Noor Khafifah Khalid

Evaluation of Ordering Methods for DNA Sequence Design Based on Ant Colony System

Hybridization between a DNA sequence and its base-pairing complement is crucial in DNA computing to retrieve the information stored in DNA sequences and operate a computation processes. Therefore, much works have focused on designing the DNA sequences for a reliable molecular computation. In this paper, Ant Colony System (ACS) is proposed to solve the DNA sequence design problem. ACS, which is based on Ant Colony Optimization (ACO) is an improvement of Ant System (AS) that uses some agents to obtain the solutions based on the pheromone in their colony. The DNA sequence design problem is modeled by four nodes, representing four DNA bases (A, T, C, and G) using the nearest-neighbor thermodynamic parameters Watson-Crick base-pair DeltaGdeg37 as distances between one node to other nodes. Seven ordering methods for ACS are presented in this study in order to obtain the best set solution. The performance of each of those methods are compared and evaluated to decide the best ordering method for this application.

Implementation of an ant colony system for DNA sequence optimization

DNA computation exploits the computational power inherent in molecules for information processing. However, in order to perform the computation correctly, a set of good DNA sequences is crucial. A lot of work has been carried out on designing good DNA sequences to archive a reliable molecular computation. In this article, the ant colony system (ACS) is introduced as a new tool for DNA sequence design. In this approach, the DNA sequence design is modeled as a path-finding problem, which consists of four nodes, to enable the implementation of the ACS. The results of the proposed approach are compared with other methods such as the genetic algorithm.

Implementation of Binary Particle Swarm Optimization for DNA Sequence Design

In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence design problem is a multi-objective problem, and it can be evaluated using four objective functions, namely, H measure , similarity, continuity, andhairpin . There are several ways to solve a multi-objective problem, such as value function method, weighted sum method, and using evolutionary algorithms. However, in this paper, common method has been used, namely weighted sum method to convert DNA sequence design problem into single objective problem. Binary particle swarm optimization (BinPSO) is proposed to minimize the objective in the problem, subjected to two constraints: melting temperature and GC content . Based on experiments and researches done, 20 particles are used in the implementation of the optimization process, where the average values and the standard deviation for 100 runs are shown along with comparison to other existing methods. The results obtained verified that BinPSO can suitably solve DNA sequence design problem using the proposed method and model, comparatively better than other approaches.

A Model to Optimize DNA Sequences Based on Particle Swarm Optimization

In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an elementary problem and one of the most practical and important research topics. Although the design of DNA sequences is dependent on the protocol of biological experiments, it is highly required to establish a method for the systematic design of DNA sequences, which could be applied to various design constraints. Therefore, much work has focused on designing the DNA sequences to avoid incorrect or undesirable DNA computations. In this paper, a model for particle swarm optimization (PSO) implementation is proposed to solve the DNA sequence design problem. PSO simulates a commonly observed social behavior where members of a group tend to follow the leader of the group. To demonstrate the correctness of the proposed model, a basic PSO with 10 particles is implemented to minimize an objective of DNA sequence design, which is the H-measure.

A Noise Elimination Procedure for Printed Circuit Board Inspection System

Image difference operation is frequently used in automated printed circuit board (PCB) inspection system as well as in many other image processing applications. During the implementation, this operation brings along the unwanted noise due to misalignment and uneven binarization. Thus, this paper proposes a method to eliminate, if possible, or to reduce as much as possible such noise during the computation of defect detection. This paper used a template PCB image and the tested PCB image as the input. Image subtraction operation will be applied between the images. The results of applying the proposed method showed a significant improvement during the real-time inspection of printed circuit boards.

An Optimized Ant System Approach for DNA Sequence Optimization

DNA computing is a new computing paradigm which uses bio-molecules as information storage media and biochemical tools as information processing operators. This field has shown many successful and promising results for various applications. Since DNA reactions are probabilistic in nature, different result could be produced even in the same situations, which can be regarded as errors in computing. In order to overcome the drawbacks, many works have focused on the design or error-minimized DNA sequence to improve the reliability of DNA computing. Although the design of DNA sequences is dependent on the protocol of biological experiments, it is highly required to establish a method for the systematic design of DNA sequences, which could be applied to various design constraints. In the previous paper, Ant System approach has been proposed to solve the DNA sequence optimization problem. In this paper, the optimized parameters of Ant System approach are searched to improve the performance of the Ant System for DNA sequence optimization.