Development of a multi-scale model to simulate mesenchymal stem cell osteogenic differentiation within hydrogels in a rotating wall bioreactor

Demand for mesenchymal stem cell (MSC)-based therapies for various clinical applications has been steadily increasing. Suitable implants can be obtained by in-vitro osteogenic differentiation of stem cells to meet this demand, at least in part. This paper proposes a multiscale population balance model for the osteogenic differentiation of mesenchymal stem cells encapsulated within alginate-gelatin hydrogels in a rotating wall bioreactor. The mathematical model incorporates key genes and metabolic pathways linked to proliferation and osteogenesis and captures spatial as well as cell cycle heterogeneity within the hydrogels inside the bioreactor. The discretized formulation of the model consists of 12,563 simultaneous ordinary differential equations. Concentrations of key metabolites (e.g., glucose, pyruvate, glutamine, lactate) were compared with experimental data from static well plate cultures, rendering the model predictions as adequate. Gene activation (Runx2 and osteonectin) in alginategelatin-bead-encapsulated cells was slightly delayed compared with well-plate cultures. Global sensitivity analysis revealed that parameters related to gene expression (decay rates and gene transcription activation constants) carry the most significance and should be the focus of future parameter estimation. Hydrogel bead size does not meaningfully impact simulation outcomes for bead diameters in the 2-3 mm range. The mathematical model proposed in this work can function as a foundation for model-based bioreactor and bioprocess optimization when coupled with a high-performance computing system.

Automated summary

The demand for mesenchymal stem cell (MSC)-based therapies is increasing, and this paper proposes a multiscale population balance model for the osteogenic differentiation of MSCs in a rotating wall bioreactor. The model incorporates key genes and metabolic pathways and captures spatial and cell cycle heterogeneity within the hydrogels. The model predictions are adequate and can be used for bioreactor and bioprocess optimization.