The Application Of DNA Strand Displacement In Encryption And Combinatorial Optimization Problems

At the current stage of semiconductor development,Moore’s Law may have already reached its limit,and the development of traditional computing technology is facing bottlenecks,such as limited computing speed and high energy consumption.Therefore,there is an urgent need for a new computing method to break through these limitations.DNA computing,with its natural advantages of high storage capacity,parallelism,and low energy consumption,has become one of the best alternatives to semiconductor computing and is widely used in the fields of data security and combinatorial optimization problem solving.This research is divided into the following two parts:(1)In the field of data security,encryption algorithms are of great significance as a security measure.However,in most existing DNA-based encryption methods,only DNA code transformation is used to achieve encryption,which does not fully utilize the advantages of DNA computing.To address this issue,this paper proposes a biochemical experiment-based encryption method based on DNA strand displacement technology,which uses the parameters of DNA computing experiments as part of the key to increase the difficulty of cracking.In addition,this method pushes the research of DNA strand displacement in the encryption field from the simulation stage to the biochemical experiment stage.Finally,experimental analysis demonstrates that this method has high key sensitivity,a larger key space,and the ability to resist statistical attacks.(2)In the field of data security,combinatorial optimization problems can be used to address a range of security-related issues.Moreover,many encryption algorithms involve solving combinatorial optimization problems during data transmission and storage processes.In this regard,this article proposes a DNA computing model that extends the application of DNA computing in the field of encryption to more general mathematical problems,specifically combinatorial optimization problems.NP-complete problems are very important in combinatorial optimization problems,and researchers have always maintained a high level of enthusiasm for them because they cannot be solved in polynomial time(unless P = NP).With the parallelism of DNA computing,many DNA computing methods have been proposed to solve NP-complete problems.However,existing DNA computing models are based on specific NP-complete problems,which means that one model can only be used to solve one NP-complete problem.Although all NP-complete problems have been proven to be reducible to each other in polynomial time,there is no biological method to achieve this complex reduction.To address this,this thesis discusses technologies about design a universal DNA computing model that can be used to solve a series of NP-complete problems.This process relies entirely on biological operations without the need for traditional computers to perform reduction.Finally,it is demonstrated through simulation experiments and biochemical experiments that the model has the ability to solve the minimum dominating set,maximum independent set,and minimum vertex cover problems.