NP-Problems and molecular computation

In summary, DNA-based molecular computation has had some early successes in solving NP problems, but has yet to overcome the inherent limitations.
  • #1
ryokan
252
5
Ten years ago (Science 1994;266:1021-4), Adelman built the first DNA based computer to solve the so-called Hamilton Path problem for seven nodes. SAT problems were solved with similar approaches by Lipton (Science 1995;268:542-5), with a DNA-based algorithm and Faulhammer (PNAS 2000;97.1385-9) who used a RNA-based algorithm .

The initial excitement following these reports was constrained by major limitations: mainly, the need of a massive amount of DNA and the exponential increase in the chance of error to solve larger-scale problems.

I pose the following questions:
Do you think that molecular computation is promising?
Since its first use, DNA computation had a seemingly slow development. Why? I think that besides the intrinsic limitations, this field, by interdisciplinary, is less attractive than other "hot" topics in Biology.
 
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  • #2
ryokan said:
I pose the following questions:
Do you think that molecular computation is promising?
Since its first use, DNA computation had a seemingly slow development. Why? I think that besides the intrinsic limitations, this field, by interdisciplinary, is less attractive than other "hot" topics in Biology.

I think molecular computation is as promising as any radically new technique now on the menu, in particular I believe it will pay off quicker than quantum computation.

My experimence is that it is the nature of computational methods to have a brilliant beginning and then stall, or appear to. Look at expert systems and neural computing. What have they done for us lately? Actually in fact quite a bit, but it doesn't make the papers because it's no longer a breakthrough.
 
  • #3
selfAdjoint said:
in particular I believe it will pay off quicker than quantum computation.

If so, why?
 
  • #4
Because quantum computation still requires new, as yet unkown, technology to reach its potential, but nanophysics can build chips with technology already in use.
 
  • #5
selfAdjoint said:
Because quantum computation still requires new, as yet unkown, technology to reach its potential, but nanophysics can build chips with technology already in use.
I partially agree.

Effectively, there is now technology that allows to use DNA as a tool for diagnostic purposes in form of microarrays or “biochips”.

There are also solid lines of research on the use of DNA to construct nanomachines.

And there are very interesting findings on a “cellular computer” that after diagnose a cancer cell based in its specific RNA levels, would release single-stranded DNA molecules to interfere with such RNA, causing the self-destruction of the cell.

But as far as I know, the molecular approach to solve some NP problems was based only in molecular computing in liquid phase at a laboratory – scale . And it is here where the intrinsic limitations of DNA computing (due to the error-prone enzymatic activities and the string length and amount of DNA required) seems incapable to surpass the conventional electronic computers to solve NP problems.

So, It would be possible that the technology required to quantum computing be developped before the overcome of the limitations inherents to DNA computing to solve NP problems.
 
  • #6
Computation is more easy inside than outside

Problems with DNA computation in vitro contrast with the facilities to computation that cells have.

I think that a good example of NP-problem that cells solve constantly is the correct folding of their proteins (Levinthal's paradox).

It is possible that the actual methods of "molecular computation" using DNA, with their inherent limitations, must be replaced by a possible form of "cellular computation" widely focused on NP problems with isomorphism to the problems that cells solve daily.
 

1. What are NP-Problems?

NP-Problems, or non-deterministic polynomial time problems, are a class of computational problems that are difficult to solve using traditional algorithms. These problems require a large amount of time and resources to solve, and there is no known efficient algorithm that can solve them in a reasonable amount of time.

2. How are NP-Problems related to molecular computation?

NP-Problems can be solved using molecular computation, which utilizes molecules and chemical reactions to perform computations. This is because molecular computation has the potential to solve complex problems in a parallel and efficient manner, making it a promising approach for solving NP-Problems.

3. What are the advantages of using molecular computation for NP-Problems?

Molecular computation can potentially solve NP-Problems much faster than traditional computers, as it can perform computations in parallel and at a molecular scale. This makes it a promising solution for solving complex problems that are difficult for traditional computers to handle.

4. Are there any limitations to using molecular computation for NP-Problems?

One limitation of using molecular computation for NP-Problems is that it is still a relatively new and developing field, so there are still many challenges and limitations that need to be addressed. Additionally, the scalability of molecular computation is still a concern, as it is difficult to scale up to larger and more complex problems.

5. What is the current state of research on NP-Problems and molecular computation?

The research on NP-Problems and molecular computation is ongoing and constantly evolving. Scientists are continuously exploring new methods and techniques to improve the efficiency and scalability of molecular computation for solving NP-Problems. However, there is still much to be discovered and developed in this field.

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