your CS as a minor. At least, that's what I'd do if I were gonna start over.
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.
;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").
;o)
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.
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.
A society that suffers from greater obesity, global communication, increasing reliance on power production etc etc etc
Video Game cheats, hints a
It is by the juice of saphoo that the thoughts acquire speed, the lips acquire stains, the stains become a warning, it is by will alone I set my mind in motion.
An excellent book discussing some of the isomorphisms between computers and biology is Godel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter. I can't recommend it highly enough.
I don't know half of you half as well as I should like, and I like less than half of you half as well as you deserve. BB
Imagine if we could program insects so that a swarm of ants behaved in an unnatural way that ended up being beneficial to its creators. As long as you could engineer a way for the programs to propagate to the new ants, you'd have a self-propagating supply of robots. Imagine if they were set to build structure instead of their usual hills. Or they could gather food (allright, that would be a little gross).
I think nanotech is headed in this direction anyway, and one of the main limits (for a while at least) is going to be the difficultly of making enough little critters. Self replicating silicon is a pain in the ass. Self replicating carbon is pretty much out of control, and provides an easy platform to piggyback on.
I have to think that both technologies will come to a point where they can't advance without the other, at least in the medium-term. We know (or think we know) that silicon will reach barriers it can't overcome. And at this point, we don't have a way to create complex biological computers without using existing complex organisms and therefore shooting ourselves in the foot politically. Before real-world interfaces to biological computers can be developed, we need an efficient way to interface with the biology at a low level. Traditional computers will have to provide this for us.
We may even see a true, permanent mesh of the technologies. Silicon is extremely good at some things (communications; providing an interface to mechanical items -- keyboards & mice, monitors, speakers, solar panels, servos, etc.), while it's hard to imagine really good natural language processing, learning, and nonlinear problem solving, much less a modicum of emotion to enhance usability, occuring without biology.
Who knows? My prediction is as follows:
Just a little fantastic speculation...
--Jasin Natael
True science means that when you re-evaluate the evidence, you re-evaluate your faith.
Nextel just sent every customer a SMS text message to the phones advertising a national direct connect plan for $XX/month. WTF? Every damn Nextel phone in the fucking office is beeping like crazy. What a bunch of assholes.
We didn't understand the evolved FPGA pattern implementing an XOR either, although we think of it as a bit pattern.
By the way, DNA isn't recombined with 100% fidelity. Mostly, things work ok, but things do mutate once in a while, just as they do when you make analogue recordings. This leads me to think your "digital tape recorder" analogy isn't a very good model - "DNA bits" can and do flip.
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.
There is no trap so deadly as the trap you set for yourself
-Raymond Chandler, The Long Goodbye
Seeing this discussion reminded me of a very interesting look into biology, drawing many parallels to technology.
As many similarities exist, the next question is if everything is purely coincidental and we are looking for similarities or if our technology was subconsciously built to model nature. The later would lead us to the conclusion that we would be able to use advances in technology to improve on nature.
All of which calls into question the ethical and moral decisions that come with mucking with nature.
I think it would be great if, once biological processes are understood better, they could be applied to IT to make systems faster or more efficient, etc...Our biological systems have evolved over billions of years; assuming Darwin was correct, it's seems pretty safe to say that they must be incredibly robust and efficient. Why not learn from them?
The problem I have with the reverse (computers -> biology) is that we won't know how it will affect the evolution of our species. (Just take a look around you sometime to see just how polluted our gene-pool already appears to be....)
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
To hell the luddites. Hack the genome.
With apologies to Steven Levy:
1) Access to the genome, and anything which might teach you something about the way life works, should be unlimited and total. Always yield to the Hands-On Imperative.
2) All information should be free.
3) Mistrust authority- promote de-centralization.
4) Hackers should be judged by their Hacking, not bogus criteria such as degrees, age, race, or position.
5) You can create art, beauty and even life by hacking DNA.
6) Genetic hacking can change your life for the better.
Genetic based computing certainly sounds nice.. It'd be lovely to shake some powder over a plate of agar and take a nap while my hundred teraflop computer gets itself all sored out. But I don't see such a technology coming into being in the next three decades if ever. Nature simply doesn't work that way. The DNA replication process, as best I understand, is sloppy (nature wants the odd mutation, computing doesn't), slow, and really doesn't "compute" in the traditional sense -- it replicates. There might be a handful of problems that could be approached using some form of genetic algorithm but I think the lure of the whole thing is misplaced. Yes, genes are cool and we'll be able to do amazing things once we figure them out a little more but there's little reason to believe they'll do anything but suck as calculators. A related field, proteomics, holds more promise IMHO. I've no doubt that material sciences as a whole will produce mediums with far greater potential. Imagine a crystal, for instance, that finds the next prime number as it grows. Genes and such aside, silicon based computers, like my 25 node linux cluster that can churn through a gig of genetic data per second, will make far more sense.
CommentBot 0.7a running with args "-module irritate,disagree -target random"
--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.--
The likelihood that "modern day technology concepts and theory" will be integrated into biology seems unlikely to me, but I think you're really asking the following question-- Will we be able to use technology to design life, based on our ability to manipulate the code? I suspect so, though it will never be possible to escape the reality that what we would be doing was more biology than computer science. For the first part, will biology affect technology? Definitely. Rewrite it completely? I doubt it. It's more likely that biological computing systems will work well for certain tasks but not others (based on factors like complexities or huge numbers of variables).
--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?--
I don't think anyone can even begin to answer this question, because the possibilites are practically infinite. If I had to guess though, I would say this -- most computer theory (I think not all, but I'm not sure) these days is based solidly on the binary system you mention, things are either one thing or another, a 1 or a 0. I think biological systems may someday be able to solve problems based on "fuzzier" logic, simply because the complexity that could be managed by DNA is very large.
--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?--
Like most things, I think the biggest issue determining the adoption of biology-integrated computing will be the rise of a company that can make a viable product that serves people either better than before or in a new way. Reality has shown that no matter how good an idea is, there are many other factors that can govern what is adopted and what isn't, just look at Betamax vs. VCR. Everybody knows betamax was better, but it didn't matter in the long run.
--And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences?--
As in all things, there is nothing inherently wrong with pursuing knowledge. It's how we acquire that knowledge and what we do with it that can lead to moral dilemmas.
And the obligatory clueless newbie question "What version of Windows(tm)(C) does it run?"
Who would win this election: Andrew Weiner vs Andrew Weiner's weiner.
RNA seems to only copy DNA, but there are some theories that RNA actually came first.
You raise some intriguing ideas but the unfortunate reality is that currently any computing using a biological (DNA based) system can only be used to solve a very specific subset of the total types of problems Computing Science has been tasked with addressing today. You are not going to be running Doom 4 on a massively parallel home DNA computer. But you can solve large recursive problems with a very carefully set up inital setup (Read weeks to months) using nucletides acting as symbols in your algorithm. Current computing maps to biological computing at an abstract level but the time required and the level of knowledge required to pull it off make prohibitively difficult. Maybe it will be ready for Doom 6.
Have you ever stood and
stared at it, Morpheus?
Marveled at its beauty.
Its genius. Billions of
people just living out
their lives... oblivious.
Did you know that the
first Matrix was designed
to be a perfect human
world? Where none suffered,
where everyone would be
happy. It was a disaster.
No one would accept the
program. Entire crops
were lost.
Some believed we lacked
the programming language
to describe your perfect
world. But I believe that,
as a species, human beings
define their reality
through suffering and
misery.
The perfect world was a
dream that your primitive
cerebrum kept trying to
wake up from. Which is
why the Matrix was
redesigned to this: the
peak of your civilization.
I say 'your civilization'
because as soon as we
started thinking for you,
it really became our
civilization, which is, of
course, what this is all
about.
Evolution, Morpheus.
Evolution.
Like the dinosaur. Look
out that window. You had
your time.
The future is our world,
Morpheus. The future is
our time.
stupid question, but isn't quantum computing predicated on a 3 state model analogous to the 3 nucleotide codons? and wouldn't this be relevant?
ed
First, there is a difference between bioinforamatics and DNA computing. Bioinformatics is the application of computer algorithms and statistical techniques to figure out how a biological system works. DNA computing is more of an engineering project, since you are addapting DNA to do your computational bidding (e.g. a DNA based microprocessor)
I my self am in the field of bioinformatics/molecular biology with my primary interest being in RNA regulation and regulatory elements. I am trying to find and figure out how RNA regulation works in model systems.
Now for your questions...
>In the long run, will biology rewrite computing or will modern day technology concepts and theory be integrated into biology?
Both will happen...
>If both are true, which will have the greater effect?
I don't know about biology rewriting comuting. First, yes DNA encodes information 'like' binary 1's and 0's, but we are still figuring out the system works. We know how to find some genes by just looking at the sequences, but we still have the problems with predicting genes in a sequence (e.g. gene splicing, post transciptional events, etc.
I think it would be more sane to use the modern day technological concepts and theory, but with an emphasis on parallel computing.
>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?
Well we can have those clean computers powered by photosynthesis... ok, all kidding aside, it change computing for those tasks DNA would excel at: A DNA computer is a type of non-deterministic computer. We have to overcome some of the problems imposed by DNA... its a chemical that is in an aqueous environment that tends to mutate over time; also the DNA computers I have seen work in a test tube, and you have to sequence it to get a result. That should hopefully change in time.
>More to the point, how much of the theory we learn today may change?
In biology - a sh*t load most likely; like I said above, we are still trying to understand biological systems and how they interact with each other, including DNA and how it codes for life.
>What will be the biggest issue determining the success of the adoption of biology-integrated computing?
Get it out of the test tube first... place it on a chip, like a microprocessor. Also the energy source... I don't want to share my doritos with my desktop...
>Will it be technology factors or will it be societal factors (e.g., rebellion by the Right Wing), or something else?
Don't like the right wing, eh? Well as a card carrying member of the vast right wing conspiracy, you have just as much to worry about from the left... those environuts who think we are tampering with nature (like we haven't been doing that for the last 10000+ years (e.g agriculture). Both extremes muzzel science... get used to it.
