COMPUTATIONAL ANALYSIS OF DNA SEQUENCES BASED UPON AN INNOVATIVE MATHEMATICAL HYBRIDIZATION MECHANISM OF PROBABILISTIC CELLULAR AUTOMATA AND PARTICLE SWARM OPTIMIZATION

摘要

The deoxyribonucleic acid (DNA) sequence reconstruction problem is a very complex issue of computational biology. In this paper, we introduce a modified procedure for the reconstruction process based on probabilistic cellular automata (PCA) integrated with a particle swarm optimization (PSO) algorithm. PSO is utilized to detect the optimal and adequate transition rules of cellular automata (CA) for the reconstruction process. This integration makes our algorithm more efficient. The evolution of organisms occurs due to mutations of DNA sequences. As a result, we attempt to model the evolutions of DNA sequences using our proposed system. In Particular, we determine the impact of neighboring DNA base pairs on the mutation process. We used CA rules for analysis and prediction of the DNA sequence. Our innovative model leans on the hypothesis that mutations are probabilistic events. As a result, their evolution can be simulated as a PCA model, and this enables us to discover the effects of some neighborhood base-pairs on a DNA segment evolution. The main target of this paper is to analyze various DNA sequences and try to predict the changes that occur in DNA sequences during evolution (mutations). We use a similarity score as our measure of fitness to detect symmetry relations, which in turn makes our method appropriate for comparison of numerous extremely long sequences. Phylogenetic trees are exhibited in order to view our investigated samples. Unlike using Markov chains, our proposed technique does not reveal biases in mutation rates that depend on the neighboring bases, which indicates the effect of neighbors on mutations. Incorporating probabilistic components in our proposed technique helps to produce a tool capable of foretelling the likelihood of specific mutations.