Researching DNA Computation: Questions & Answers

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Discussion Overview

The discussion revolves around the concept of DNA computation, exploring its potential, limitations, and comparisons to traditional computing methods. Participants raise questions about the practicality and efficiency of DNA-based systems, particularly in performing arithmetic operations and sorting results.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation

Main Points Raised

  • One participant questions the usefulness of DNA computation, particularly in handling large sets of results from operations like addition, suggesting that sorting through numerous answers poses a significant challenge.
  • Another participant proposes that in a massively parallel system, individual nodes could perform sorting and querying to narrow down results, although the effectiveness of this approach is not universally accepted.
  • Concerns are raised about the current limitations of DNA computation in performing operations like binary addition, with references to the inefficiencies observed in solving problems such as the Hamiltonian path problem.
  • Some participants discuss alternative algorithms for addition that may reduce the need for carry bits, but express uncertainty about their applicability to DNA computation.
  • There is skepticism regarding the advantages of DNA computing compared to other parallel architectures, with some participants questioning whether the research is driven by practical applications or novelty.
  • One participant notes that DNA computing lacks the gain found in conventional transistors, which could hinder its effectiveness in processing signals.
  • Another participant mentions that smart drug delivery systems are a potential application of DNA computing, suggesting a practical direction for the research.

Areas of Agreement / Disagreement

Participants express a range of views on the practicality and efficiency of DNA computation, with no consensus reached on its advantages or the effectiveness of current methodologies. Some participants are skeptical about its applications, while others see potential in specific areas like drug delivery.

Contextual Notes

Participants highlight limitations in current DNA computation methods, particularly regarding arithmetic operations and the sorting of results. There is also a noted dependence on specific algorithms and the challenges of implementing them in a biological context.

Who May Find This Useful

This discussion may be of interest to researchers in computational biology, computer science, and those exploring alternative computing architectures, as well as individuals curious about the intersection of biology and computation.

cam875
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I have a problem with DNA computation that is currently being researched. Currently all the research is being done because cells are so parallel but how is that even useful. I keep thinking if you have this so called cell computer calculate every possible addition problem possible for a 32 bit number than what is the point your going to have to still have enough memory to store all the answers and sort through them. I mean its the same with all this quantum computation stuff. Your just going to end up with millions of different answers which you have to sort. Maybe I am just not understanding how cells are parallel and efficient for computation so can someone please enlighten me. Thanks in advance.
 
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In a massively parallel system (like DNA computing), the computing nodes themselves are supposed to do the 'sorting'. Suppose each node is checking if its own number divides a given large number. You could then have each node query three neighbors "do you have the answer, or have you heard about the answer?", then repeat a number of times until you can query just a few and find out if the answer is found. You can narrow it down with similar techniques.
 
but what if you wanted to add 10110101 to 100001010011, isn't that where current dna computation efforts are behind on. And even if dna can sort itself and all that, you still have to ravage through the results which is where it lacks also according to the articles I have read it took them along time to ravage through the results of the hamiltonian path problem.
 
cam875 said:
but what if you wanted to add 10110101 to 100001010011, isn't that where current dna computation efforts are behind on. And even if dna can sort itself and all that, you still have to ravage through the results which is where it lacks also according to the articles I have read it took them along time to ravage through the results of the hamiltonian path problem.

If you wanted to add two (large) binary numbers together there are parallelizable algorithms that don't require as much carrying as the standard 'grade school' algorithm. I don't know how these could be adopted for DNA computation, though.

On the computational power of DNA has a good overview of DNA computing as of last decade. The chart on p. 3 is especially helpful.
 
ok but it does seem strange, because it would seem like there is only one way to really add two numbers together and that involves a carry bit and everything just like todays full electronic binary adders do. But thanks for the link there.
 
cam875 said:
ok but it does seem strange, because it would seem like there is only one way to really add two numbers together and that involves a carry bit and everything just like todays full electronic binary adders do. But thanks for the link there.

A carry-lookahead adder requires much less carry propagation than the standard ripple-carry adder:
Al Aho and Jeff Ullman, Foundations of Computer Science ch. 13, pp. 716-721.

One way to reduce the number of carry bits for *multiplying* would be the Wallace tree multiplier:
Chris S. Wallace, "A Suggestion for a Fast Multiplier", IEEE Transactions on Electronic Computers EC-13:1 (1964), pp. 14-17.

These require O(log n) carry propagation for n-bit numbers, compared to O(n) for a the usual method.
 
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but than it involves you having to multiply, does it not? which is just adding multiple times in a sense or am I confused?
 
I'm not in this field... but I hear people talk about DNA computing from time to time. I've always wondered, what's the point? There are plenty of other massively parallel architectures out there which sound much easier to implement. What's the advantage of DNA computing? Or is this something we're doing "just because it's cool"?
 
DNA computing is just a curiosity. It suffers from a fundamental problem, and that is of gain. Conventional transistors have gain, which allows the signal to be lifted out of the noise.

No such luck with DNA. They are probably using a roomfull of equipment to read the DNA.
 
  • #10
cam875 said:
but than it involves you having to multiply, does it not? which is just adding multiple times in a sense or am I confused?

A carry-lookahead adder does not require multiplication. I'm not sure if I understand your question...
 
  • #11
Cincinnatus said:
I'm not in this field... but I hear people talk about DNA computing from time to time. I've always wondered, what's the point? There are plenty of other massively parallel architectures out there which sound much easier to implement. What's the advantage of DNA computing? Or is this something we're doing "just because it's cool"?

Smart drug delivery systems, apparently, seem to be the goal of the research.
 
  • #12
kingdomof said:
Smart drug delivery systems, apparently, seem to be the goal of the research.

ah, of course, that makes sense. Thanks!
 

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