Generic framework for mining cellular automata models on protein-folding simulations.
Cellular automata model identification is an important way of building simplified simulation models. In this study, we describe a generic architectural framework to ease the development process of new metaheuristic-based algorithms for cellular automata model identification in protein-folding trajectories. Our framework was developed by a methodology based on design patterns that allow an improved experience for new algorithms development. The usefulness of the proposed framework is demonstrated by the implementation of four algorithms, able to obtain extremely precise cellular automata models of the protein-folding process with a protein contact map representation. Dynamic rules obtained by the proposed approach are discussed, and future use for the new tool is outlined.
Departamento de Sistemas, Universidad del Cauca, Popayán, Colombia.|Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Colombia.
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Diaz
Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Colombia.
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Tischer
D000465:Algorithms; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D008956:Models, Chemical; D056004:Molecular Dynamics Simulation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
D011506:Proteins