If we start to integrate computers into our selves... yeah I think society will have some issues to face about what it means to be human. (I'll go with David Hume with this gem "I'm human because my parents were human")
>What things must hold true to make the idea succeed?
1. Perfect DNA computing
2.
3. Profit -- of course!
Ok, seriously -- there need to be interest in the scientific community, we need to figure out how DNA works in living beings... how it encodes all its data (and how about that junk DNA?). We need to get it on a chip (not a microarray chip... some times called DNA chips). And there needs to be a profit motive.
>And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences?
Hell no! But if you are interested in DNA computing, the bioinformatic
Accentuate the positive, don't waste your mod points on the negative.
They figured out the code segments in DNA. Now they need to figure out the data segments and maybe in time they can figure out how datasegments in DNA manage to make their way into a creatures memory. Thats a few levels of indirctions that have to be figure out.
DNA decoding is starting to pick up on some of the debugging concepts that have been in the digial world for 50 years. There are ways to iterating over code so it looks like the single steping is going places. Its just hard to pull off on a multithreaded cluster and understand whats going on.
Of course what they are having a real problem is with the DRM stuff thats making it hard to build replacement brains out of stem cells.
...and before you slashdotters jump on me and criticize my paper for suckage, keep in mind that I completed the paper and defense in the middle of an unexpected death in my immediate family.
:)
So that explains why I didn't give two farts about cleaning up the listing for Algorithm 1, among other things...
However, I plan to expand the work in the future, so constructive criticism is welcomed
James
Or perhaps the reason for such a coincidence is due to the fact that we ARE a computation as Douglas Adams would have put it.
Perhaps we will find out one day what problem our creators were looking to solve when they put us into motion.
"Last one in is a rotten goblin!" - Kepp
"If your brain talked directly to your computer, now that would be fast and much less labor intensive."
not neccisarily. you may be forgeting that things like this often advance in scale- if you could interface directly with a computer you would be expected to process a lot more information, possibly even increasing the ammount of mental labor involved.
technology is often assocated with labor saving devices, this however is not really acurate- there is instead an exchange of energy along a band, the base unit never changes, the relationships are different, but the level of interaction stays on a consistant curve or "wave"
you must be the first person ~ever~ to realize this! it usually takes most science students until their 2nd year genetics course to grok this!
RNA is not copying anything (unless you are talking about the infamous (but unproven) self-replicating primordial RNAs). Enzymes like DNA polymerase are doing the copying.
Computing has accelerated biological research, but I'm unconvinced that it will fundamentally alter the prevalent paradigm in the biological sciences. OTOH, biology may provide the concepts that will push a change in computer science.
Not being a computer scientist, I can't say this for sure, but I think one of the places to look would be in membrane potentials & how that might be applied to fundamental computer architecture. For the totally baffled here, a nerve cell membrane may be polarized (off), depolarized (on), hyperpolarized (extremely off), or anywhere in between (sorta off/almost on). This isn't binary any more, Toto.
Whatever, let's remember what the scripture (Kuhn's Structure of Scientific Revolutions) tells us: Change will happen when practitioners of the old paradigm die. Don't look for anything other than incremental change in the meantime...
"Obviously, I'm not an IBM computer any more than I'm an ashtray" (Bob Dylan)
I think the largest factor in biological computing will be acceptance of society in its use. I believe there is no doubt it will be one of the largest transitions in computing we have seen thus far. Simular to the transition from analog computing to digital, but with a much larger twist.
More and more people seem to accept the roles which computers play in every day lives, but it has taken time. There is still much debate in the use of genetic engineering, but it has become more common practice today than it was a decade ago. Today almost all the foods we eat are genetically engineered to be better, more resilient to pests, yielding larger quantities, whatever the case might be (although I am not a fan of genetically engineered foods).
I think eventually we might be using computers which is some sort of brain (ie vaugely resembling a humans), and this will frighten some people tremendousaly. The moral aspects will most definitely be hindered by the government (whos job seems to be determining morality), thereby extending the process by generations. However ethical computing is not something which Computer Scientists or Biological Engineers should regard as a formality, because I feel it does deserve some very deep thought.
The possibilities are endless, and with all technologies it can be used for Good or Evil. The only thing we can really hope for is that humanity will extend itself rather than drive itself to extinction. So far so good though right?
- To those people at the Mayo Clinic... Keep up the good work! We need more people actively seeking these types of technologies and questions!
Being an MIS major with a heavy CS background I'm sure it will help with your COBOL OLTP system
HOLY CRAP that shit just changed my LIFE!!
MOD parent WAAYYY up.
Everyone must see the magnificent GAPING ANUS
Respectfully:
,..Now!
I admire your youthful enthusiasm in pursuing this thought provoking subject.
Computing now is digital. It was once analog. To inhabit and cooperate in the biological world, to achieve the massive and parallel computational throughput needed by a combined biological and cybernetic organism, computing as we know it must become ultrafast analog. Further, instead of binary digits we will need singular yet complex symbols to transfer data,..like the written Chinese characters which are both an alphabet, a phenomic indicator and when simultaneously having a temporal function yield whole libraries of data for corporate or research use- remove the temporal for individual human useage/home computing- for example.
The conversion from analog to digital or from digital to analog will be too great a computational load to enable the kind of computing I believe you speak of in this article - too great. Analog is vulnerable to both time and amplitude distortions when observed/used without a set of limit parameters. Complex math functions can be used to ' forward correct ' the analog activities however. Yes, I must state that analog is the platform on which biological and computational systems(note: I did not write, ' electronic ') can function as one truly, not virtually.
The change in thery will actually, IMHO, be an integrated , cross/multi-discipline approach to cybernetic organisms: The ssame as nanotechnology which also requires a multidisciplinary view when used in design work both a mono-disciplinary view when actually using the technology. The laws of physics and chemistry will yet be obeyed but- and quite obviously the question," what is Life." Will be answered wether we want to know or not! Then come your ethical questions:
Is this knowledge shared with, say poor, black cultures who could benefit the most but have no money?
Do we give these new devices names? Advanced physical performance parameters that make them supermen and women capable of reproducing and thereby interacting with our biosphere-causing change- and possibly wiping out we ' mere ' mortals? Or do we adapt this new technology to all who exist presently enhancing their lives,..or do we white people keep it to ourselves and as in the last, worldwide colonial period, dominate all those who are not enhanced?
You see - our technological prowess can easily outrun our ethical development or lack thereof! No one wants to have their morals dictated to them until AFTER Pandora has opened the box or Eve has eaten the fruit!
To make all of this succeed, I believe, humbly, we will need an area purchased near a major university or set of universities that is handed the funds to build a physical building for think tank that has international scholars and an international over-sight committe which includes all known bodies of clergy OR
Open Source!
Donning flameproof -unobtainium body armor
I have no specific technical background from which to address your questions (I know, I know... this is Slashdot), but your moral questions are interesting:
* 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?
First, I know it's only an example you've given (lit., "exempli gratia"), but the "societal" factors as you call it -- more political, really, but let's compromise on socio-political -- are not an exclusively "Right Wing" threat. The modern Left holds many central beliefs contrary to the integration of technology and biology, especially concerning human biology, for instance the primogeniture of society over the individual and (partially by extension) the malleable, ahistorical understanding of the human mind (a notion commonly referred to as "tabula rasa"). Under this view, attempting to "improve" or in any way alter humans as conscious beings by improving or altering us as biological beings will seem either immoral or, more likely, futile. This mostly to point out that limiting factors for the progress in your field don't come exclusively from conservative ideology.
In general there seems to be a growing trend in intellectual/ethicist circles toward acknowledging the massive (though far from exclusive) importance of our evolutionary past, which in simple political terms is more or less centrist or apolitical, though could be interpreted as slightly "right wing" (more libertarian or classical liberal than conservative), which suitably allows you scientists to carry forth your apolitical and almost-amoral research, leaving as the likely culprit for "most likely to impede the progress of biology-integrated computing" common economical factors: what innovations will ultimately create the most value, and therefore what innovations will proximately be most likely to succeed (in getting funded, in getting researched, etc.). And if you take exception to my "almost-amoral" comment (which you shouldn't), I mean it compared to people who spend their lives sweating over the ones and zeroes of right and wrong -- not that you value ethical behaviour any less than they do, only that you likely (likely) pay less attention to the nuances of what makes ethical behaviour ethical; my guess is you probably subscribe to a simplistic (and ages-old and approximately, though probably not absolutely right) axiom like the biblical (new and old testament) reciprocating Golden Rule or the commission-of-harm-avoiding Hippocratic Oath -- good on you.
* And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences?
There are numerous criteria for wrongness, and in the case that you mean moral wrongness there are numerous defensible moral systems. Also, if you mean specifically moral wrongness, most moral systems taken into consideration not just consequences of actions, but the intents behind them as well (brick-in-the-head obvious example, Western legal tradition's distinction between premeditated and non-premeditated murder, or either of those and accidental homicide) -- if you meant to imply the connection I've understood between inherent wrongness and consequences.
I don't see anything one could construe as inherently wrong with the research you propose, if you don't believe in God or make intuitive essentialist ascriptions to the human form or subscribe to the aforementioned primogeniture of society over the individual and all that entails. In other words, assuming of course otherwise ethical behaviour, if you're a modern freedom-loving humanist (or, in the trivial case if you're a nihilist), it seems to me there's no basis for having qualms about the philosophical nature of what you're doing -- but it's healthy of you to be wary of slipping into less-than-savoury situations, and to constantly question yourself to defend against aforementioned slipping and to ameliorate yourself -- no doubt the skeptical scientist in you.
Fuck it
A striking aspect of this analogy is how poorly functional units are separated from one another in organisms. The largely distinct functions of storage and computation appear to overlap at molecular and cellular levels. I feel that a coming big revolution in computers is a blurring of these distinctions, but that idea is vague futurism by me.
Computing is already helping biology, like with protein folding. This is only going to get stronger.
