Enhancing the understanding of CHO cell culture metabolic traits through integrated first-principle modelling and data-based parameter estimation

The focus of the thesis is to develop hybrid techniques that enhance the parameter estimation procedure used to predict the behavior of CHO cellular lines. CHO cells are a type of mammalian cell extensively adopted in pharmaceutical industries to produce drugs and component essential to human health such as monoclonal antibodies, hormones, enzymes, vaccines and other. In addition, these cellular cultures are characterized by rapid growth and adaptability to various culture conditions. Rhe first objective is the enhancement of the existing model. The current approach for predicting the behavior of CHO cellular lines utilizes first principal models that try to capture the main chemical and biological process that affect the cells. The aim is to update this model by incorporating new insights gained from cellular cultures. The refined model should demonstrate improved efficiency in predicting the behavior of CHO cellular lines. After that, an Integration of metabolomics data is necessary. In the project, metabolomics data will be integrated into the estimation of the most important parameters of the first principle model. The goal is to leverage the biological information contained in the metabolomic dataset to enhance the predictive capability for CHO cellular lines. Resuming, by combining the strengths of existing models with new considerations derived from cellular cultures and metabolomics data, the developed hybrid procedures aim to improve the accuracy and reliability of parameter estimation in predicting the behavior of CHO cellular lines