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

机译:Verhulst模型和随机模型用于比较6种CHO细胞系中单克隆抗体生产率的机制

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摘要

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 (rx), held well across all cell lines in spite of differences in their mechanisms for productivity—that is, rx 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.
机译:本研究验证了先前发表的随机和Verhulst方法,可用于模拟分批培养的6种CHO细胞系的生长和单克隆抗体生产力。细胞计数和生化数据用于模拟生长和生产力。随机解释模型的开发是为了增进我们对增长和生产力的潜在机制的理解,而Verhulst机械模型的开发是出于可预测性。比较了两组模型的参数的生物学意义。基于细胞数据的随机模型表明生产力机制是细胞特异性的。但是,如前所示,建模结果表明G2 + ER表示高生产率,而G1 + ER表示低生产率,其中G1和G2是细胞周期阶段,ER是内质网。在所有细胞系中,生长被证明与G1期的累积G1时间(CG1T)成反比,而生产力与ER成正比。 Verhulst的规则“内在生长因子(r)越低,生长(K)越高”,并不适用于所有细胞系的生长,但适用于具有相同生长机制的细胞系-即r是细胞具体。但是,Verhulst生产力规则认为,生产力与内在生产力因子(rx)成反比,尽管它们在生产力机制上存在差异,但在所有细胞系中都保持了良好的关系-也就是说,rx不是特定于细胞的。如Verhulst的逻辑模型所描述的那样,生产率概况与米利斯(Michaelis)-曼滕(Menten)酶动力学方程非常相似,表明生产率在本质上更可能是酶促的。随机模型与Verhulst模型的比较表明,细胞计数数据中的CG1T与Verhulst模型中的内在生长因子r具有相同的意义。随机解释模型和Verhulst Logistic模型可以解释六个克隆的生产力差异。

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