Cell strain similarity evaluation method and similar cell strain culture medium formula recommendation method

Abstract

The invention discloses a cell strain similarity evaluation method and a similar cell strain culture medium formula recommendation method. And cell strain characteristics are learned from cell parallel experiment data by using cell strain culture, metabolism and other data by adopting an AI technology, so that the similarity of the cell strains is quantitatively evaluated, and the method has good accuracy and generalization ability. The quantitative cell strain similarity can be used for providing related historical data reference for same and similar cell experiments, and even directly providing related experimental guidance, so that the experimental exploration range is reduced, the experimental time is shortened, and the experimental efficiency is improved. And cell culture medium formula recommendation is performed by utilizing a cell strain similarity analysis technology, so that historical culture medium formula data can be directly utilized, additional culture medium formula data does not need to be added, and the efficiency is further improved.

Description

technical field [0001] The invention belongs to the field of biotechnology, and more specifically relates to a method for evaluating similarity of cell lines and a method for recommending a culture medium formula for similar cell lines. Background technique [0002] From the perspective of artificial intelligence modeling, there are various types of cell lines. Taking CHO cells as an example, the types of CHO cell lines include CHO-K1, CHO-DG44, CHO-GS, etc. The protein expression of CHO cell lines is as follows: The difference between fusion protein, monoclonal antibody, and double antibody, and the content and proportion of various amino acids in the protein are also different. In addition, there are also differences in the shape and size of cell lines. Therefore, there are more or less differences in the characteristics of different cell lines. [0003] Diverse cell lines will present great difficulties in terms of evaluation standards, data analysis and processing. On ...

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Problems solved by technology

[0007] Aiming at the above deficiencies or improvement needs of the prior art, the present invention provides a method for evaluating similarity of cell lines and a method for recommending medium formulations of similar cell lines. The mathematical model is self-learning and fitting, and the characteristics of the cell lines are mathematically vectorized to quantitatively evaluate the similarity of similar cell lines in different aspects. Based on the similarity of the cell lines, similar experimental conditions can be explored, such as the recommendation of the medium formula, so that Shorten the experiment to explore the practice, thereby solving the technical problem that the existing technology cannot quantitatively evaluate the similarity between cell lines, nor can it use the similarity between cell lines

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