Literature DB >> 22285956

Dynamic model for CHO cell engineering.

Ryan P Nolan1, Kyongbum Lee.   

Abstract

Industrial CHO cell fed-batch processes have progressed significantly over the past decade, with recombinant protein titer consistently reaching the gram per liter level. Such improvements have largely resulted from separate advances in process and cell line development. Model-based selection of targets for metabolic engineering in CHO cells is confounded by the dynamic nature of the fed-batch process. In this work, we use a dynamic model of CHO cell metabolism to simultaneously identify both process and cell modifications that improve antibody production. Model simulations explored ca. 9200 combinations of process variables (shift temperature, shift day, seed density, and harvest day) and knockdowns (8 metabolic enzymes). A comprehensive examination of a simulated solution space showed that optimal gene knockdown clearly depends on the process parameters such as temperature shift day, shift temperature, and seed density. Knockdown of enzymes related to lactate production were the most beneficial; however, depending on the process conditions, modulating such enzymes yielded varying productivities, ranging from a reduction in final titer to greater than 2-fold improvement.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22285956     DOI: 10.1016/j.jbiotec.2012.01.009

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  6 in total

1.  Early integration of Design of Experiment (DOE) and multivariate statistics identifies feeding regimens suitable for CHO cell line development and screening.

Authors:  Alessandro Mora; Bernard Nabiswa; Yuanyuan Duan; Sheng Zhang; Gerald Carson; Seongkyu Yoon
Journal:  Cytotechnology       Date:  2019-11-09       Impact factor: 2.058

Review 2.  Engineering biological systems using automated biofoundries.

Authors:  Ran Chao; Shekhar Mishra; Tong Si; Huimin Zhao
Journal:  Metab Eng       Date:  2017-06-07       Impact factor: 9.783

Review 3.  Macroscopic modeling of mammalian cell growth and metabolism.

Authors:  Bassem Ben Yahia; Laetitia Malphettes; Elmar Heinzle
Journal:  Appl Microbiol Biotechnol       Date:  2015-07-22       Impact factor: 4.813

4.  Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer.

Authors:  Mahua Roy; Stacey D Finley
Journal:  Front Physiol       Date:  2017-04-12       Impact factor: 4.566

5.  Dynamic Modeling of CHO Cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy.

Authors:  Juan A Martínez; Dubhe B Bulté; Martha A Contreras; Laura A Palomares; Octavio T Ramírez
Journal:  Front Bioeng Biotechnol       Date:  2020-04-15

Review 6.  A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering.

Authors:  Osvaldo D Kim; Miguel Rocha; Paulo Maia
Journal:  Front Microbiol       Date:  2018-07-31       Impact factor: 5.640

  6 in total

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