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Related Experiment Video

Updated: Jun 4, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Advancing biopharmaceutical process development by system-level data analysis and integration of omics data.

Jochen Schaub¹, Christoph Clemens, Hitto Kaufmann

¹Department of Biopharmaceutical Process Science, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany, jochen.schaub@boehringer-ingelheim.com.

Advances in Biochemical Engineering/Biotechnology

|February 4, 2011

Summary

This summary is machine-generated.

Advanced bioprocess data analysis using multivariate data analysis (MVDA), metabolic flux analysis (MFA), and gene expression mapping improved recombinant protein production. This framework enhanced IgG titers in Chinese hamster ovary (CHO) cell lines by over 50%.

Related Experiment Videos

Last Updated: Jun 4, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

Biotechnology
Bioprocess Engineering
Systems Biology

Background:

Efficient bioprocesses are crucial for cost-effective recombinant therapeutic protein manufacturing.
Process improvement necessitates comprehensive analysis of both process and cell-level data.

Purpose of the Study:

To present a framework for advanced bioprocess data analysis integrating multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis.
To apply this platform for optimizing IgG production in Chinese hamster ovary (CHO) cell lines.

Main Methods:

Multivariate data analysis (MVDA) including Principal Component Analysis (PCA), k-means clustering, and Partial Least Squares (PLS) models for macroscopic bioprocess data.
Metabolic Flux Analysis (MFA) and gene expression analysis to investigate intracellular characteristics of high-performance cell cultivations.
Pathway analysis for mapping large-scale gene expression datasets.

Main Results:

MVDA identified correlations between essential amino acids and product concentration, and differentiated between productivity-driven and process control-driven processes.
MFA revealed phenotypic characteristics in central carbon metabolism, amino acid metabolism, and energy yield.
Gene expression analysis identified 247 deregulated metabolic genes involved in amino acid metabolism, transport, and protein synthesis.
Maximal product titer increased from approximately 5 g/L to 8 g/L in an IgG-producing CHO cell line.

Conclusions:

The integrated data analysis framework provides comprehensive insights into bioprocess performance.
This approach enables rationalization and significant improvement of bioprocesses for recombinant protein production.
The study demonstrates a successful application of the framework, leading to a substantial increase in product titer.