Estimation of the Amount of Recombinant Protein A Secretion Using Fuzzy Regression

Abstract Background and purpose: Since protein A is considered an important protein from medical, medicinal, genetic engineering, and biotechnology point of view, the present study attempted to investigate and determine to what extent protein A is produced through regression, in addition to the production conditions of the protein. Thus, a figure was introduced as for the estimation of the amount of protein A. Methods: With the introduction of fuzzy mathematics and its combination with statistical methods, the kinds of regression models for estimating the amount of unknown variables were introduced. The utilization of fuzzy regression was developed from 1982 through the introduction of regression models, and the fuzzy data was based on a kind of linear plan. One of these regression models is fuzzy regression which considers the features of fuzzy numbers and the estimation obtained through them, and it has a higher level of reliability. Results: In the present study, fuzzy regression method was introduced, and the number usage of this model in estimating the amount of secretion of protein A was investigated. It was then confirmed that this estimation method had a higher level of reliability. The type of regression used in this article was fuzzy regression that had a higher confidence level than the point classic regression. At the same time, the number of triangular fuzzy number was used in the current research in terms of computational handling, and it was found that triangular fuzzy number was much easier to use in comparison with the other species. Conclusion: Secretion and extracellular production of recombinant protein is a wide production method which is currently developing. In the present study, it was observed that the statistical methods for improving the process in medical biotechnology are ideal methods. It was also documented that for laboratory designing of this important protein and achieving the best and most improved conditions for production and secretion, its amount of production must first be calculated through statistical methods.