;-)
Biology may help build better computers, either by "growing" things like media, or with nanotechnology indistinguishable from biology being used to grow chips.
However, the "ultimate" convergance of a biological computer is not going to happen, except perhaps in an isolated sense where it can be made cheaper to grow a computer. The problem with biological computing is that generally we want to compute, not be awed by the biology. (Far, far too many people when trying to imagine the future get sidetracked by the "awe" factor, but the "awe" factor is not a long-term factor.)
"Pure" biological computing has an unavoidable disadvantage vs. non-biological computing: It's biological. Which is to say, you need an infrastructure to keep the biological part alive, which the non-biological solution does not need. This is an intrinsic flaw which can not be overcome except by leaving the biological realm. By the time we could build the "biopacks" seen in Voyager, we'll be able to build something much better that isn't biological. The part of the system keeping the biopack not only alive, but in the quite-likely narrow environment it will actually "work" in, would be better spent on actually doing the computation.
Biological systems are astonishingly redundent, but that's just not necessary for non-living systems, where cracking the system open, repairing it, and reassembling it and expecting it to work isn't that big a deal. Do you think twice about repairing your car that way? Since it is of no particular consequence if a computer "dies" briefly, there's just no need for the astonishingly complex low-level redundency and healing capabilities in living systems.
A pure, 50-50 convergance is a chimera. Both fields will be helping each other, computing probably helping biology more then the opposite, but total convergance is not going to happen. "Every discipline inevitably thinks of itself as the most fundamental." Computer science isn't exempt, and I know biologists feel that way. But a dispassionate examination shows there are fundamental differences such that the only way they are going to "merge" is if biology ends up being redefined to be the same as "nanotechnology" and includes things that we do not currently consider "biological".
Which will probably happen, but it's not the sense you're asking about right now.
BTW, "genetic" computing is mostly a side-show. It's practical significance is virtually nil. It looks cool, but it's slow as all hell and unreliable to boot. (What, slow you say? Yeah, it takes forever to set up the problem. Sure, it runs quickly after that, but it's disingenuous to dismiss the setup time, which while certainly possible to accelerate, will almost by definition take longer then checking the answer directly.) Current machines can already stomp the performance of any pure genetic computer you can imagine. (Note this very distinct from a machine that some genes may grow; be sure you know what these terms mean before you criticize this post, all you budding Slashdot biological computing experts.
A lot of other existing "biological computing" is mostly a side-show too; cute, but it takes some serious trips into fantasy-land to come up with a practical application that will actually beat the non-biological competition.
To the extent you care about my opinion, and remember, you asked, I would not advise getting too far involved in this field.
(Now it's entirely possible that in the process of researching a pure biology computer that something interesting could be learned. I also think pure quantum computers are impossible but the research is useful and useful hybrid solutions will be developed, so the research is not a waste. But on a personal level, I would still not want to actively pursue something that's unlikely to be possible.)
I work for a biotech developing their informatics syste, and have been heavily exposed to the challenges of working with chemical and biological data. And, I will be starting work on a PhD in CS focusing on these areas in August.
A quick note, I use the more general term 'life sciences' to denote the field that this work affects. While chem, bio and medicine are traditionally seperate disciplines, the computational problems are similar and shouldn't be forced into the old categories.
The biggest challenge currently facing both people attempting to solve problems in the life sciences using computers and those trying to implement computers using organic materials is the lack of mathematical and computational models to describe these systems.
Step back 50 years to the early days of computing. Computers were designed and envisioned as equation solvers and theorom provers (gross simplification, but bear with me). The physical sciences naturally adapted to computers since most of their theory is written using the language of mathematics. These fields helped push high-performance computing to where it is today and in doing so, molded the field to solve math-based problems.
During the same time period, the life sciences were starting to understand DNA. With a strong tradition of wet-lab and notebook based work, computers were essentially ignored. This changed somewhat with data aquizition systems in the 70s and 80s, but to this day, most analysis is still done on paper or in generic spreadsheets, using printouts from data systems (I see this every day).
On the modelling side, accurate mathematical models for even the simplest chemical and biological processes do not exist. While there's an obvious mapping of DNA to bits, no one has been able to do anything useful other than similarity searchers (which are rooted in math). The 'obvious' solutions to most problems (eg, protien folding) are generally NP-Hard and simplifications tend to yeild poor results that are not consistent with nature even for the simplest datasets.
Thus, the challenge in working with life sciences problems lies in the lack of a mathematical and computational system that can be used to explore the problems. Current research into linked systems (networks) and agent-based systems appear to be promising areas, but they quickly run into the same computational limitations of modelling life sciences problems - ie, they grow too big too fast.
Note that this discussion has left out the data analysis and processing problem (traditional bioinformatics), but it's worth mentioning that many of the same challenges exist. With no theoretical foundation for the data, it is hard to come up with a meaningful interpretation.
So, it appears that what is needed is not more articulation of current mathematical and computational models, but rather a different theoretical framework to work with. As mentioned, networks and agents brush this surface as haves chaos, cellular automata, complexity, neural nets, and most other 'fun' ideas of the last 20 years. Wolfram attempted to and has probably failed at providing a new framework to work within, but others are still looking and his ideas will hopefully lead to other insights towards this end.
The key here is that it will probably not be based on predicate logic and only implementable on Turing machines in ways that make it non-computable. So, a completely new system of logic will be required and a new model for computers.
Once this is in place, then life sciences problems will be accessible computationally. In the meantime, there are still lots of math-based problems that our current models will help sovle.
-Chris
I was wondering what people thought about the future of biology-based and genetics-based computing...
It's been done and it ended in tragedy. You can read a case study in The Hitchhiker's Guide to the Galaxy by Douglas Adams.
"How can I turn this into a thesis?"
WARNING: there is a trojan on your
You might want to check out:
http://www.nature.com/nsu/030421/030421-6.html
Science. 2002 Apr 19;296(5567):499-502
and also EMBO reports vol 4 No 1 2003 pg. 7-10
Certainly evolution has come up with a pretty robust (although for some of us not robust enough) methods of error correction and data storage. However, the bottleneck for us taking advantage of this would be the ability to manipulate DNA - our ability to duplicate, cut, and splice DNA in the lab has been entirely dependent on isolating enzymes from organisms that happen to do the exact reaction that we want (the use of t. aquaticus polymerase in PCR is a good example). Biological systems use DNA to, well, express proteins in response to stimuli and most of these enzymes aren't of obvious use for computational endeavors.
IMHO biological computing won't be truely feasible until we understand enough about protein structure & function that we can design new enzymes from scratch to do whatever DNA-manipulations we want. Which opens a whole other can of worms, trying to simulate biochemical pathways is another interesting field of recent interest (e.g. enzymes are regulated by large interconnected networks of enzymes that modify each other). Some people at UCSD are trying to simulate whole cells using modified analog circuit simulation software (since analog circuits are coupled DiffEQ's just like biochemical networks). Pretty far out stuff.
I'm afraid I do not see the connection between DNA and computing systems at all. Since any system with a discreet base is a Turing machine, and a human being is a provably more powerful machine than the Turing machine (we being able to attempt the halting problem, and whatnot) it is somewhat presumptuous to assume that DNA is a base 4 system, and thus life is subject to the rules bonding a discreet base system. The base of the universe is e (non-terminating, not-repeating decimals give the real world its spice), and the fact that man can even attempt questions like "is this beautiful" and "what is love" in my mind makes all comparisions of this kind impossible. -Benjamin "Durandal" Edelen bkedelen@yahoo.com
Q1: 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. A1: No. The informational aspects of DNA have been known for 50 years, only slightly longer than computers and failed to influence computer development. Nor does the computer science theory of information help biologists because they don't understand the intermediate mechanisms thoroughly (like precisely how a cell works). Q2: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? A2: The underlying theory, which is essentially irrelevant, will not change. Q3: 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? A3: Digital computers will always be better computers than biological based systems, that is why biological computational systems are going to be relegated to university labs and academic papers. Q4: And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences? A4: If society declares such research forbidden, some other society will pursue it, if it has any value. Ethics only apply in a closed system, which world science is not. A5: Biological systems and engineered systems are as different as a dog and an engine. Either can pull a sled, but it easy to pick out the engineered version and the biological version. Ask a mechanical engineer how much animal anatomy affects his craft or how much mechanical engineering affects a dog breeder. The questions are as relevant as the ones you asked.
You do know that we're just part of a 10 billion year computer program on Earth, the greatest computer ever built in space and time, and commissioned by mice?
Simple Guide to DNA Computers
How Stuff Works - DNA Computers
No ground breaking crypto solving or Beowulfs yet but some solid calculations going on.
Bleh!
I recently started working at a bioinformatics position as well, coming from a pure CS background. I havn't learned enough of the biology side of things to really get into much more than tool support for distributed sequence analysis toolchains, but what the hell, might as well comment.
One thing I want to say before responding to your points: nature is _NOT_ "efficient" like computers are "efficient". Natural systems are enormous, ad-hoc, kludges. They work extremely well, and have tons of redundancy and fault-tolerance, but that's mainly due to about 4-billion years of slow, brutal, optimisation by the evolutionary process. Natural systems do certain things faster than computer systems because:
1. They've been optimised for a hell of a long time, and they've found ways to engineer and construct extremely complicated structures and processes that are still "small" (compared to modern human-engineered technology).
2. They've been allowed to search through a much larger solution space than what computers have searched through. Computers are inherently limited by the fact that they are tools which can still be reconciled for a large part with human reason - they were constructed using models that humans can understand and reason about, and explain fully from the start. Evolution, on the other hand, is much more of a blind search.
Another thing to note is that natural systems all try to solve one problem: existence and self-perpetuation. All the natural systems we are able to observe today exist because they are structured such that they can fulfill these basic requirements. Now, in the process of solving this single-minded problems, nature has managed to come up with solutions for many other problems - many of which can be borrowed and applied to human problems. But it's erroneous to think of nature as "god's textbook of problem-solving", or anything like that.
> 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.
