César Manuel Vargas Benítez

Parallel Artificial Bee Colony Algorithm Approaches for Protein Structure Prediction Using the 3DHP-SC Model

This paper reports the use of the Artificial Bee Colony algorithm (ABC) for protein structure prediction using the three-dimensional hydrophobic-polar model with side-chains (3DHP-SC). Two parallel approaches for the ABC were implemented: a master-slave and a hybrid-hierarchical. Experiments were done for tuning the parameters of the ABC, as well as to adjust the load balance in a cluster-based computing environment. The performance of the parallel models was compared with a sequential version for 4 benchmark instances. Results showed that the parallel models achieved a good level of efficiency and, thanks to the co-evolution effect, the hybrid-hierarchical approach improves the quality of solutions found.

Hierarchical Parallel Genetic Algorithm applied to the three-dimensional HP Side-chain Protein Folding Problem

This work describes a Hierarchical Parallel Genetic Algorithm (HPGA) applied to the Protein Folding Problem (PFP). The modeling of the problem, using the 3DHP-Side-chain model, and details of the HPGA are presented. The effect of the energy weights in the performance of the algorithm was also studied. The HPGA was tested using three sets of benchmark sequences. Results show that the HPGA obtained biologically coherent results, suggesting the adequacy and efficiency of the HPGA for the problem.

Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm

This work presents a master-slave parallel genetic algorithm for the protein folding problem, using the 3D-HP side-chain model (3D-HP-SC). This model is sparsely studied in the literature, although more expressive than other lattice models. The fitness function proposed includes information not only about the free-energy of the conformation, but also compactness of the side-chains. Since there is no benchmark available to date for this model, a set of 15 sequences was used, based on a simpler model. Results show that the parallel GA achieved a good level of efficiency and obtained biologically coherent results, suggesting the adequacy of the methodology. Future work will include new biologically-inspired genetic operators and more experiments to create new benchmarks.

Population-based harmony search using GPU applied to protein structure prediction

This work presents a new evolutionary algorithm based on the standard harmony search strategy, called population-based harmony search PBHS. Also, this work provides a parallelisation method for the proposed PBHS by using graphical processing units GPU, allowing multiple function evaluations at the same time. Experiments were done using a benchmark of a hard scientific problem: protein structure prediction with the AB-2D off-lattice model. The performance and the solution quality were evaluated and compared using four implementations: two concerning the standard HS, one running in CPU and another running in GPU, and two implementations concerning the PBHS, also running in CPU and in GPU. Results show that the quality of solutions and speed-ups achieved by the PBHS is significantly better than the HS.

Parallelism, hybridism and coevolution in a multi-level ABC-GA approach for the protein structure prediction problem

This paper reports the hybridization of the artificial bee colony (ABC) and a genetic algorithm (GA), in a hierarchical topology, a step ahead of a previous work. We used this parallel approach for solving the protein structure prediction problem using the three‐dimensional hydrophobic‐polar model with side‐chains (3DHP‐SC). The proposed method was run in a parallel processing environment (Beowulf cluster), and several aspects of the modeling and implementation are presented and discussed. The performance of the hybrid‐hierarchical ABC‐GA approach was compared with a hybrid‐hierarchical ABC‐only approach for four benchmark instances. Results show that the hybridization of the ABC with the GA improves the quality of solutions caused by the coevolution effect between them and their search behavior. Copyright

A parallel genetic algorithm for protein folding prediction using the 3D-HP Side Chain model

This work presents a methodology for the application of a parallel genetic algorithm (PGA) to the problem of protein folding prediction, using the 3DHP-Side Chain model. This model is more realistic than the usual 3DHP model but, on the other hand, it is has a higher degree of complexity. Specific encoding and fitness function were proposed for this model, and running parameters were experimentally set for the standard master-slave PGA. The system was tested with a benchmark of synthetic sequences, obtaining good results. An analysis of performance of the parallel implementation was done, compared with the sequential version. Overall results suggest that the approach is efficient and promising.

A Heterogeneous Parallel Ecologically-Inspired Approach Applied to the 3D-AB Off-Lattice Protein Structure Prediction Problem

This paper applies a heterogeneous parallel ecology-inspired algorithm (pECO) to solve a complex problem from bioinformatics. The ecological-inspired algorithm represents a new perspective to develop cooperative evolutionary algorithms. Different algorithms are applied to compose the computational ecosystem in a heterogeneous model. The aim is to search low energy conformations for the Protein Structure Prediction problem, concerning the 3D-AB off-lattice model. Being a problem that demands a lot of computational effort, a parallel master-slave architecture is employed in order to allow the application of the computational ecosystem in a reasonable computing time. From the results, the pECO approach obtained the best conformation for the 13 amino-acid long sequence and competitive results for the other sequences.

Reconfigurable hardware computing for accelerating protein folding simulations using the harmony search algorithm and the 3D-HP-side chain model

Proteins are essentials to life and they have countless biological functions. They are synthesized in the ribosome of cells following a template given by the messenger RNA (mRNA). During the synthesis, the protein folds into an unique three-dimensional structure, known as native conformation. This process is called protein folding. Several diseases are believed to be result of the accumulation of ill-formed proteins.Therefore, understanding the folding process can lead to important medical advancements and development of new drugs.

Simulation of the dynamic behavior of one-dimensional cellular automata using reconfigurable computing

This paper presents the implementation of an environment for the evolution of one-dimensional cellular automata using a reconfigurable logic device. This configware is aimed at evaluating the dynamic behavior of automata rules, generated by a computer system. The performance of the configware system was compared with an equivalent software-based approach running in a desktop computer. Results strongly suggest that such implementation is useful for research purposes and that the reconfigurable logic approach is fast and efficient.

Gene Expression Programming for Evolving Two-Dimensional Cellular Automata in a Distributed Environment

This paper presents a novel distributed bio-inspired approach that uses Gene Expression Programming (GEP) to evolve transition rules for two-dimensional Cellular Automata (2D-CA). The 2D-CA are simulated in parallel using a masterslave distributed environment. The fitness function of the GEP ultimately measures the ability of a given CA to create a suitable solution for a complex Bioinformatics problem. To validate the proposed approach, extensive experiments were done dealing with a computationally expensive problem, that is considered to be one of the most important open challenges in Bioinformatics. Results of simulations show that the proposed approach was effective for the problem. Future works will investigate other distributed approaches of this approach, such as those based on General-Purpose Graphics Processing Units (GPGPU) or hardware-based accelerators. Finally, we believe that the method proposed in this work can be useful for other computational problems.