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RNA Secondary Structure Prediction using Ant Colony Optimisation
"... It is important to know the secondary structure of RNA for applications such as drug development and modelling single stranded viruses. Predictive methods have various degrees of accuracy but are significantly faster and cheaper than empirical methods such as Xray crystallography. This project expl ..."
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It is important to know the secondary structure of RNA for applications such as drug development and modelling single stranded viruses. Predictive methods have various degrees of accuracy but are significantly faster and cheaper than empirical methods such as Xray crystallography. This project explores how Ant Colony Optimisation (ACO) performs on the task of RNA secondary structure prediction (RNASSP). An ant colony system is developed and experiments are conducted to examine its behaviour on this problem and to determine a good set of parameters. The performance and accuracy of this approach is then compared with alternative methods. The main findings are that whilst the accuracy of ACO is as good as dynamic programming for small sequences it is significantly slower to execute. For longer sequences both slower and less accurate than dynamic programming. i Acknowledgements I would like to thank my supervisor, Dr. Gillian Hayes, for her help and advice
Novel Heuristic Search Methods for Protein Folding and Identification of Folding Pathways
, 2006
"... Proteins form the very basis of life. If we were to open up any living cell, we would find, apart from DNA and RNA molecules whose primary role is to store genetic information, a large number of different proteins that comprise the cell itself (for example the cell membrane and organelles), as well ..."
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Proteins form the very basis of life. If we were to open up any living cell, we would find, apart from DNA and RNA molecules whose primary role is to store genetic information, a large number of different proteins that comprise the cell itself (for example the cell membrane and organelles), as well as a diverse set of enzymes that catalyze various metabolic reactions. If enzymes were absent, the cell would not be able to function, since a number of metabolic reactions would not be possible. Functions of proteins are the consequences of their functional 3D shape. Therefore, to control these versatile properties, we need to be able to predict the 3D shape of proteins; in other words, solve the protein folding problem. The prediction of a protein’s conformation from its aminoacid sequence is currently one of the most prominent problems in molecular biology, biochemistry and bioinformatics. In this thesis, we address the protein folding problem and the closelyrelated problem of identifying folding pathways. The leading research objective for this work was to design efficient heuristic search algorithms for these problems, to empirically
A Cooperative Combinatorial Particle Swarm Optimization Algorithm for Sidechain Packing
"... Particle Swarm Optimization (PSO) is a wellknown, competitive technique for numerical optimization with realparameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting ..."
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Particle Swarm Optimization (PSO) is a wellknown, competitive technique for numerical optimization with realparameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting the candidate solution vector into components, where each component is optimized by a particle. Particles move throughout a continuous space, their movements based on the influences exerted by static particles that then get feedback based on the fitness of the candidate solution. Here, the application of this technique to sidechain packing (a proteomics optimization problem) is investigated. To verify the efficiency of the proposed CCPSO algorithm, we test our algorithm on three sidechain packing problems and compare our results with the provably optimal result. Computational results show that the proposed algorithm is very competitive, obtaining a conformation with an energy value within 1 % of the provably optimal solution in many proteins.
Solving Lattice Protein Folding Problems by Discrete Particle Swarm Optimization
"... Abstract—Using computer programs to predict protein structures from a mass of protein sequences is promising for discovering the relationship between the protein construction and their functions. In the area of computational protein structure analysis, the hydrophobicpolar (HP) model is one of the ..."
