Multi-cell-line learning for the data-driven construction of mechanistic metabolic models

Yen-An Lu, Meghan G McCann, Wei Shou Hu, Qi Zhang

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Mammalian cells are commonly used as hosts in cell culture for biologics production in the pharmaceutical industry. Structured mechanistic models of metabolism have been used to capture complex cellular mechanisms that contribute to varying metabolic shifts in different cell lines. However, little research has focused on the impact of temporal changes in enzyme abundance and activity on the modeling of cell metabolism. In this work, we present a framework for constructing mechanistic models of metabolism that integrate growth-signaling control of enzyme activity and transcript dynamics. The proposed approach is applied to build models for three Chinese hamster ovary (CHO) cell lines using fed-batch culture data and time-series transcript profiles. Leveraging information from the transcriptome data, we develop a parameter estimation approach based on multi-cell-line (MCL) learning, which combines data sets from different cell lines and trains the individual cell-line models jointly to improve model accuracy. The computational results demonstrate the important role of growth signaling and transcript variability in metabolic models as well as the virtue of the MCL approach for constructing cell-line models with a limited amount of data. The resulting models exhibit a high level of accuracy in predicting distinct metabolic behaviors in the different cell lines; these models can potentially be used to accelerate the process and cell-line development for the biomanufacturing of new protein therapeutics.

    Original languageEnglish (US)
    Pages (from-to)2833-2847
    Number of pages15
    JournalBiotechnology and bioengineering
    Volume121
    Issue number9
    DOIs
    StatePublished - Sep 2024

    Bibliographical note

    Publisher Copyright:
    © 2024 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.

    Keywords

    • Chinese hamster ovary cell
    • mechanistic metabolic model
    • multi-task learning
    • parameter estimation
    • transcriptome

    PubMed: MeSH publication types

    • Journal Article

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