Modeling and Analysis of Protein Synthesis and DNA Mutation Using Colored Petri Nets

• Published in: IEEE access 2018-01, Vol.6, p.22386-22400
• Main Authors: Yang, Jinliang, Pu, Haitao, Lian, Jian, Gu, Jason, Fan, Mingqu
• Format: Article
• Language: English
• Subjects: Amino acids; Analytical models; Biological activity; Biological processes; Biological system modeling; Chemical synthesis; colored Petri Net; Deoxyribonucleic acid; deoxyribonucleic acid (DNA) mutation; Discrete event systems; DNA; Gene sequencing; Hidden Markov models; Mathematical analysis; Mathematical model; Mathematical models; Model accuracy; modeling; Molecular biology; Mutation; Nucleotides; Petri nets; Polypeptides; Protein synthesis; Proteins; type determination
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2825782

In molecular biology, protein synthesis is an essential biological process of generating specific proteins in living systems. Many mathematical models are designed to characterize the process of the flow of genetic information from deoxyribonucleic acid (DNA) to protein; however, most of them cannot provide detailed and observed steps to describe and analyze this fundamental biological process. Colored Petri Net (CPN), as a mathematical method widely applied in the process analysis of discrete event dynamic systems, has increasingly become an innovative and efficient theoretical approach for exploring the biological processes. Aiming at describing the entire process of protein synthesis, a CPN-based model has been designed that successfully and intuitively represents the flow of genetic information involving transcription and translation. DNA mutations are permanent alterations in the nucleotide sequence of the DNA strand, while different types of mutations have various effects on the synthesized proteins. Based on the proposed protein synthesis model, we put forward a new CPN model to identify the type of mutation, which is beneficial for analyzing whether the mutation has impacts on the structure or function of the produced protein. The mutation position and bases mutated rate are obtained by contrasting the nucleobases on DNA sequences, while the mutation type is determined via the alignment of the amino acids in the polypeptide chain. The model's effectiveness and accuracy are illustrated by biological mutation examples, indicating this method offers great superiority in modeling and analyzing the complex biological processes.