Literature DB >> 26307674

Verhulst and stochastic models for comparing mechanisms of MAb productivity in six CHO cell lines.

Nishikant Shirsat1, Mohd Avesh1, Niall J English1, Brian Glennon1, Mohamed Al-Rubeai2.   

Abstract

The present study validates previously published methodologies-stochastic and Verhulst-for modelling the growth and MAb productivity of six CHO cell lines grown in batch cultures. Cytometric and biochemical data were used to model growth and productivity. The stochastic explanatory models were developed to improve our understanding of the underlying mechanisms of growth and productivity, whereas the Verhulst mechanistic models were developed for their predictability. The parameters of the two sets of models were compared for their biological significance. The stochastic models, based on the cytometric data, indicated that the productivity mechanism is cell specific. However, as shown before, the modelling results indicated that G2 + ER indicate high productivity, while G1 + ER indicate low productivity, where G1 and G2 are the cell cycle phases and ER is Endoplasmic Reticulum. In all cell lines, growth proved to be inversely proportional to the cumulative G1 time (CG1T) for the G1 phase, whereas productivity was directly proportional to ER. Verhulst's rule, "the lower the intrinsic growth factor (r), the higher the growth (K)," did not hold for growth across all cell lines but held good for the cell lines with the same growth mechanism-i.e., r is cell specific. However, the Verhulst productivity rule, that productivity is inversely proportional to the intrinsic productivity factor (r x ), held well across all cell lines in spite of differences in their mechanisms for productivity-that is, r x is not cell specific. The productivity profile, as described by Verhulst's logistic model, is very similar to the Michaelis-Menten enzyme kinetic equation, suggesting that productivity is more likely enzymatic in nature. Comparison of the stochastic and Verhulst models indicated that CG1T in the cytometric data has the same significance as r, the intrinsic growth factor in the Verhulst models. The stochastic explanatory and the Verhulst logistic models can explain the differences in the productivity of the six clones.

Entities:  

Keywords:  Batch cultures; CHO cell lines; MAb productivity; Stochastic models; Verhulst logistic models

Year:  2015        PMID: 26307674      PMCID: PMC4960197          DOI: 10.1007/s10616-015-9910-9

Source DB:  PubMed          Journal:  Cytotechnology        ISSN: 0920-9069            Impact factor:   2.058


  34 in total

1.  Determinants and rate laws of growth and death of hybridoma cells in continuous culture.

Authors:  A P Zeng; W D Deckwer; W S Hu
Journal:  Biotechnol Bioeng       Date:  1998-03-20       Impact factor: 4.530

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Authors:  A P Zeng; W D Deckwer
Journal:  Biotechnol Prog       Date:  1999 May-Jun

3.  Large scale gene expression profiling of metabolic shift of mammalian cells in culture.

Authors:  Rashmi Korke; Marcela de Leon Gatti; Ally Lei Yin Lau; Justin Wee Eng Lim; Teck Keong Seow; Maxey Ching Ming Chung; Wei-Shou Hu
Journal:  J Biotechnol       Date:  2004-01-08       Impact factor: 3.307

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Authors:  M al-Rubeai; A N Emery; S Chalder; D C Jan
Journal:  Cytotechnology       Date:  1992       Impact factor: 2.058

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Authors:  Tom A Rapoport; Veit Goder; Sven U Heinrich; Kent E S Matlack
Journal:  Trends Cell Biol       Date:  2004-10       Impact factor: 20.808

Review 6.  How cells coordinate growth and division.

Authors:  Paul Jorgensen; Mike Tyers
Journal:  Curr Biol       Date:  2004-12-14       Impact factor: 10.834

7.  Logistic equations effectively model Mammalian cell batch and fed-batch kinetics by logically constraining the fit.

Authors:  Chetan T Goudar; Klaus Joeris; Konstantin B Konstantinov; James M Piret
Journal:  Biotechnol Prog       Date:  2005 Jul-Aug

8.  Dynamics of mitochondria during the cell cycle.

Authors:  Naokatu Arakaki; Takeshi Nishihama; Hiroyuki Owaki; Yoshinori Kuramoto; Midori Suenaga; Eri Miyoshi; Yuka Emoto; Hirofumi Shibata; Masayuki Shono; Tomihiko Higuti
Journal:  Biol Pharm Bull       Date:  2006-09       Impact factor: 2.233

9.  An algorithm for operating a fed-batch fermentor at optimum specific-growth rate.

Authors:  P Agrawal; G Koshy; M Ramseier
Journal:  Biotechnol Bioeng       Date:  1989-01-05       Impact factor: 4.530

10.  Cell cycle- and growth phase-dependent variations in size distribution, antibody productivity, and oxygen demand in hybridoma cultures.

Authors:  O T Ramirez; R Mutharasan
Journal:  Biotechnol Bioeng       Date:  1990-10-20       Impact factor: 4.530

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