Next Article in Journal
Dual Solutions and Stability Analysis of Magnetized Hybrid Nanofluid with Joule Heating and Multiple Slip Conditions
Previous Article in Journal
Phytochemical Compounds of Branches from P. halepensis Oily Liquid Extract and S. terebinthifolius Essential Oil and Their Potential Antifungal Activity
Previous Article in Special Issue
Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study
Open AccessArticle

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01850, USA
*
Author to whom correspondence should be addressed.
Processes 2020, 8(3), 331; https://doi.org/10.3390/pr8030331
Received: 26 December 2019 / Revised: 3 March 2020 / Accepted: 6 March 2020 / Published: 11 March 2020
(This article belongs to the Special Issue Application of Systems Engineering Principles to Bioprocessing )
Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines. View Full-Text
Keywords: genome-scale metabolic model; time-series transcriptomics; omics data integration; CHO cell genome-scale metabolic model; time-series transcriptomics; omics data integration; CHO cell
Show Figures

Graphical abstract

MDPI and ACS Style

Huang, Z.; Yoon, S. Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering. Processes 2020, 8, 331.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop