DNA 코딩과 진화연산을 이용한 함수의 최적점 탐색방법

백동화(Dong-Hwa Peak),강환일(Hwan Il Kang),김갑일(Kab Il Kim),한승수(Seung-Soo Han) 한국지능시스템학회 2001 한국지능시스템학회논문지 Vol.11 No.6

DNA computing은 Adleman의 실험 이후에 많은 여러 가지의 최적화 문제에 적용되어 왔다. DNA computing의 장점은 스트링의 길이가 가변적이고 4가지 염기를 이용하기 때문에 복잡한 문제에서 전역 최적점을 찾는데 기존의 다른 방법보다는 효율적이라는 것이다. 본 논문에서는 이진 스트링의 개체 집단 위에서 모의진화를 일으켜 효율적으로 최적 해를 탐색하는 GA(Genetic Algorithms)와, 생체 분자인 DNA를 계산의 도구 및 정보 저장도구로 사용하며, A(Adenine), C(Cytosine), G(Guanine), T(Thymine)등의 4가지 염기를 사용하는 DNA 코딩 방법을 이용하여 multi-modal 함수의 전역 최적점을 탐색하는 문제에서의 각각의 성능을 조사하였다. Selection, crossover, mutation 등의 GA연산자를 DNA 코딩에 동일하게 적용하였으며 최적의 해를 탐색하는데 걸리는 시간과 찾아낸 최적해의 값을 평가하였다. DNA computing has been applied to the problem of getting an optimal solution since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems. This paper presents DNA coding method for finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms (GA). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses DNA molecules and four-type bases denoted by the A(Ademine), C(Cytosine), G(Guanine) and T(Thymine). The selection, crossover, mutation operators are applied to both DNA coding algorithm and genetic algorithms and the comparison has been performed. The results show that the DNA based algorithm performs better than GA.

Sequence Design for Direct-Proportional Length-Based DNA Computing using Population-Based Ant Colony Optimization

Tri Basuki Kurniawan,Noor Khafifah Khalid,Zuwairie Ibrahim,Mohamad Shukri Zainal Abidin,Marzuki Khalid 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

Generally, in DNA computing, the DNA sequences used for the computation should be critically designed in order to reduce error that could occur during computation. In order to design a set DNA sequences for Direct-Proportional Length-Based DNA Computing (DPLB-DNAC), a Population-based Ant Colony Optimization (P-ACO) method is proposed. Previously, the DNA sequences for DPLB-DNAC are designed using graph method and Generate-and-Test approach, respectively. The both of methods are without the optimized objective functions process. The proposed method used four object functions in their process to obtain the best solutions. The results obtained from the proposed method are compared with the sequences generated by graph and Generate-and-Test methods. The results show that P-ACO approach can generate relatively better DNA sequences in some objectives than others. It can be concluded that proposed algorithm can obtain relatively a better set of DNA sequences for DPLB-DNAC.

생체분자 퍼셉트론의 신뢰성 향상을 위한 열역학 기반 가중치 코딩 방법

임희웅(Hee-Woong Lim),유석인(Suk I. Yoo),장병탁(Byoung-Tak Zhang) 한국정보과학회 2007 정보과학회논문지 : 소프트웨어 및 응용 Vol.34 No.12