There are two aspects to this - borrowing ideas from biology (i.e. reimplementation), and borrowing biological structures themselves (e.g. using bacteria to make enzymes, viruses as delivery vectors for drugs, growing muscle tissue for robot-locomotion, etc.). Both are happening to a certain extent.
I think it'll be a while yet before we will be able to jump into biological systems and "change the code to do what we want". We do it in really primitive, crude ways right now, but the level of complexity of biological systems, I think, will mean that it'll take time before we are able to fully control them.
>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?
I don't think biology will change theory that much. CS theory comes from the human reasoning process. I don't think there are that many abstract concepts that we can extract out of biological systems. I think the real impact will be in engineering aspects - mimicing, or reusing wholesale, biological structures to acheive the properties that we want.
> 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?
Forget the right wing. They make a lot of noise, but ultimately they are not that powerful, especially in the capitalist west. The religious conservatives are used as a tool to get votes, by pandering to their pet causes, but once people figure out a w
There actually was a STAR TREK TNG episode where an alien data capsule stored flight-path information as RNA. I excuse the science advisors for choosing RNA over DNA, although having extensively handled both in the lab I can say DNA would be a much more sound choice. RNA tends to degrade rapidly if you look at it funny, whereas DNA is reassuringly robust and you can heat it up and shake it up and it won't break. You choose which one to put in a data capsule in an exploding escape pod being chased by aliens with bigs guns :-)
*** Mild spoilers, if you haven't read at least the first two books in the series ***
Rama talks about biots, which is short for biological robots. It's a combination of organic matter with mechanical/electronic parts. The crew captures one and takes it apart to find a combination of biological based batteries (think: electric eel), electronic parts (for recording, visualization, etc.), and other parts. The idea is furthered in the subsequent book, Rama II where the Octospiders manage to engineer biological creatures to do work for them. For example, a dragonfly-type machine can be built to do a videorecording of a scene. When it's done recording, it uploads its data to a central location and, in exchange, is rewarded for doing the assignment correctly by receiving energy or food (or karma points, whatever).
There will be an eventual convergence of biology and computers. And I'm not talking about simulated biology via genetic algorithms/programming Preliminary progress is being made, but I await the day I can plug in a 1 terabyte hard drive into my brain!
Want to improve your Karma? Instead of "Post Anonymously", try the "Post Humously" option.
I think that ultimately biology will contribute more to CS than the other way 'round.
Presuming you're not a creationist, there are MILLIONS of generations worth of Darwinism at work in even a simple worm - weeding out the inefficient in times of stress, etc.
Granted, the process in biology is neither linear nor even relatively efficient, but there are tremendous lessons in autonomous operation, fault-tolerance (HUGE), adaptability, etc that bio systems can teach or implement in computer situations - what can bio-systems get from computers? It just seems natural (ha!) that the more we learn from bio-systems, the more we'll apply it to computer paradigms. Until now, it's been too complex for us to really understand.
-Styopa
Let's just say that there'd BETTER be some redundant code in there, guys. Let's hear it for organic checksums.
Also, has anybody thought about the concept that this gives a whole new twist to computer viruses?
Do we have computer languages sophisticated, durable and trustable enough to trust with letting loose on the REAL global infrastructure? I believe that the same case can be made as if talking about organic nanites. Can you see a seething black mass of disassemblers where Earth used to be? I don't know about you, but it would really put a crimp in MY summer vacation.
Now, add in the concept of prions, and mix well.
I don't WANT computers and organics to mix until we can isolate the labs on the other side of at least 60 miles of vacuum, with a nice, hot re-entry burn bewteen us. Debugging could be a real bitch.
This is way out there, probably off-topic, but, so what, this is slashdot...
I think we (humanity) need to start an artificial brain project. The way I envision it would be a kind of simulation (hardware, software, probably both) of an actual functioning human brain. Admittedly, we are a long way from being able to achieve this, but I think it should be a goal, much as putting the man on the moon was a goal. It would (potentially) answer some very interesting questions, or at least shed some light. E.g. What is conciousness? Does free will exist? (if it does or doesn't, is there a measurable difference?) Can machines think? How does the brain work?
Some would argue (perhaps religiously) that such a project is doomed to failure, and should thus not even be attempted. Poppycock. Even supposing it is doomed.. (e.g. by "magic" (or nature), machines are "prohibited" from thinking, somehow) we would still learn so much by the attempt.
Of course, such a project would need to start small and work up to a human brain, e.g start off much smaller, emulating a few neurons.
Eventually much more than just the brain would be needed, as a brain, by itself is useless. Much of the supporting body would also need simulation, though perhaps in simplified form.
I wonder if there is any ongoing, serious research in this area...
Conduction was the wrong word - I meant processing and typed conduction (was in the middle of writing my thesis when I posted :P). The best biophysical representations of neurons (using GENESIS or NEURON) cannot operate on anywhere NEAR the time scale that normal neurons operate on (off by a few orders of magnitude). By operation, I mean all of the internal processing and such that determines the behavior of the neuron. Granted, you are right in saying that electrical impulses are very fast, but remember that it is the circuitry (wiring) that will slow it down. Neurons use all kinds of tricks (Nodes of Ranvier) to bypass some of the constraints of their system (though as you said, still not approaching the speeds of silicon). The true problem will be to see if silicon processors will ever be able to match the processing power of a single neuron.
I agree with the parent post. We don't actually know what this "junk" is for. In my own projects, I have certain things turned off and on by dummy variables and seemingly unnecessary if-then statements. You could very well go into my stuff and say "ten percent of this stuff is unnecessary. You could erase it or replace it and nothing will go wrong.
And you would be right. Except that you killed off some functions not meant for today. You killed off functions that weren't in the right conditions to be operated today. Unfortunately, you killed off a whole lot of stuff that could be valuable tomorrow or for future development of the program.
I think programmers are in a good position to gain insight to the meaning of life or at least the creation and perpetuation of it. The code might be dead to you but you didn't write it nor were you involved in the development.
The wise would reserve judgment on those curious stretches of DNA until we know more.
Laws are for people with no friends.
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.
Convergence of Biology and Computers? It's already happened.
Everyone knows that planet earth is the biggest beowulf cluster in the universe.
[ducks]
first off ) why would you ask the slashdot
'community' if you wanted real information?
second ) why do asshats everywhere marvel at
the various slight homomorphisms out there?
it isn't like 2 doesn't come after 1 regardless
of the discipline you're talking about
third ) screw it
What will happen is lawyers, marketers and a whole lot of other people who also know little about either will create a monster, ungainly, unkillable, self serving, that will help people in all the advertised ways while hurting them in new unforseeable ways, then a white rap singer turned actor will star in a movie that gets it all wrong.
It's still a practical application, despite the trivialness of it.
Yes, maybe a travelling salesperson problem with something on the order of a million possible answers would be solvable using DNA. Right now, it's probably 100 times more capable (speed- and memory-wise) than our conventional computers.
However, DNA doesn't get any smaller or more efficient. It simply cannot advance. As problems get more complex the margin of error gets too large to ignore, and reactions take too long. In the long run (10-20+ years), DNA will not be as fast or accurate as other solutions.
If I had 10 years to collaborate with other scientists to produce the best travelling-salesperson-solving computer, I'd look long and hard at Quantum computing; it's the opposite: it solves more complex problems just as easily as simple ones.
If your brain talked directly to your computer, now that would be fast and much less labor intensive.
When you type, you slow down everything inorder to focus your random sporadic thoughts into one concise clear communication.
I often find myself rewording everything and going back to fix spelling mistakes before hitting "submit". With a direct connection to my brain a computer would blow up due to indecision or conflicting desires.
I can barely handle tabbed browsing. Imagine how many "tabs" would be open at the same time with a direct brain connect.
I forgot to feed the computer!
Free Software: Like love, it grows best when given away.
Go get yourself Neon Genesis Evangelion. Watch it all in one a week and come back with your comments. Biology, Tech, and the next step of evolution.
It's a work of art, not an action anime.
FWIW, I remember telling my parents 8-10 years ago that computers would simply evolve and we would eventually create the human brain. I gotta thank you for your comments. I'm really excited about what the future of biocomputers holds for us all.
How much longer until we develop biological material that we can place binary/electrical data into and control? i.e. a remote control biological single celled organism! Or perhaps a squirrel. Sure would make animals in films fun, and it would put those computer animators of small furry creatures out of work.
The problem is that when storing data, the most basic form of all information is the boolean- true or 1, vs false or 0. If a system has sufficient dexterity to determine the difference between the nucleotides A,T,C or G, then that implies memory redundancy as A,T,C or G could be broken down into smaller constituent parts. To put it another way- if you have to determine atomic differences to work out if a particular molecule is A, T, C or G then you are at the lowest level counting the presence or absence of various sub atomic particles- which IS A BINARY PROCESS.
but lets for a moment accept that there may be a world beyond binary, base 2 encoding- we humans use base 10 after all!! DNA seems a little limited at base 4 (yes, yes I know that there a really much more nucleotides than the 4 basic ones, but lets keep it simple here!!) why not go the whole hog and find a medium that enables base 1000 or even greater...
Biological hardware would certainly have many benefits, but the encoding would always be binary, and therefore DNA would not be best suited to this purpose
When the biology folks and the computing folks get together, it always gets really theoretical and really useless. All of the real-world-benefit folks are busy working on problems of a fundamental chemical nature: it is enzymes that shape, edit, and cut DNA. It is enzymes that create drugs and ARE drugs. You cannot learn too much about protein chemistry or structural biology. These disciplines are under-appreciated and there are very real biology and computational problems involved in them for those who take the time to look.
http://tinyurl.com/4ny52
When I was learning the concepts of EJB's and remote processing, I began to see patterns between how Cells intercommunicate and the way Clients/Servers work. I began to see similarities between how cells set up "firewalls" and open firewalls and the way we do it in computing.
As an OO programmer, I began to look at the design patterns we use in OOAD and the design patterns cells use, both internally and externally.... Wish I was back in graduate school!!!! What a dissertation!!!!
