Abstract

<p class="Abstract">Much of our scientific, technological, and economic future depends on the availability of an ever-increasing supply of computational power. However, the increasing demand for such power has pushed electronic technology to the limit of physical feasibility and has raised the concern that this technology may not be able to sustain our growth in the near future. It became important to consider an alternative means of achieving computational power. In this regard, DNA computing was introduced based on the usage of DNA and molecular biology hardware instead of the typical silicon based technology. The molecular computers could take advantage of DNA's physical properties to store information and perform calculations. These include extremely dense information storage, enormous parallelism and extraordinary energy efficiency. One of the main advantages that DNA computations would add to computation is its self - parallel processing while most of the electronic computers now use linear processing. In this paper, the DNA computation is reviewed and its state of the art challenges and applications are presented. Some of these applications are those require fast processing, at which DNA computers would be able to solve the hardest problems faster than the traditional ones. For example, 10 trillion DNA molecules can fit in one cubic centimeter that would result in a computer that holds 10 terabytes of data. Moreover, this work focuses on whether a large scale molecular computer can be built.</p>

Highlights

  • The amount of information gathered on the molecular biology of DNA over the last 40 years is almost overwhelming in scope

  • Deoxyribonucleic acid is the molecular structure which provides the genetic instructions used in the development and for the functioning of a cell of all known living organisms

  • Researchers have pointed out that if using Adelman’s method to solve a 200 city Hamiltonian Path (HP) problem the amount of DNA required would be utterly impossible (Ryu, W 2007) [3], (Paun, G 1998) [4]. Another factor that places limits on his method is the error rate for each operation. Since these operations are not deterministic but stochastic in nature, each step contains statistical errors, limiting the number of iterations we can do successively before the probability of producing an error becomes greater than producing the correct result

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Summary

Introduction

The amount of information gathered on the molecular biology of DNA over the last 40 years is almost overwhelming in scope. Getting bogged down in too much biochemical and biological details. We will concentrate only on the information most relevant to DNA role in computing

The structure of DNA and its role in the cell
DNA as a data structure
DNA operations in parallel
Adleman’s Experiment
Generate all possible routes
Select paths that start and end with the correct cities
Select paths that contain the correct number f cities
Select paths that have a complete set of cities
Conclusion to Adleman’s Experiment
DNA Advantages and Limitations
DNA and Cryptography Systems
Advances in DNA Computing
Conclusion
10 Authors
Full Text
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