The surface-based DNA computing is one of the methods of DNA computing which uses DNA strands immobilized on a solid surface. In this paper, we applied surface-based DNA computing for solving the dominating set proble...The surface-based DNA computing is one of the methods of DNA computing which uses DNA strands immobilized on a solid surface. In this paper, we applied surface-based DNA computing for solving the dominating set problem. At first step, surface-based DNA solution space was constructed by using appropriate DNA strands. Then, by application of a DNA parallel algorithm, dominating set problem was resolved in polynomial time.展开更多
In this paper, the sticker based DNA computing was used for solving the independent set problem. At first, solution space was constructed by using appropriate DNA memory complexes. We defined a new operation called “...In this paper, the sticker based DNA computing was used for solving the independent set problem. At first, solution space was constructed by using appropriate DNA memory complexes. We defined a new operation called “divide” and applied it in construction of solution space. Then, by application of a sticker based parallel algorithm using biological operations, independent set problem was resolved in polynomial time.展开更多
Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a f...Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a fixed code length. According to the characteristics of the problem, a DNA algorithm solving the minimum spanning tree problem is given. The effectiveness of the proposed method is verified by simulation. The advantages and disadvantages of this algorithm are discussed.展开更多
The biggest bottleneck in DNA computing is exponential explosion, in which the DNA molecules used as data in information processing grow exponentially with an increase of problem size. To overcome this bottleneck and ...The biggest bottleneck in DNA computing is exponential explosion, in which the DNA molecules used as data in information processing grow exponentially with an increase of problem size. To overcome this bottleneck and improve the processing speed, we propose a DNA computing model to solve the graph vertex coloring problem. The main points of the model are as follows： The exponential explosion prob- lem is solved by dividing subgraphs, reducing the vertex colors without losing the solutions, and ordering the vertices in subgraphs; and the bio-operation times are reduced considerably by a designed parallel polymerase chain reaction （PCR） technology that dramatically improves the processing speed. In this arti- cle, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNA computing model. The experiment showed that not only are all the solutions of the graph found, but also more than 99% of false solutions are deleted when the initial solution space is constructed. The powerful computational capability of the model was based on specific reactions among the large number of nanoscale oligonu- cleotide strands. All these tiny strands are operated by DNA self-assembly and parallel PCR. After thou- sands of accurate PCR operations, the solutions were found by recognizing, splicing, and assembling. We also prove that the searching capability of this model is up to 0（3^59）. By means of an exhaustive search, it would take more than 896 000 years for an electronic computer （5 x 10^14 s-1） to achieve this enormous task. This searching capability is the largest among both the electronic and non-electronic computers that have been developed since the DNA computing model was proposed by Adleman＇s research group in 2002 （with a searching capability of 0（2^20））.展开更多
Recently, the possibility of using DNA as a computing tool arouses wide interests of many researchers. In this paper, we first explored the mechanism of DNA computing and its biological mathematics based on the mechan...Recently, the possibility of using DNA as a computing tool arouses wide interests of many researchers. In this paper, we first explored the mechanism of DNA computing and its biological mathematics based on the mechanism of biological DNA. Then we integrated DNA computing with evolutionary computation, fuzzy systems, neural networks and chaotic systems in soft computing technologies. Finally, we made some prospects on the further work of DNA bio soft computing.展开更多
To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, o...To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.展开更多
DNA computing is a novel method for solving a class of intractable computational problem, in which the computing can grow exponentially with problem size. Up to now, many accomplishments have been achieved to improve ...DNA computing is a novel method for solving a class of intractable computational problem, in which the computing can grow exponentially with problem size. Up to now, many accomplishments have been achieved to improve its performance and increase its reliability. Hamilton Graph Problem has been solved by means of molecular biology techniques. A small graph was encoded in molecules of DNA, and the 'operations' of the computation were performed with standard protocols and enzymes. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.展开更多
The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can b...The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.展开更多
As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the o...As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.展开更多
文摘The surface-based DNA computing is one of the methods of DNA computing which uses DNA strands immobilized on a solid surface. In this paper, we applied surface-based DNA computing for solving the dominating set problem. At first step, surface-based DNA solution space was constructed by using appropriate DNA strands. Then, by application of a DNA parallel algorithm, dominating set problem was resolved in polynomial time.
文摘In this paper, the sticker based DNA computing was used for solving the independent set problem. At first, solution space was constructed by using appropriate DNA memory complexes. We defined a new operation called “divide” and applied it in construction of solution space. Then, by application of a sticker based parallel algorithm using biological operations, independent set problem was resolved in polynomial time.
文摘Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a fixed code length. According to the characteristics of the problem, a DNA algorithm solving the minimum spanning tree problem is given. The effectiveness of the proposed method is verified by simulation. The advantages and disadvantages of this algorithm are discussed.
基金The authors are grateful for the support from the National Natural Science Foundation of China （61632002, 61379059, and 61572046）.
文摘The biggest bottleneck in DNA computing is exponential explosion, in which the DNA molecules used as data in information processing grow exponentially with an increase of problem size. To overcome this bottleneck and improve the processing speed, we propose a DNA computing model to solve the graph vertex coloring problem. The main points of the model are as follows： The exponential explosion prob- lem is solved by dividing subgraphs, reducing the vertex colors without losing the solutions, and ordering the vertices in subgraphs; and the bio-operation times are reduced considerably by a designed parallel polymerase chain reaction （PCR） technology that dramatically improves the processing speed. In this arti- cle, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNA computing model. The experiment showed that not only are all the solutions of the graph found, but also more than 99% of false solutions are deleted when the initial solution space is constructed. The powerful computational capability of the model was based on specific reactions among the large number of nanoscale oligonu- cleotide strands. All these tiny strands are operated by DNA self-assembly and parallel PCR. After thou- sands of accurate PCR operations, the solutions were found by recognizing, splicing, and assembling. We also prove that the searching capability of this model is up to 0（3^59）. By means of an exhaustive search, it would take more than 896 000 years for an electronic computer （5 x 10^14 s-1） to achieve this enormous task. This searching capability is the largest among both the electronic and non-electronic computers that have been developed since the DNA computing model was proposed by Adleman＇s research group in 2002 （with a searching capability of 0（2^20））.
文摘Recently, the possibility of using DNA as a computing tool arouses wide interests of many researchers. In this paper, we first explored the mechanism of DNA computing and its biological mathematics based on the mechanism of biological DNA. Then we integrated DNA computing with evolutionary computation, fuzzy systems, neural networks and chaotic systems in soft computing technologies. Finally, we made some prospects on the further work of DNA bio soft computing.
基金This Project was supported by the National Nature Science Foundation (60274026 ,30570431) China Postdoctoral Sci-ence Foundation Natural +1 种基金Science Foundation of Educational Government of Anhui Province of China Excellent Youth Scienceand Technology Foundation of Anhui Province of China (06042088) and Doctoral Foundation of Anhui University of Scienceand Technology
文摘To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.
基金Supported by the CNSF(60274026 60174047+1 种基金 30370356) Supported by the Anhui Provinc'e Educational Committee Foundation(2003Kj098)
文摘DNA computing is a novel method for solving a class of intractable computational problem, in which the computing can grow exponentially with problem size. Up to now, many accomplishments have been achieved to improve its performance and increase its reliability. Hamilton Graph Problem has been solved by means of molecular biology techniques. A small graph was encoded in molecules of DNA, and the 'operations' of the computation were performed with standard protocols and enzymes. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.
基金This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401)in part by the 2022 Yeungnam University Research Grant.
文摘The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.
文摘As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.