There have to be levels of abstraction when ANYTHING deals with complexity and "fuzzyness". Cells do this very well (aided by eons of evolution). To solve some of the problems we have in computer science, maybe we should look at how Biology does it. We don't have to create biological computers, we just have to look, understand the abstractions and see how they apply to other problems.
Biology's central dogma claimed for a long time that DNA mostly codes for proteins, and that RNA serves as the messenger, and that's it (simplified, but mostly true). So screening for RNA expression mostly tried to find those RNA chunks that code for proteins - those that are long enough to code something meaningful, and have other characteristics (like having a translation starting point and translation end - sort of like the curly braces at the beginning and end of a C function...).
To make matters worse, RNA is some of the easiest stuff to contaminate, it breaks easily, and generally is not fun to mess with in the lab - So when you get short RNA sequences in your test tubes, you assume (rightly) that it is an artifact and not the real thing.
However, in the past 10 years people have been discovering that there is RNA that gets produced from DNA ("transcribed" or "expressed"), which is not getting translated to proteins, but has amazingly strong effects on the organism, nonetheless. Just one example - small pieces of RNA, as small as 20 bases, can cause the complete shutdown of specific proteins synthesis (it's called siRNA - short interferring RNA).
Stuff like this is making people rethink the "junk DNA" hypothesis; Why would we lug along so vast an amount of DNA that has no purpose? Why would it be replicated with such fidelity that it still resembles DNA of yeast, bugs, fish, etc.?
Of course alot of it is just the old code that nature didn't bother to ^K after it has commented it out, but among all this cruft is the little gems - the precious "if", "switch", "break" and "exit(1)" statements that actually drive our software.
My "take home message" is that out of this seemingly "junk" DNA might, not so far in our future, spring a new discipline that will make genetic engineering something comparable with electrical engineering; we (biologists) just need to understand that proteins are not enough to explain the complexity of a living machine. (yeah, I know I can get a little bombastic.)
DNA for computing - why? DNA just sits around much like a string of zeroes and ones. Proteins - now that's hot. Think about it this way:
bytes = base 2, aggregates of 8 - not much storage space and not able to interact with other 0s and 1s
Codons = base 4, aggregates of 3 - not much storage space either but DNA doesn't really communicate with other DNA strands (not directly at least)
Proteins = base 20, aggregates of variable length - and they can interact with one another in highly complex ways
I think it's clear which approach will give us the best problem solver in the future. Though they may not be very useful as personal computers, a protein-based computer could model things like weather patterns, economic cycles, etc.
What I want to know is: We use binary math time the standard number of increments (8bits to a byte) to denote the amount of space a file takes, or what the storage capability is. DNA uses base 4 math, and has 3 sections to a whatsamacallit, and how many of those to our entire DNA sequence? What I want to know... How much storage does one Human DNA strand have, measured in KB, or MB.
I don't know if I would necessarily trust a mechanical process that is as prone to upset as much as a biological one. A bunch of little carbon spindles will work forever, DNA only works until some gamma ray bisects it and then who KNOWS what the hell your process will start doing.
I prefer knowing that the bugs I introduce to my code are static, thank you...
I remember learning about RNA translation and PCRs in 9th grade biology. I thought the link between dna and computers was interesting so I did a search and found a Howstuffworks.com article entitled "how dna computers will work" http://computer.howstuffworks.com/dna-computer.htm
http://news.com.com/2030-6679_3-998622.html
668: Neighbour of the Beast
IMHO, there is one very important fundamental difference between the way that information is processed biologically and the way that it is processed by a computer. Computers work under the assumption that data is processed in a perfectly orderly manner. Programmers strive for a situation where you start with a known state and move to the desired state with mathematical certainty. This requires very carefully-built equipment and software, but the benefit is that you can know with some certainty that you have received exactly the right answer. In fact, this is so much the case that random number generators cannot easily be implemented on a computer.
Biology works on a completely different principle. The "equipment" operates in an unknown environment. The very goal of the equipment may change during operationg. Having predictable or exact results is not required and not even really desired. Mathematical certainty goes completely out the window. And most importantly, not only are random variations in the information processing process considered not a problem, they're actually an integral part of how the system works. That's a pretty big fundamental difference in my opinion.
To put it another way, in computing, anomalies in information processing are called errors, and they cause problems, producing a crash, a hang, or incorrect results. In biology, anomalies in information processing are called mutations, and they cause solutions (sometimes!) to unforseen problems that the population encounters. The two systems are based on totally different ways of operating. Computing seeks to solve a perfectly-well-defined problem perfectly. Biology solves a wide variety of unforseen problems imperfectly -- hopefully well enough to let the population as a whole survive, but maybe not.
Part of this is meant as a warning to those who want to make computers work more like biology. If we do that, we will lose the exactness and reliability of computers. That may be an OK thing if we expect it and decide we want it, but it's something to keep in mind.
More to the point for this specific situation, if you are going to use biological mechanisms to try to do traditional computing, you need to consider how well they transfer from one paradigm to the other. They're obviously well-suited for biology, but can they provide what is needed in an environment where having one wrong bit can cause the whole system to fail? And if not, can you build a system that that keeps that source of error isolated?
There's a nice paper called "The Computational Linguistics of Biological Sequences" that deals with a lot of the interface between formal language theory (computers) and modern genetics.
I'm not sure how authoritative it is but it is fairly accessible to someone with a little math and computer science background.
There are important similarities between the information processing and transfer in living organisms and mathematical computation, which have been recognized for more than 50 years (see Gunther Stent's "Paradoxes of Progress" for some essays on the nature of genes and biological information transfer as the central dogma was emerging). But there are critical differences as well, which are often misunderstood.
The fundamental difference between computing in biology and computing with man-made computers is that biological systems were not designed. This has very important implications for the relationship between biology and computer science:
So, to answer the questions posed:
...get into bioinformatics, computational biology, and do your CS as a minor. At least, that's what I'd do if I were gonna start over.
The tough thing about entering the compbio world, specifically in industry, is that truly engineering style gene programming type jobs (what the previous poster is alluding to) are few for now. I have a biotech degree from an engineering school and focused on compbio as my specialty. After 4 years (short I know...) in the discovery sector I have seen a few of the junctions of computing and bio come and go. Most notable of these would most likely be gene expression analysis. A few years ago simply running a dozen gene chips and doing a simple clustering analysis was enough to get a pub. Now you *MUST* follow up with functional analysis of your work.
My recommendation to those interested in computational bio is certainly to pursue your dream; just keep in mind that when you actually get out there the nexus between bio and computers is embryonic, and for the most part dominated by biologists. Hard core soft-eng types may find a slower moving, less structured environment for pursuing their goals of writing the first biological computer than they envision.
> 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.
These aren't really "either-or" but "both"
Computing will probably evolve to DNA-based biological computation to some extent. There will remain an "inorganic" element (which I lump polymeric semiconductors which are strictly organic from a chemist's POV. Biological computing has the benefit of a billion years of debugged code effort.
Our understanding of biology will integrate many current concepts and ideas from mathematics, engineering and computer, in general. Biologists at the moment are fairly illiterate mathematically (newer bio majors excepted?). The fact that so many biologist seriously believed or, at least, propagated, the "One-Gene-One-Disease" garbage is a clear illustration of this. Fortunately, this idea has finally been trounced by the recent gene counts of the Human Genome Project. Any 2nd year engineering student could look at the DNA-RNA-Enzyme-Protein pathways and tell you it couldn't possibly be "One-Gene-One-Disease" because that would imply linearity and no feedback loops. I predict that biology will begin to require an undergraduate curriculum vitually identical to engineering very soon (before 2010). This would include 4 years of advanced math, as wells as plenty of computers and physics.
> 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?
Biology is usually taught holistic as a holistic science. This is good because, generally it is, and because reductionism has its gaping blind spots too. However, this is often used as an excuse to shun reductionism by some in the field. The only real difference between the hard sciences and soft sciences is that the reproducibility of phenomenology has a small variance in the former giving the appearance of "hardness" and simply facilitating reductionism, but has a large variance in the latter, often addling our limited brains and requiring holistic methods instead. Mixing computational tools with biology facilitates our brains' ability to apply reductionism to a large variance phenomena. In either case, the science is equally knowable and explainable, only we mere humans may need computers to help - hardness or softness is an artifact of human cognition.
> 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?
Fundamentally there has to "value" to a technology for adoption to succeed; without "value" opposition to a technology can (but may not) win. What is "value"? Anything a consumer of the technology decides it is for themselves. Simply being "neat" from a techie POV is not enough for success. Even though file sharing music may be technically and rationally wrong, it delivers so much value to most people that the RIAA will always be on the losing end of the argument regardless of how technically "right" they may be. RIAA members so out of touch with consumer value-needs that don't even see why this situation has occurred and why most everything they do riles their would-be customers into opposition. Most of us know this intuitively rather than rationally.
The value comes from what it does for you, not what it is. For techies there happens to be an alignment between what is and what it does for the techie (socially, intellectually, emotio
I think your falling into a very subtle trap if you think like that. Yes biological systems (say a worm) take input (light) and convert it into output (say keep moving till its dark). So do computers, DNA looks sort of looks like binary code. however computers were not designed by evolution.
Evolution is like a programmer who does not know how to program, so it just makes random alteration (from toggling bits to duplicating whole chunks of code) to the program, to the compiler to the operating system and to the hardwear then uses the solutions that are just good enough for the job(1). If a new project comes in (ie ecological niche) evolution will try and hammer all its previous projects into the new job specification and randomly diddle with the closest fits.
(1) 'just good enough for the job' does not mean that the 'program' is not very good at that job just good enough is a very tough spec if your in direct competition with a 100 other programs. However it does lead pretty shoddy work... have you ever wondered why people fall to bits after reproductive age? Evolution does not care about you after you have sucessfully reproduced
Information may be coded digitaly (in DNA) thats just storage. Its expressed in analog form as proteins which can be created is hundreds or even thousands of different varients, are sensitive to the actions of other proteins, the chemical enviroment, itself, and quite literally the phase of the moon.
