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Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.

 

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross‐validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large‐scale manufacturing of regenerative medicines and cell therapies.

 

The use of machine learning and deep learning has become prominent within various fields of bioprocessing for countless modeling and prediction tasks. Previous reviews have emphasized machine learning applications in various fields of bioprocessing, including biomanufacturing. This comprehensive review highlights many of the different machine learning and multivariate analysis techniques that have been utilized within Chinese hamster ovary cell biomanufacturing, specifically due to their rising significance in the industry. Applications of machine and deep learning within other bioprocessing industries are also briefly discussed. 

Abstract Chinese hamster ovary (CHO) cell lines are widely used to manufacture biopharmaceuticals. However, CHO cells are not an optimal expression host due to the intrinsic plasticity of the CHO genome. Genome plasticity can lead to chromosomal rearrangements, transgene exclusion, and phenotypic drift. A poorly understood genomic element of CHO cell line instability is extrachromosomal circular DNA (eccDNA) in gene expression and regulation. EccDNA can facilitate ultra-high gene expression and are found within many eukaryotes including humans, yeast, and plants. EccDNA confers genetic heterogeneity, providing selective advantages to individual cells in response to dynamic environments. In CHO cell cultures, maintaining genetic homogeneity is critical to ensuring consistent productivity and product quality. Understanding eccDNA structure, function, and microevolutionary dynamics under various culture conditions could reveal potential engineering targets for cell line optimization. In this study, eccDNA sequences were investigated at the beginning and end of two-week fed-batch cultures in an ambr ® 250 bioreactor under control and lactate-stressed conditions. This work characterized structure and function of eccDNA in a CHO-K1 clone. Gene annotation identified 1551 unique eccDNA genes including cancer driver genes and genes involved in protein production. Furthermore, RNA-seq data is integrated to identify transcriptionally active eccDNA genes. 

The accumulation of metabolic wastes in cell cultures can diminish product quality, reduce productivity, and trigger apoptosis. The limitation or removal of unintended waste products from Chinese hamster ovary (CHO) cell cultures has been attempted through multiple process and genetic engineering avenues with varied levels of success. One study demonstrated a simple method to reduce lactate and ammonia production in CHO cells with adaptation to extracellular lactate; however, the mechanism behind adaptation was not certain. To address this profound gap, this study characterizes the phenotype of a recombinant CHO K-1 cell line that was gradually adapted to moderate and high levels of extracellular lactate and examines the genomic content and role of extrachromosomal circular DNA (eccDNA) and gene expression on the adaptation process. More than 500 genes were observed on eccDNAs. Notably, more than 1000 genes were observed to be differentially expressed at different levels of lactate adaptation, while only 137 genes were found to be differentially expressed between unadapted cells and cells adapted to grow in high levels of lactate; this suggests stochastic switching as a potential stress adaptation mechanism in CHO cells. Further, these data suggest alanine biosynthesis as a potential stress-mitigation mechanism for excess lactate in CHO cells.