A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation

Abstract

Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.

Titrating bacterial growth and chemical biosynthesis for efficient N-acetylglucosamine and N-acetylneuraminic acid bioproduction

“…Model simulation. MATLAB R2019a was used for kinetic modeling of cell growth, as well as for target metabolite (GlcNAc) and by-product (acetate) synthesis [61][62][63] . The development of the kinetic model was mainly based on four kinetics, including the growth kinetic model of acetic acid-producing E. coli developed by Luli et al, Monod equation describing nutrient limitation, acetate synthesis and specific growth rate equations determined by Basan et al, and Michaelis-Menten equation describing the biosynthesis pathway of GlcNAc 61,64,65 .…”

Dynamical models for metabolomics data integration

“…Due to this heterogeneity, various metabolic modeling approaches have been developed that are, more or less, tailored or restricted to specific types of data [3]. However, in many studies, a combination of different types of data is acquired and integration of all these data is possible with dynamical models [13,14].…”

“…Dynamical metabolic models have been applied in various contexts. Recurrently pursued objectives are 1) understanding dynamical processes [15,16], for example, through comparison of competing models [17]; 2) inferring control mechanisms and rate-limiting steps [13,18]; and 3) leveraging those to push some system of interest in specific directions, for example, for strain optimization or drug target identification) [14,19,20].…”

“…Their comparison of the results from the dynamic model to those derived from a static model indicated higher accuracy of the former. Ramos et al [14] integrated both extracellular and intracellular time-resolved metabolite measurements from different experimental settings using a dynamical model comprising 33 state variables and describing central carbon metabolism and cell growth. They identified dif- Quality of fit to time-resolved in vivo and in vitro measurements of metabolite and phosphoprotein abundance after different stimulations.…”

“…Given sufficiently informative data, model parameters can be inferred, for example, via optimization- [13][14][15][16][17][18][19][20][21]23] or sampling-based [22] approaches. However, this is computationally costly as, for most cases, it involves thousands to millions of numerical ODE simulations.…”