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

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.

OPP Labeling Enables Total Protein Synthesis Quantification in CHO Production Cell Lines at the Single‐Cell Level

“…Global protein synthesis rates measured by OPP-labeling also provide an additional parameter that could be very useful for the improvement of current CHO bioprocess modeling approaches. [26,27] A recently developed genome scale metabolic model for CHO for instance predicted that only a small part of the total protein synthesis capacity is actually used for recombinant protein production. [3] Finally, the OPP technique adds to a toolbox of advanced (single) cell analysis methods that recently came into focus, for example Raman microspectroscopy.…”

Mechanochemical Synergism of Reactive Oxygen Species Influences on RBC Membrane

“…The mutual transformation of ROS during various chemical reactions is a distinctive feature. Kinetic models such as the Malthus, Verhulst, and Lotka–Volterra models are usually used for the mathematical modeling of the change in the number of specimens during biological and chemical processes [ 31 , 32 , 33 ]. In the case of mutual interactions of ROS in biological systems, the complex spatiotemporal pattern must be taken into account.…”