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Abstract—Using computer programs to predict protein structures from a mass of protein sequences is promising for discovering the relationship between the protein construction and their functions. In the area of computational protein structure analysis, the hydrophobicpolar (HP) model is one of the most commonly applied models. The protein folding problem based on HP model has been shown as NPhard, to handle such an NPhard problem, this paper proposes a discrete particle swarm optimization algorithm (DPSOHP) to solve various 2D and 3D HP lattice modelsbased protein folding problems. The discrete particle swarm optimization method used in DPSOHP is based on the set concept and the possibility theory from a setbased PSO (SPSO). A selection strategy incorporating heuristic information and possibilities is adopted in DPSOHP. A particle’s positions in the algorithm are defined as a set of elements and the velocities of a particle are defined as a set of elements associated with possibilities. The experimental results on a series of 2D and 3D protein sequences show that DPSOHP is promising and performs better than various competitive stateoftheart evolutionary algorithms. Index Terms—Bioinformatics, Computational intelligence, Discrete particle swarm optimization, Hydrophobicpolar
Parallel Algorithms for Computational Biology
, 2007
"... Bioinformatics algorithms are computationally demanding and most of them are compute intensive. Hence we require huge amount of computing power. High Performance Computing as been traditionally believed to be an expensive affair. In this paper we investigate techniques that have been used to paralle ..."
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Bioinformatics algorithms are computationally demanding and most of them are compute intensive. Hence we require huge amount of computing power. High Performance Computing as been traditionally believed to be an expensive affair. In this paper we investigate techniques that have been used to parallelize conventional algorithms, coding patterns and architectural enhancements of platforms for better performance on Shared Memory and Distributed Memory architectures. 1 Sequence Comparison Sequence database searching is on of the most important tasks in Bioinformatics [RS00]. The SmithWaterman Algorithm (SMA) is the best known algorithm and the only algorithm which guarantees the finding of the local alignment amongst given two sequences. But it is also known that it is one of the slowest of all algorithms too [Far07]. Since the actual algorithm is very slow, many heuristic based approaches like BLAST [AGM + 90] have been tried. But these approaches being based on heuristics, do not give exact results. Hence, there has been considerable effort amongst the researchers to find alternate
Prediction of Protein Backbone Based on the Sliced Lattice Model ∗
, 2008
"... In the past decades, a significant number of studies on the prediction of protein 3D tertiary structures have been extensively made. However, the folding rules, the core issue of protein structure prediction, still stay unsolved. Given a target protein with its primary amino acid sequence, the prote ..."
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In the past decades, a significant number of studies on the prediction of protein 3D tertiary structures have been extensively made. However, the folding rules, the core issue of protein structure prediction, still stay unsolved. Given a target protein with its primary amino acid sequence, the protein backbone structure prediction (PSP) problem is to construct the 3D coordinates of αcarbon atoms on the backbone. We propose a hybrid method by combining the homology model and the folding approach to solve the PSP problem. Our idea of protein folding is performed on the combined sliced cubic lattice, which mixes coarse lattices with fine lattices. Our computation is based on the HP (HydrophobicPolar) model, combined with the constraint of disulfide bonds. The folding is optimized by using the ant colony optimization (ACO) algorithm. Our experimental results show that our prediction accuracy is better than previous methods by the measurement of RMSD. Key words: bioinformatics, protein backbone,
4 Ants solve TSP I The Travelling Salesman Problem:
, 2014
"... I Can simulation be used to improve distributed (agentbased) problem solving algorithms? I Yes: directly, in a supervised fashion (e.g. Neural Nets “simulators”) I But also, indirectly, via exploration & experimental parameter ..."
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I Can simulation be used to improve distributed (agentbased) problem solving algorithms? I Yes: directly, in a supervised fashion (e.g. Neural Nets “simulators”) I But also, indirectly, via exploration & experimental parameter
Hybrid Genetic Algorithm for Protein Folding Simulations in the 2D HP model
, 2008
"... The prediction of a protein’s structure from its aminoacid sequence is one of the most important problems in computational molecular biology. In this thesis, we demonstrate a hybrid genetic algorithm that simulates protein folding under the widely studied 2dimensional hydrophobic hydrophilic (H ..."
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The prediction of a protein’s structure from its aminoacid sequence is one of the most important problems in computational molecular biology. In this thesis, we demonstrate a hybrid genetic algorithm that simulates protein folding under the widely studied 2dimensional hydrophobic hydrophilic (HP) lattice model. The protein folding problem in the HP model is to find a lowest energy conformation, which is known to be a NPhard combinatorial problem. In comparison to similar algorithms, our algorithms performed well on standard benchmark instances. In addition, we present a graphical version of the genetic algorithm that uses sec