Computer programs for modeling mammalian cell batch and fed-batch cultures using logistic equations

Abstract

A MATLAB Ò toolbox was developed for applying the logistic modeling approach to mammalian cell batch and fed-batch cultures. The programs in the toolbox encompass sensitivity analyses and simulations of the logistic equations in addition to cell specific rate estimation. The toolbox was first used to generate time courses of the sensitivity equations for characterizing the relationship between the logistic variable and the model parameters. Subsequently, the toolbox was used to describe CHO cell data from batch and fed-batch mammalian cell cultures. Cell density, product, glucose, lactate, glutamine, and ammonia data were analyzed for the batch culture while fedbatch analysis included cell density and product concentration. In all instances, experimental data were well described by the logistic equations and the resulting specific rate profiles were representative of the underlying cell physiology. The 6-variable batch culture data set was also used to compare the logistic specific rates with those from polynomial fitting and discrete derivative methods. The polynomial specific rates grossly misrepresented cell behavior in the initial and final stages of culture while those based on discrete derivatives had high variability due to computational artifacts. The utility of logistic specific rates to guide process development activities was demonstrated using specific protein productivity versus growth rate trajectories for the 3 cultures examined in this study. Overall, the computer programs developed in this study enable rapid and robust analysis of data from mammalian cell batch and fed-batch cultures which can help process development and metabolic flux estimation.

Macroscopic modeling of mammalian cell growth and metabolism

“…For instance, Acosta et al ( 2007 ) use two asymmetric logistic equations for growth and nutrients and products. Logistic equations have been successfully used in a variety of applications to describe the dynamic of population growth; most of them involved bacterial growth (Gibson et al 1987 ; Tsoularis and Wallace 2002 ) but also mammalian cell growth (Goudar 2012a , b ; Goudar et al 2005 , 2009 ). This kind of model is particularly useful if the matrix S from Eq.…”

Technological tools and strategies for culturing human gut microbiota in engineered in vitro models

“…The two main factors influencing the biomass growth‐profile in a fed‐batch bioreactor are the inoculum density and the substrate depletion rate (Modak et al, 1986 ; Rumney & Rowland, 1992 ). When properly combined, these parameters induce a shift of the growth curve from the typical quadriphasic trend of the batch culture (i.e., lag, logarithmic, stationary and death phases) to enhanced logarithmic and stationary phases, as demonstrated both experimentally and theoretically (Figure 2a ) (Goudar, 2012 ; Landa et al, 2001 ; Modak et al, 1986 ). The possibility to culture the same bacteria population for prolonged times in a single procedure brings some advantages, such as the minimal incidence of mutations and multi‐compound fermentation (Kubitschek & Bendigkeit, 1964 ; Mora‐Villalobos et al, 2020 ).…”

Accelerating Biologics Manufacturing by Upstream Process Modelling

“…[13]. Macroscopic kinetic models simulating the dynamic state of a cell culture can gather essential information about cellular conditions (e.g., lag, exponential, stationary, decline phase), substrate uptake, metabolite, and productivity, as well as possible feeding adjustments [14][15][16][17][18][19]. A combination of online turbidity data resembling the cell concentration and a macroscopic kinetic model was already applied to estimate substrate and metabolite concentrations [11].…”