Dynamic DNA computing model

  • Authors:

  • Zhiquan Frank Qiu;Mi Lu

  • Affiliations:

  • Department of Electrical Engineering, Texas A&M University, College Station, Texas;Department of Electrical Engineering, Texas A&M University, College Station, Texas

  • Venue:
  • Biocomputing
  • Year:
  • 2004

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Abstract

It has been evidenced that DNA computing can solve those problems which are currently intractable on the even fastest electronic computers. The algorithm design for DNA computing, however, is not straightforward: A strong background in both DNA molecule and computer engineering are required to develop efficient DNA computing algorithms. All of these algorithms need to start over from the very beginning when their initial condition changes. This can be frustrating, especially if the change in the initial condition is very small. The existing models from which a few DNA computing algorithms were developed are not able to accomplish this dynamic updating.For a long time, people have talked about the huge memory made possible through DNA computing due to the fact that each strand can be treated as both storage media and processor. There is, however, no existing application that has yet made use of this huge memory because, though, it is very easy to read from this memory, it is very difficult to write data to it. The memory can only be read after the data has been stored.In this paper, a new DNA computing model is introduced based on which new algorithms are developed to solve the 3-Coloring problem. These new algorithms are presented as vehicles for demonstrating the advantages of the new model, and can be expanded to solve other NP-complete problems. They have the advantage of dynamic updating, so an answer can be changed based on modifications to the initial condition. The new model makes use of this huge memory by generating a "lookup table" during the process of implementing the algorithms. If the initial condition changes, the answer will change accordingly. In addition, the new model has the advantage of decoding all the answer strands in the final pool very quickly and efficiently. The advantage provided by this new model makes DNA computing both efficient and attractive in solving computationally intense problems.