생체분자 컴퓨팅은 DNA와 같은 생체 분자를 이용하여 정보를 표현하고 처리하는 새로운 컴퓨팅 패러다임이다. 작은 부피에 존재하는 무수히 많은 분자와 화학 반응에 내재된 대규모 병렬성은 새로운 개념의 고성능 계산 기법에 영감을 주었고 이를 바탕으로 다양한 계산 모델 및 문제 해결을 위한 분자알고리즘이 개발되었다. 한편 생체 분자를 이용한 정보처리라는 특징은 생물학 문제에 적용될 수 있는 가능성을 시사한다. 유전자 발현 패턴과 같은 생화학적 분자 정보의 분석을 위한 도구로서의 가능성을 가지고 있는 것이다. 이러한 맥락에서 DNA 컴퓨팅 기반의 생체분자 퍼셉트론 모델이 제안되었고 그 실험적 구현 결과가 제시된 바 있다. 생체분자 퍼셉트론의 핵심인 가중치 표현 및 가중치-합 연산은 입력 분자와 가중치를 표현하는 프로브 분자간의 경쟁적 혼성화 반응에 기반하고 있다. 그러나 그 혼성화 반응에서 열역학적 대칭성을 가정하고 있기 때문에 사용하는 프로브에 따라 가중치 표현의 오차가 있을 수 있다. 본 논문에서는 비대칭적인 열역학적 특성을 고려하여 일반화된 혼성화 반응 모델을 제시하고, 이를 바탕으로 신뢰성 있는 생체 분자 퍼셉트론의 구현을 위한 가중치 코딩 방법을 제안한다. 그리고 본 논문에서 제시한 가중치 표현 방법의 정확성을 이전 모델과 컴퓨터 시뮬레이션을 통해 비교하고 한계 오차를 만족하기 위한 조건을 제시한다. Biomolecular computing is a new computing paradigm that uses biomolecules such as DNA for information representation and processing. The huge number of molecules in a small volume and the innate massive parallelism inspired a novel computation method, and various computation models and molecular algorithms were developed for problem solving. In the meantime, the use of biomolecules for information processing supports the possibility of DNA computing as an application for biological problems. It has the potential as an analysis tool for biochemical information such as gene expression patterns. In this context, a DNA computing-based model of a biomolecular perceptron has been proposed and the result of its experimental implementation was presented previously. The weight encoding and weighted sum operation, which are the main components of a biomolecular perceptron, are based on the competitive hybridization reactions between the input molecules and weight-encoding probe molecules. However, thermodynamic symmetry in the competitive hybridizations is assumed, so there can be some error in the weight representation depending on the probe species in use. Here we suggest a generalized model of hybridization reactions considering the asymmetric thermodynamics in competitive hybridizations and present a weight encoding method for the reliable implementation of a biomolecular perceptron based on this model. We compare the accuracy of our weight encoding method with that of the previous one via computer simulations and present the condition of probe composition to satisfy the error limit.

GPU를 이용한 DNA 컴퓨팅 기반 패턴 분류기의 효율적 구현

최선욱(Sun-Wook Choi),이종호(Chong-Ho Lee) 대한전기학회 2009 전기학회논문지 Vol.58 No.7

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

A tool for the design of DNA base sequences for molecular circuits

Yuki Yoshida,Takashi Nakakuki 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10

Designing a set of DNA base sequences for a large-scale molecular circuit is a difficult problem in the field of DNA computing. A DNA circuit frequently suffers from unintended DNA strand displacements among strands; this problem can ruin the desired function of a DNA circuit. In this study, our aim was to develop a computational tool for the design of DNA base sequences by means of an original algorithm based on mismatches in a DNA base sequence. Validity of the proposed algorithm is confirmed by practical redesign examples.

DNA 컴퓨팅을 이용한 배낭 문제 해결

김은경,이상용 公州大學校 工學硏究院 生産技術硏究所 2004 論文集 Vol.12 No.-

Though Knapsack Problem appears to be simple, it is a NP-hard problem that is not solved in polynomial time as combinational optimization problems. To solve this problem, GA(Genetic Algorithms) was used in the past. However, there war a difficulties in real experiment because the conventional method didn't reflect the precise characteristics of DNA. This paper proposes code optimization DNA-Haskell, which is a functional language to reduce the error of DNA manipulation and to expect more accurate prediction during the DNA computing process. Code optimization DNA-Haskell was applied to (0,1) Knapsack Problem; as a result, it reduced experimental errors an compared with conventional methods, and found accurate solutions more rapidly.

