Stochastic biological system-of-systems modelling for iPSC culture

Area of Science:

• Biotechnology
• Cell Biology
• Bioengineering

Background:

• Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is crucial for advancing cell therapies and regenerative medicine.
• iPSCs aggregate in suspension bioreactors, leading to nutrient deprivation and waste accumulation in core cells.
• Micro-environmental variations can cause significant heterogeneity within iPSC populations.

Purpose of the Study:

• To develop a novel Biological System-of-Systems (Bio-SoS) framework for modeling iPSC aggregation and micro-environmental effects.
• To characterize cell-to-cell interactions, spatial and metabolic heterogeneity, and cellular responses to environmental changes.
• To provide a predictive model for optimizing large-scale iPSC manufacturing processes.

Main Methods:

• Developed a Bio-SoS model integrating stochastic metabolic reaction networks, aggregation kinetics, and reaction-diffusion mechanisms.
• Modeled causal interdependencies at the cellular, aggregate, and population levels.
• Incorporated a modular design for data integration across different culture systems (monolayer and aggregate).

Main Results:

• The Bio-SoS model effectively characterizes cell-to-cell interactions and heterogeneity within iPSC aggregates.
• The framework quantifies the impact of factors like aggregate size on cell health and product quality.
• Demonstrated improved predictions for both monolayer and aggregate iPSC culture processes.

Conclusions:

• The proposed Bio-SoS framework offers a robust approach to understanding and mitigating heterogeneity in large-scale iPSC manufacturing.
• This modeling strategy can guide the optimization of bioreactor conditions to enhance cell product quality for therapeutic applications.
• The variance decomposition analysis provides critical insights into factors influencing cell quality heterogeneity.