Simulation And Implementation Of Dna Computing Algorithm Based On The Ic

1994, Adleman resolve a classic NP-complete problem called Hamiltonian path problem (include 7 vertex). Since then, the DNA computing terms as the junction of biotechnology and computer science get rapid development.Although there are some advantages of DNA computing, such as highly parallelism compute, high computational speed, big storage capacity, low-energy consumption and abundant in natural resources etc., the realization of DNA computing is mainly base on the chemical method which have high error rate. With the development of current VLSI technology, the speed of IC increasing and the prices of storage equipment become lower and lower. Taking the above into account, we decide to use IC to realize DNA computing.This paper mainly discusses follows:1. Discuss and summarize the method, the principle, the main realize method, the advantages and disadvantages of DNA computing. Learne the principles of the MCU and FPGA's software development environment and propose a method of using IC to realize DNA computing.2. Traditional computer is good at solve serial task. DNA computing is better than traditional computer in some points, it can search and get all the answers at the same time. Combine the traditional computer and DNA computing, we design computer model based on DNA computing.3. According to the principle of DNA computing, we design a algorithm to solve SAT problem, and check the answer with two examples, one is four variables problem and the other is six variables problem. We also use MCU as controller and FPGA as circuit realize the problem above. Under the present experimental conditions, although we can solve any number of variables in theory, we only solved 11 variables problem.4. Analyze the 0-1 planning model, and according to the principle of the issue, a DNA computing algorithm is proposed. We use an example verified the algorithm.5. Analyze the principle equally distribution problem, base on original model, we simulate example of seven variables. It indicate the correctness of the algorithm again.6. Maximal clique problem in graph theory is an important issue. This paper attempts to solve a five variables maximal clique problem. The Algorithms currently only suit for the smaller number of variables.