딥러닝을 사용하는 IoT빅데이터 인프라에 필요한 DNA 기술을 위한 분산 엣지 컴퓨팅기술 리뷰

( Temesgen Seyoum Alemayehu ),조위덕 ( We-duke Cho ) 한국정보처리학회 2020 정보처리학회논문지. 컴퓨터 및 통신시스템 Vol.9 No.12

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

Model-Based Design and Control of Distributed DNA-Based Systems by Petri Nets

Rizki Mardian,Kosuke Sekiyama 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2016 NANO Vol.11 No.1

Coordination is an important aspect in developing distributed systems. While in silicon-based agents, i.e., mechanical robotics, designing individual-level behavior that may emerge into one global function is a typical approach to such systems, in DNA-based agents, programming of each individual's behavior still remains a challenge, as they are based on chemical reactions. These reactions occur immediately after all reactants have been mixed into a solution, which introduces challenges in logical control. In this work, we report a design strategy for coordinated eventdriven DNA-based systems by using a Petri Nets model. First, computational primitives based on DNA strand displacement reaction are introduced. Second, their molecular implementation is abstracted by Petri Nets for high-level design. Third, as our main contribution, we propose the model of interacting multi-agent systems based on DNA-only reactions. We verify our design via in silico simulation and show initial experiments of Petri Nets operators. From the obtained results, we argue that our design strategy is feasible for coordinating interaction of distributed DNA-based systems.

Design method for biomolecular circuit with plasticity

Kenta Hidaka,Takashi Nakakuki 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10

In this paper, we propose a design method for a biomolecular circuit that can dynamically change its function according to repetitive input stimuli. In DNA computing, which involves the construction of a DNA reaction system that executes desired information processing, the circuit function strongly depends on the network structure and reaction rate parameters of the chemical reaction system. In other words, it is necessary to design a DNA circuit specialized for a specific circuit function. We aimed to achieve “plasticity” of the DNA circuit by addressing the motivating example; for a two-input one-output DNA circuit, its function is initially YES logic but turns to OR logic as the repetitive input stimuli are given. Our proposed method was verified by numerical simulations based on reaction kinetics while utilizing dependable computer-aided software.

Standard Semen Parameters vs. Sperm Kinematics to Predict Sperm DNA Damage

Aghazarian Artin,Huf Wolfgang,Pflüger Heinz,Klatte Tobias 대한남성과학회 2021 The World Journal of Men's Health Vol.39 No.1

Purpose: The aims of this study were to associate sperm kinematics and standard semen parameters with sperm DNA damage and to evaluate whether the addition of sperm kinematics improve the multivariable prediction of sperm DNA fragmentation compared to standard semen parameters alone. Materials and Methods: We evaluated sperm kinematics, standard semen parameters, and DNA fragmentation index (DFI) in 122 men. Univariate and multivariate logistic regression models were fitted to evaluate the association of sperm kinematics and standard semen parameters with pathologically damaged sperm DNA (DFI≥26%), and receiver operating characteristics (ROC) curves were calculated for these models. Results: On univariate analyses, average velocity, curvilinear velocity, straight-line velocity, straightness (STR), beat-cross frequency (BCF), and the percentage of progressive motile sperm cells (PPMS) were significantly associated with pathologically damaged sperm DNA. Likewise, among standard semen parameters, sperm concentration, progressive motility, normal morphology, and vitality were found to be linked with sperm DNA damage. On the multivariate analysis, vitality was the strongest predictor of pathologically damaged sperm DNA with an area under the ROC curve (AUROC) of 88.3%. Adding STR, BCF, and PPMS to vitality increased the AUROC to the significant extent of 91.5%. Conclusions: Sperm vitality is the most accurate routine-based laboratory test for the prediction of pathologically damaged sperm DNA, but the addition of sperm kinematics increases its accuracy. Both standard semen parameters and sperm kinematics are complementary in predicting pathologically damaged sperm DNA, and might serve as a new tool to screen for fertile men.