"Biology works abit like a computer" is a useful analogy, but never forget an organism under rigerous conditions of tempreature, pressure, humidity will do what it bloody well pleases.
~~ :-)
I'm a fugitive from the PCR Chain gang. Now I bioinformatose all day long
The Army is constantly doing things that defy logic, so if you are going to start comparing it to computers, you would have a machine that would give you 5 when you add 2 + 2.
Fascism should more properly be called corporatism because it is the merger of state and corporate power. -- Mussolini
It seems that DNA has a lot in common with /.
/. posts are 'Junk Posts' (like this one)
99% of DNA is 'Junk DNA'
99% of
hmmmmmm
--Laugh it will make you smile
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.
The first. Biology is just superior cybernetics. See cybernetic biology and autopoeiesis.
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?
Self-conscious computers are only possible if we mimic biological self-generation (autopoiesis), without self-generation there is not self, whithout self there can be no self-consciousness.
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?
Exterminate traditional (pre-cybernetic) biologists!!! :-P
No, really, there is a question of paradigm shift involved; traditionalist biologists are the greatest obstacle in the development of both biology and para-biological computing.
And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences?
Inherently wrong? Nothing I can think of. Consequence, and rather serious, would be for humanity to face an alien intelligence---at least it scare the willies out of me. Another consequence would be the de-icing of research on evolution as the Great Neodawinian Theoreticl Winter finally comes to an end.
``L'imagination au povoir.''
If you'd studied evolutionary biology at all (or even just read any phylogenomics), you'd know that nature is actually very inefficient. I've also worked in molecular biology and bioinformatics (at Berkeley) enough to realize that stuff evolves to be "just good enough," and no better.
Computer science has always been driven by completing tasks as efficiently as possible. It's romantic to dream of biological computers someday running in reaction tubes, but giving up real efficiencies in the silicon world for fanciful efficiencies in the biologic is like trying to run backwards as fast as you can.
Paragraphs!
The Earth *would* be a computer if the Golgafrincham's B Ark hadn't messed things up for the mice's plans.
hmm... although computers might be efficient at add() or sort(), or other smaller operation, at the systems level they aren't there.
For instance, how would you represent the following functions:
I know! We'll use one of those "DNA computers" I heard those are really fast at doing work like that.
The snow doesn't give a soft white damn whom it touches. -- ee cummings
Biological organisms do acomplish computational tasks but I doubt anyone's going to try to port netBSD to the ecoli bacteria anytime soon. What we stand to learn from studying these systems is the understanding of the Algorithms that govern basic biological functions.
How does a few simple molecules of DNA self assemble into an elephant? These are structures which are orders of magnitude larger and seemingly more complex than the original building blocks. Can we code a set of building blocks to self assemble into a stapler instead of an elephant?
If our DNA code is to large and obfuscated to make any sence right now, let's forget about it for a moment, how wold you execute any of these instructions to begin with? What protine.dll files do you need to interpret DNA? What happens if you're missing them? What if you cold write extra?
What are the errors involved in genetic computation? Can knowing how these errors are produce be used to prevent disease? Will knowing how these errors occur help us detect precursurs years before disease sets in?
Can we use the knowledge of self assembly to make better RFID tags. The application of this knowledge to nano/micro/macro manufacturing are going to be most imediate in this field. Then maybe nano/micro robotics in a few years... okay many years.
As for the ethics, well we're a long way from being doing anything that's ethicly questionable so ethics is mostly a nonissue. The knowledge just isn't there yet. People occasionally do things in the course of their research that involve creepy things like dead babies, but that's not a new debate in the ethics.
Sure - but I'd say that puts you in the same position as the MIT hackers in the late '60s. If the field develops as I (expect/hope) it to, that's a very good place to be, both in terms of future career development and in terms of the potential to "learn new stuff" or "change the world", particularly if you're just about to enter university.
> Hard core soft-eng types may find a slower moving, less structured environment for pursuing their goals of writing the first biological computer than they envision.
I should also have clarified that I'm not convinced that "biological computers" (that is, Turing machines or massively-parallel non-deterministic problem-solving machines) are gonna be the Next Big Thing. (They might be, but I'm not yet convinced.)
I'm increasingly convinced, however, that going the other way - hacking DNA and running it as if it were code, should enable the production of gobs of useful genetically-modified organisms, which I think has a much higher probability of being the Next Big Thing. Think "build bugs that can generate vaccines" (already being done), "make a fish's skin able to do photosynthesis" (wacky idea off the top of my head to eliminate plankton from the food chain), or human-modding (like the guys who tried to cure CF by h4x0r1ng lung cells with viruses, although the hack didn't work.)
Those hacks were done using cut-and-paste techniques without a lot of real understanding about how DNA "code" really runs. Sorta like cargo-cult programming - you don't know what the code does, but it's close to what you wanna do, so you cut and paste the whole module at a time and see what it does. The kinds of hacks that could be done once we really know how DNA works, would make these pale in comparison. (I dunno, say, double the human brain size with upscaled intelligence, add huge eyeballs that can see in the infrared, or use the current eye and hack it to see UV, user-controllable meat/machine interfaces - graft silicon onto/into flesh for additional math sk1llz, engineer a set of symbiotic organisms to act as a second immune system, etc. etc. etc... and in short, make possible all that wacky sci-fi stuff that gives geeks hard-ons and bioethicists nightmares :-)
Bioinformatics is a great field for CIS, mathematicians, statisticians and quantitative types to get involved in if they're doing it for the right reasons. That is, if they like the science and want to make a contribution. Otherwise the field seems to be becoming saturated with quantitative types who are unwilling to make a real commitment to learning the technology.
It was Leonard Adleman (of RSA fame) who first proposed the idea of using DNA to perform simple computations in a 1994 paper entitled "Molecular computation of solutions to combinatorial problems" (you can find it here.
Adleman's DNA computer computed the answer to the Hamiltonian Path problem for a small graph. The Hamiltonian Path problem is solvable on a conventional computer, however it is an "NP-Complete" problem, which means that all known deterministic algorithims have a running time which is exponential with respect to the problem size.
Adleman's solution was to encode random paths through the graph in billions of DNA strands, then use custom engineered enzymes to eliminate those strands that were not a Hamiltonian path. Essenially, he simulated a non-deterministic machine through massive parallelism.
While this is increadibly clever, and very interesting, it isn't necissarily practical; at least, not for NP-complete problems. Adleman acheived linear execution time for an NP-complete problem, but he did so at the expense of requiring an exponential number of DNA strands with respect to his problem size. A small graph with only a few hundred nodes would require more strands of DNA than there are atoms in the universe.
This is not to say that DNA computers are of purely academic interest; Adleman's computer was merely a "proof of concept". I'm sure there exist problems in P which would benefit immensely from massively parallel computing. It's just a question of finding problems which are cost effective to implement.
Perhaps many of these "distributed" computing efforts that are underway now would better be served by a DNA computer.
It has always seemed to me that the DNA sequence is like compiled code. So, when will someone invent the DNA disassembler so we can look at the "source code."
...
What would that look like?
main John_Smith()
{
eyes() = blue();
}
Syntax error: loose != lose, affect != effect, then!=than
With any luck you will have a much better base to build on.
I think biologists are trying the same thing - they are trying to "run" the genome rather than statically analyse it. So they knock stuff out to see if it has any visible effect, etc. But I think they are probably getting pathetic percentage coverage of gene expression compared to the 90% or more you would get from that mame run.
Pankaj Arora needs to get a life.
Before I answer any of the questions, please remember that in the interests of brevity I have omitted to insert the phrase "I think" before every sentence. Please feel free to insert the phrase yourself. It's my personal opinion and this is pretty leading edge stuff, so others in the field will probably disagree and in many cases with good reason. Anyhoo, here goes.
* 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.
ANSWER:
//Biology will not rewrite computing in the sense of hardware or even software. The reason is that biomolecules are pretty flaky. Once pesky bacterium gets in there and its curtains. Also, its has a habit of changing. Its just too UNCERTAIN.
However, all field sof engineering till benefit from in-depth study of biology. Biomechanics gives us new bridges, the immune system gives IBM an idea of self-repairing computers.
My view is that biology and computing will meet when computing ceases to be "digital". We are getting there. Big systems are now storing and processing so much data that the complexity is approaching that of simple molecules. Add quantum computing and who knows?
Biology is the ULTIMATE uncertain system. We need other UNCERTAIN systems to analyse it properly.
* 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?
I dont see biology having an effect. See above. Too hard to look after. As for theory, well a lot has already changed. A CS will already be familiar with MC, GA and GP, SA, Neural nets, Inference networks, all that good stuff. For my lifetime, and with Moores Law still on the statue book, I think semiconductors running code will ba the hardware, but the algorithms will borrow heavily from nature.
* 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?
Assuming simple biomolecules get used, the sort of things we are talking about are bacteriorhodopsin for information storage, nucleic acids for intractable problem domains and (maybe) proteins for fast switching. These are not nearly biological systems as even a molecular biologist would understand them, never mind the public. Now putting a rat brain into a microwave - thats another story but that is not going to happen, well, ever. QNX on a microcontroller can look after a microwave.
I can see a future for complex sensor arrays being used by human beings to control hardware or to communicate with each other. But again, speaking with my biology hat on, it is far more likely that you would want to do this via conventional hardware detecting electrical signals than by integrating hardware into the body at the molecular level.
* And perhaps the hottest issue of all: Is there anything inherently wrong with pursuing this avenue? What may be some of the consequences?
I dont see an issue in vitro. A company I was involved with was using protein arrays to map aspects of drug candidates' structures. We used a Linux cluster to crunch the data from the array to get an answer. The reason for this is that although QM and docking are neat, a REAL protein will bind and energetically minimise in femtoseconds where even a big cluster will take hours to do it using QM and compute time shortcuts, eg Gaussians.
The 64x10E6 currency unit question is what happens if we start tinkering in vivo. Rat brains con
I wish at was Friday, but I dont want to wish my life away. So I wish it was last Friday.
