Editorial - (2023) Volume 9, Issue 1
Received: 02-Feb-2023, Manuscript No. Ipmedt-23-13457; Editor assigned: 06-Feb-2023, Pre QC No. Ipmedt-23-13457; Reviewed: 20-Feb-2023, QC No. Ipmedt-23-13457; Revised: 22-Feb-2023, Manuscript No. Ipmedt-23-13457 (R); Published: 28-Feb-2023, DOI: 2572-5475- 09.01-123
The discipline of computational biology faces a difficult research problem in DNA sequence reconstruction. It is impossible to sum up the evolution of the DNA in a few simple factors. As a result, a modelling strategy is required for examining DNA patterns. In this publication, we suggested a brand-new framework for studying DNA pattern. The suggested framework is divided into two phases. While the other step is used for the
reconstruction process, the first stage is used to analyse the evolution of DNA sequences. For the purpose of studying and forecasting the DNA sequence, we used cellular automata rules. The reconstruction technique is then updated, and the Particle Swarm Optimization algorithm and Probabilistic Cellular Automata are added into it. The integrated system increases the suggested framework's effectiveness and achieves the best transitional guidelines. The premise of our novel approach is that mutations are probabilistic occurrences. As a result, a PCA model can be used to simulate their progression.
DNA sequence reconstruction; Computational biology; Mutation rates; Probabilistic cellular automata (PCA); Particle swarm optimization (PSO)
The primary goal of this essay is to forecast the changes that take place in DNA during evolution by analysing diverse DNA sequences. In order to find symmetry relations, we employed a similarity score as fitness metric, which is suitable for many very lengthy sequences [1]. The CpG-methylation-deamination processes, which affect DNA areas where a guanine nucleotide follows a cytosine nucleotide in the linear sequence of bases, are the subject of the results [2]. The developed coloured paradigms are used to represent DNA evolution [3]. As a result, including probabilistic elements aids in creating a tool that can predict the likelihood of specific mutations [4]. Besides, it demonstrates their ability to manage intricate relationships. Several biological problems can be modelled using mathematical techniques and algorithms [5]. Thus, it would appear to be quite advantageous for both mathematics and biology to incorporate the findings from their respective fields [6]. Analysis of deoxyribonucleic acid, which is described by a sequence of bases, is one of the most important issues [7]. The four nucleic acid bases included in a DNA sequence adenine, cytosine, guanine, and G are complementary to one another [8].
The two strands of DNA are connected to one another and comprise a paired-strand molecule [9]. A pyrimidine on one strand and a purine on the other, or vice versa, form a hydrogen bond to form the linkage [10]. A DNA strand is created by a In the DNA replication process, a sequence of these four nucleotides is transcribed to produce another analogous sequence. It is characteristic for phylogeny because the DNA of a complex organism shares certain similarities with the DNA of a simpler one and because DNA preserves genetic information. There is no set process for modelling DNA. Some modelling methods call for the incorporation of ideas from other disciplines, including chemistry, physics, thermodynamics, and computer science. The focus of this study will be models built on the idea of probabilistic cellular automata. On the one hand, we investigated DNA evolution using our proposed probabilistic criteria. One way to think of a DNA strand is as a row of cells, each holding one of the four bases. This series is transcribed in the DNA replication process to produce a second comparable sequence. The difficulty in modelling DNA with cellular automata is in mapping the problem onto a realworld setting that complies with CA criteria. For the purpose of calculating mutation rates, we employed the sequence CpG methylation, followed by deamination and mutation. The principles that CA discovered during DNA modelling can provide a useful perspective on the influences of nearby base pairs on the evolution of DNA sequences. However, as CA proved to be a useful method for simulating DNA, we now employ similar probabilistic guidelines once more to rebuild DNA sequence.
We suggested a hybridised PCA and Particle Swarm Optimization approach for extracting the best transition rules. The proposed framework is more effective thanks to this integration, which also results in the best transition rules. The goal of this study is to identify neighbourhood rules that take the impact of mutations that occurred during the evolution of the sequences into account. Additionally, this study makes an effort to eliminate uncertainty in intermediate sequences. It is satisfied by adding stochastic components to our suggested method, which results in a more accurate simulation. The proposed model may also be useful for comprehending antibiotic-resistant microorganisms, which pose a serious threat to the public's health. With thousands of distinct resistance genes identified, only DNA analysis can provide comprehensive genetic data. For a precise assessment of the current resistance mechanism, this information from the retrieval is necessary. Genomes of bacteria change with time.
None
No conflict of interest
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Citation: Shukla A (2021) Reconstruction Based On Innovated Hybridization Technique of Probabilistic Cellular Automata and Particle Swarm Optimization by DNA Sequence. Mol Enzy Drug Targ, Vol. 9 No.1: 123.
