Towards rational glyco-engineering in CHO: from data to predictive models

TL;DR

This paper discusses the development of predictive models for glyco-engineering in CHO cells, emphasizing data quality, model selection, and parameter estimation to improve biopharmaceutical protein glycosylation predictions.

Contribution

It provides guidance on choosing appropriate kinetic models and experimental strategies for accurate glycosylation modeling in biopharmaceutical research.

Findings

Highlighting the importance of high-quality input data.

Guidelines for selecting suitable kinetic models.

Considerations for experimental design in glycosylation modeling.

Abstract

Metabolic modeling strives to develop modeling approaches that are robust and highly predictive. To achieve this, various modeling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.