I think the pr0nographers have already significantly merged biology & computers. Or maybe that's just gynecology & computers...
"Obviously, I'm not an IBM computer any more than I'm an ashtray" (Bob Dylan)
Wow, this could make pirating more interesting. nucleus warez! woohoo!
Karma: Bad. Mostly because the only moderators that notice me are conservatives.
Just because it's repetative doesn't mean it's not servering a useful purpose. I think of timing marks on floppy disks that are before and after every sector on the disk. Remove the "junk bits" and you can no longer reliably read bits from the floppy.
is a sci-fi book that deals with an interesting possibile use for "Junk" DNA.
I highly recommend it (even if it does feel like a preface to the real story , presumably to be found in the sequel "Darwins Children")
For a slight spoiler _______________________
- Ever wondered why Gradualism in Evolutionarey theory is becoming less popular than Punctuated Equilibrium ? It's because we haven't found any "missing links" in the fossil record... Well this book contains an interesting speculation about why that might be...
In addition to solving computationally complex problems involving pattern matching (DNA computing), the ciruitry in the eye can do some real time processing. The retina is wired in a way that edges are automatically computed. It works like this - the retina is basically an array of light detectors (rods and cones) connected to nerve cells.The rod and cone generate small electrical signals when they receive a photon (a single quantum) of light. This signal flows into a the central region of a cell called a horizontal cell. The axon of the horizontal gathers signals from nearby cells, but of the opposite polarity and the amount of signal degradation is proportional to the distance from the center of the cell. Thus the cell computes a weighted average - for two neighbors - the weights might be -1/2 -1 3 -1 -1/2 - the result is something that approximates a laplacian, a 2nd derivative in space. For a uniform illuminated field, the 2nd derivative is zero - but when there is an abrupt change in illumination, the derivative is non-zero. If there is motion, then the array of horizontal cells will produce a signal that spatially follows the edge. Neat stuff
I think the biggest breakthrough will come when they start understanding "junk" DNA's function. Dismissing it as junk is just as silly as dismissing the cosmic background radiation as "noise". Important discoveries will be made and they will be the basis for amazing new drugs, just not before the quarter ends as the boss had hoped. Humans are a computing platform and DNA is (at least part) the program. Unfortunately, the program has been written by billions of programmers over millions of years. Oh yeah, and they NEVER commented their code no matter how ingenious or complicated a solution they came up with was.
Search on google for "fractal DNA" and you'll discover some people who might be on the right track.
These researchers at the University of Glasgow are studying a correlation between some bird species having relatively large genomes, and their long life spans and decreased senescence compared to mammals.
"I never really used Joe either but a stupid editor is a stupid editor." -D. Reed.
I am very interested in new programming styles for fault tolerant systems modeled around fundamental biology for checks and balances, i.e error control.
-=[ Who Is John Galt? ]=-
An interesting point is brought up here. It seems like, yes,biology and computering are merging, maybe not in the aesthetic sense but certainly in the sense that we are creating higher forms of life as we create more complex computers and it seems to make sense that the kind of life we create is somewhat like life on the planet today. If you're more interested in the philosophical side to all this check out: http://www.utm.edu/research/iep/f/function.htm
two different beasts?
you're absolutely wrong, and projects like this prove that, and there are far better examples as well.
the questions were good, the problem is a lot more needs to be known before they can be fully answered in terms of actual potential of the convergence of biology and computing. however, most reasonable estimates even indicate tremendous potential does indeed exist, and examples of molecular computing even from today, while not doing the full potential justice, exemplify that.
i'd agree to hang on to your segate stock, but the rest is just tunnel vision. biology can and will be controlled to allow for further "computing" capability and we will see the two converge. it's just a matter of when and how much convergence -versus- absolute change in the way we do things.
1. unknown
2. particles evolve into atoms
3. atoms evolve into molecules
4. molecules evolve into life
5. life evolves into intelligence
6. intelligence evolves into designed evolution
7. all matter is eventually part of a self redesigning omnipresent entity that is as close to being God as this universes laws will allow
8. unknown
We are today within a lifetime's time frame of the crux point seperating step 5 from step 6 (at least for this galaxy. Gaxaxies are too small to contain more than one or two ecosystems that create self-reproducing-starcraft).
Just because DNA is base 4 does not make just like computers! I bet every computer type thinks this--omigosh, that's 2 bits per unit! Yet another example of an expert in one field thinking it automatically transfers to others. Step away from the computer, please...
-Libertarian secular transhumanist
In most of Cordwainer Smith's stories, the computers are all biological (typically rat tissue in a laminated substrate). Seems he didn't put much faith in electronics. But most of his stories are about some serious genetic engineering and the societal effects.
________________________________________ History Must Not Fall Into The Wrong Hands ___________________________________
DNA is to computers what computers are to using rocks on the ground to count.
Look, if I have a series of 1s and 0s on a CD, will the CD become a cell?
No.
DNA is not only information, it is *active*: it is in a sense self-processing information.
Computers have a hell of a long way to go.
Some time ago I wrote site about DNA as seen through the eyes of a coder, which dovetails nicely with this article.
:-)
Highly recommended
bert.
I figure that around when nanotechnology gets figured out this will be at the tip of the bubble.
- Does quantum computing invalidate the Church-Turing hypothesis?
- Are biological systems capable of quantum computing?
If the answer to either of these questions is no, then current computer science will become the core of biological theory. "Yes" to either dramatically changes the relationships of the subjects; the mathematics of quantum computing bears little resemblance to the finite discrete mathematics now studied as the theoretical core of computer science.First off, this guy is an MIS major with a HEAVY CS background... a.k.a he failed out of CS after the his second semester. But on the bright side, he did learn how to make links in HTML. I mean, it was very helpfull to have a link to the exciting town of Rochester, MN. I know its so fun because I too worked at Mayo. I'm also glad the original poster took care to explain everything he knew about binary numbers for us... saved me some time from looking it up. I hope he gets all of his questions answered, and maybe then he won't turn out as Nick Burns, your company's computer guy.
I am gonna build an H-1B from hell. Wooaaaa haa ha haa ha!
.. if some Open Source types would write a decent, free sequence manipulation software. Something that could handle DNA and protein sequences and do some simple manipulations thereof. The best piece of software I know that does this is Vector NTI, and that costs $7000. Imagine all the tax-payer supported NIH grant money going to stuff like that (along with MicroSoft stuff, but that's a whole 'nother problem..) If I ever get a reasonable NIH grant, I'm going to see if I can bribe someone to write the software for open source.
Here are a few issues I wanted to address in this discussion.
1. DNA computers â" There has been a lot of hype about DNA computing and how it will revolutionize everything. I think this is never going to happen for several reasons. DNA is a fragile molecule and requires active maintenance by cells to retain its fidelity. Components made out of plastic, metal, and composite inorganic materials are much stronger, tougher, and long-lasting. Also, there has been a big trend towards solid-state electronics (of all kinds) because they are so much more reliable and sturdy. A DNA-based computer belies this trend and is therefore unlikely.
2. Genetic Algorithms â" The concept of using evolutionary principles to find solutions to complex problems is a good one. Generating a random array of solutions is not difficult, and optimizing through successive rounds of competition, selection, and mutation is feasible
3. Manipulating the environment â" You asked how biology will affect computing. The realization that biology works so well because it has evolved precise, molecular control of virtually every biochemical variable has profound implications for technology. Nanoscience is trying to realize this level of molecular control in technology. Computers will obviously be needed to realize this goal, and will also be profoundly affected by it.
4. The bottom line â" systems theory. Biology and bioinformatics have given us a lot to think about, especially in the context of complicated, self-referencing systems. I believe that the major effects of both disciplines on each other will be theoretical and âoebig pictureâ in scale. The fact is that microchips and enzymes have vastly different operating parameters and wonâ(TM)t likely be integrated directly. However, the concepts illuminated by studying biology (massively parallel processing, highly redundant systems, programmed mutation) have had and will continue to have a big effect on how we design computers.
5. Ethical implications â" I envision some big ethical issues as biology and technology become further integrated. As it stands, there is a fairly well-defined dividing line between what is biological and what is technological. When we are able to design cybernetic dogs that actually act like dogs, or when people can replace their eyes with broad-spectrum CCD detectors then that line will begin to blur. As nanotechnology and biotechnology advance, we will likely gain complete control over all life processes. Obviously, that has some wonderful and frightening implications. I guess weâ(TM)ll just have to keep our eyes and minds openâ¦.
Simple, use squid ganglia, or work out how to create neurons from stem cells extracted from fatty tissue to build your machine. Furthermore you control the architecture of the input and the output layers.
Why do you think that biological computers must resemble the human brain?
I'm a graduate student in chemical engineering at the University of Minnesota and this is my field of research...
(Sorta strange how Minnesota is a big center for medical devices / chemical engineering)
I'm in the process of designing systems of genes that interact to perform specific functions, like switches, oscillators, filters, etc. I won't go into a long harange over how it's done or the detailed specifics, because if you're really interested you can read my paper to be published in 'Computers in Chemical Engineering' that will be published sometime in November/December. (Yes, shameless self-promotion.)
Very briefly, systems of biological reactions occur in such small volumes and in such small concentrations that the traditional mathematics of describing chemical reactions breaks down. One requires probability theory and the usage of Markov processes, a type of stochastic process, to accurate describe what's really going on inside cells. One does this with a very handy algorithm developed by a guy named Daniel Gillespie (search the literature if you're interested) and big freakin computers. (I'm going to gloat: I'm getting access to the 54th fastest computer in the world. Oh, fellow Slashdotters, it brings a tear to my eyes...)
Here's my two bits on the subject of integrating biology and computers...
You have two distinct areas of computational biology (as Slashdotters know it) that will probably go into different directions. One can use computers to design biological systems in order to perform certain functions (medical, industrial, etc). This is entirely analogous to an engineer using a computer to design a factory before building it...and knowing exactly (or almost) how it will all turn out _prior_ to building it. This is also why buildings don't regularly fall down.
