Abstract
Production of bio-pharmaceuticals in cell culture, such as mammalian cells, is challenging. Mathematical models can provide support to the analysis, optimization, and the operation of production processes. In particular, unstructured models are suited for these purposes, since they can be tailored to particular process conditions. To this end, growth phases and the most relevant factors influencing cell growth and product formation have to be identified. Due to noisy and erroneous experimental data, unknown kinetic parameters, and the large number of combinations of influencing factors, currently there are only limited structured approaches to tackle these issues. We outline a structured set-based approach to identify different growth phases and the factors influencing cell growth and metabolism. To this end, measurement uncertainties are taken explicitly into account to bound the time-dependent specific growth rate based on the observed increase of the cell concentration. Based on the bounds on the specific growth rate, we can identify qualitatively different growth phases and (in-)validate hypotheses on the factors influencing cell growth and metabolism. We apply the approach to a mammalian suspension cell line (AGE1.HN). We show that growth in batch culture can be divided into two main growth phases. The initial phase is characterized by exponential growth dynamics, which can be described consistently by a relatively simple unstructured and segregated model. The subsequent phase is characterized by a decrease in the specific growth rate, which, as shown, results from substrate limitation and the pH of the medium. An extended model is provided which describes the observed dynamics of cell growth and main metabolites, and the corresponding kinetic parameters as well as their confidence intervals are estimated. The study is complemented by an uncertainty and outlier analysis. Overall, we demonstrate utility of set-based methods for analyzing cell growth and metabolism under conditions of uncertainty.
Highlights
Production of bio-pharmaceuticals in cell culture is frequently described by unstructured and segregated models
Basic properties of cell growth have been described in various publications for hybridoma [42,43], myeloma [44] and CHO cells considering unstructured models, refer [45,46], and metabolic shifts have been investigated for AGE1.HN cells using metabolic flux analysis [23]
We proposed a structured approach for analyzing and characterizing cell growth and metabolism, outlined for AGE1.HN cells cultured in two commonly used environments
Summary
Production of bio-pharmaceuticals in cell culture is frequently described by unstructured and segregated models. A main advantage of these models is that they can be tailored to particular growth phases and process conditions. This is important since cell growth and product formation depend on a variety of factors, e.g. the availability of substrates, inhibitors, or changes in the cultivation conditions (e.g. oxygen, temperature, pH [1,2]). Within a particular experimental setting, only some of these factors contribute to the observed cell dynamics. To obtain a concise model of the process, it is necessary to identify apparent phases and the main influencing factors of cell growth and product formation
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