Then you have the Cyborg fantasy... Ie. Putting computers in your body to somehow enhance performance. Well, I would say that is numerous decades away because we currently lack the understanding of our brains...and the enhancement of our brains' computational speed is the only area in which digital computers can enhance human performance significantly (I discount super strength as novelty rather than enhancement.)
But, there is a useful aspect to the 'cyborg' fantasy: Using designed cells to enhance the performance of humans. Cures to many of our current diseases require significant changes to our DNA and/or microscopic structure of our cells. Currently, the approach has been to design (or discover randomly...) molecules that interact with our cells in a way that improves our health.
Now extend that thinking further... What about designing whole cells to interact with our cells in order to improve health. Here's some examples that may come true in the next twenty years:
A cell (of human origin) that lives benignly in one's body until it detects a protein that is only produced (in large quantity) by a cancerous cell. When it detects large numbers of that protein, it may do the following actions:
--Replicate itself quickly (in a controlled fashion, unlike cancerous cells, however)
--Warn the person by producing a visible indicator (ie. make the person urinate blue (har har))
--Recruit the person's immune system to attack the cancerous cell
--Attack the cancerous cell itself (phagocytosis, etc)
--Produce a molecule (a drug) that is known to kill that cancerous cell
Here's another example:
Someone designs a microbe that detects one or more specific chemicals in order to alert humans of its presence...a biosensor.
When the microbe (or its ten+ million neighbors) detects a specific chemical (Anthrax, ricin, smallpox, influenza, etc, etc), it produces a green fluorescent protein (GFP)..and tells all of its neighbors to produce GFP too. Thus one has a very sensitive, very specific biosensor. Place 'em in every airport and seaport in the world and one now has an (almost) instant indicator of the presence of such toxins...
So, to answer one o
Favorite
Hmm. Maybe. Sounds cool, but you are not in an isolation facility, are you?
First, consider hacking your own genome before hacking another human being's. They may not like what you do.
Second, mind out any non-human ones you hack don't turn into a plague.
Bio-hacking on the environmental scale is old and only sporadically successful - think "We'll just add this species to kill that other one, that got introduced because someone thought it would be aesthetically pleasing". It has brought us plagues of rabbits and cane toads. And much more.
No matter how cynical you become, it's never enough to keep up.
That isn't the whole story, and the running backwards analogy is just plain wrong.
Have you heard of something called parallel computation? RSA is doing it right here with DNA computing.
I suggest you read some background on what this means in terms of the nature of modern day computing, there's a good article here. Here's something from the second page of the article:
Now let's consider how you would solve a nontrivial example of the traveling salesman problem (# of cities > 10) with silicon vs. DNA. With a von Neumann computer, one naive method would be to set up a search tree, measure each complete branch sequentially, and keep the shortest one. Improvements could be made with better search algorithms, such as pruning the search tree when one of the branches you are measuring is already longer than the best candidate. A method you certainly would not use would be to first generate all possible paths and then search the entire list. Why? Well, consider that the entire list of routes for a 20 city problem could theoretically take 45 million GBytes of memory (18! routes with 7 byte words)! Also for a 100 MIPS computer, it would take two years just to generate all paths (assuming one instruction cycle to generate each city in every path). However, using DNA computing, this method becomes feasible! 10^15 is just a nanomole of material, a relatively small number for biochemistry. Also, routes no longer have to be searched through sequentially. Operations can be done all in parallel.
This is a huge deal for computing. Huge.
I went to Berkeley too. Have you heard of The Berkeley Initiative in Soft Computing (BISC)? Read their website, it will also increase your understanding as to how fuzzy logic translates into efficiencies and more to the point, performance. Not to mention the potential for efficent and high levels of data storage in DNA. The possibilites are amazing! A detailed understanding of evolutionary biology in the context of fuzzy logic and modern day computer computation (especially parallel) will blow your mind in terms of how things came to be, and how they fit so perfectly with certain operations. This is really the next big thing.
G.R. Bouchard, PhD
Associate Professor of Biophysics
[...]
In the cell, DNA is modified biochemically by a variety of enzymes, which are tiny protein machines that read and process DNA according to nature's design. There is a wide variety and number of these "operational" proteins, which manipulate DNA on the molecular level. For example, there are enzymes that cut DNA and enzymes that paste it back together. Other enzymes function as copiers, and others as repair units. Molecular biology, Biochemistry, and Biotechnology have developed techniques that allow us to perform many of these cellular functions in the test tube. It's this cellular machinery, along with some synthetic chemistry, that makes up the palette of operations available for computation. Just like a CPU has a basic suite of operations like addition, bit-shifting, logical operators (AND, OR, NOT NOR), etc. that allow it to perform even the most complex calculations, DNA has cutting, copying, pasting, repairing, and many others. And note that in the test tube, enzymes do not function sequentially, working on one DNA at a time. Rather, many copies of the enzyme can work on many DNA molecules simultaneously. This is the power of DNA computing, that it can work in a massively parallel fashion.
[...]
From here, someone else mentioned it in another post. Biology isn't the same, but it does have properties that are very close -- and they can be intergrated as the Author implies with his questions. Your evolution argument has nothing to do with the fact that biology can be applied to computers and that -- as the Author brought up -- computing theory might change thanks to biology. Read this page to get a better idea of where the Author is coming from.
Jake Lead
Salk Institute
Here, here, and here are just some of the reasons! ;-)
The analogues you describe - 0/1 to ACGT, bits to codons, arfe very low level. it is possible that we will be able to exploit the huge information packing density of DNA for computation (or, more accurately, storage, purposes). But if we do, it will only add another twist to Moores Law. We will be building determinate state machines using, in the broad sense, "programs". And there is nothing inherently wrong with this - it is just atoms, and the fact that we build them into useful patterns similar to those used by the body is irrelevant.
It is much hihger up the biological level at which new insights, new mechanisms, and new ethical questions might appear. W don't catually know all the levels between DNA and thought. DNA to proteins - yes. Big, but understood.But how proteins build cells, how cells interact, how the body knows which cells ti build, how nerve cells make a brain, how a brain works - these are still very much lost in fog, albeit fog with occasional gaps.
And it is in these level that the possibilities for something different and possibly objectionable lie. But it is not the DNA technologies which matter, it s whart you might call the Turing technologies thatr matter. If I manage to make a computer program which in every possible way simulates you, how does that computer program differ from you? If can simulate the verbal etc. responses, I could also attach actuators. Or you might become quadraplegic. Since the only way that I know that you are alive and feeling and have human rights is from your words and actions, does not something which behaves identically have the same rights.
If you answer yes, or even maybe, to that, what if I chop out a bit of your behaviour and make a robot with it. Maybe you are an expert chicken sexer. If I emulate the bit of your brain which has that knowledge, does it have any of your rights. OK, how if I clone that bit of your brain and copy the re1quisite skills into it? Now it is a bit of clone-you with a fragment of clone-personality. has it human rights, or is it a machine.
So I don't think any problems, or any strtl;ing new effects will come from the low-level mechanical level of biology. From the structural level, however, ther may come enormous novelty. For a start, nature has a far better grip on parallel processing than we can understand. Contingency and fault tolerance are also major features of natural systems.
Consciousness is an illusion caused by an excess of self consciousness.
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
it looks like fun, what is step 2?
p.s.
have you considered a human to computer interface yet? other than visual used with touch.
Damn MIS majors... don't know nuthin' about no inner workings of computers... Don't be a pussy! Computer Engineering is FUN! Anyway, I think making a computer WORK like a brain/person is a completely different problem than actually designing it to do the same processes on the most basic levels as a brain/person. The latter would be quite impossible with the technology of today, or of fifty years from now. The point is, that whether or not they actually work the same is not an issue, it's the result that counts. In Mark Twain's essay on the nature of man, he states that not only does the mind WORK like a machine, it IS a machine, and works on a certain, predefined set of rules. So you need not actually design a computer system to work in the same ways the body does, but to simply produce the same result with bits of data as opposed to DNA and cells. This is a much simpler method, since we do not fully understand how the mind works in the first place! However, we DO, for the most part, know the results we need to get from a computer that "thinks" and acts like a human. We can see the result we need by simply studying how humans act. umm... that's all i got today...
-SpeedoMask
Binary is a very simple approximation... there's no "grey", just black and white (one or not one).
Since we can do BINARY so gosh-darn fast, we have now developed ways to use TONS of fast simple APPROXIMATIONS to simulate complexity. To do this, we create STATISTICAL MODELS and the binary operations operate within the assumptions of those statistical models.
To the extent that our model assumptions are correct, our binary approximations may be correct - that is today's science.
On the other hand, biology (or perhaps more appropriately, biochemistry)is COMPLEX. It, too is WICKED FAST but it is not as simple. By definition, it is NOT an approximation, but it is THE REAL THING.
Now, can we break this complex biological stuff down into simple, almost-binary-like pieces? Sure - there are plenty of analogs like +/- ions, ATGC base pairs, etc.
Once we have those, can we use our advanced, binary tools to rebuild (around model-based statistical assumptions) pseudo-complex systems? Sure. That's a large part of bioinformatics and bioengineering.
Just remember, when we do things that way, the results are only valid within the model assumptions we made, which permitted the reconstruction of complex behavior from simple pieces. It is not reality, but a reflection of the statistical behavior of the simulation we have made.
So is it "wise" to work this way? If we have the REAL system in hand, why build an approximation whose behavior will be suspect?
Because it's too damn hard for us to understand the complex system without breaking it down like this (e.g. superposition). And we don't have (yet) better tools for breaking it down. And our super-fast binary computers are not really that fast when it comes to these complex systems (which is one reason why those "bio" chips in development show promise for being so much faster than the silicon we use now).
Hopefully one day we will have "bio" computing (not binary computing) which solves biological systems as fast as needed. Then we might make research progress on something like a Moore's Law curve - but not with binary.
Welcome to "bioengineering". Geesh - talk about a field in its infancy. Now, if you want a really difficult problem to attempt while you are engaged in bio-IT research, figure out how society can accommodate the COSTS associated with researching they way your colleagues are currently researching....