Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and...

Area of Science:

• Regenerative Medicine
• Biotechnology
• Computational Biology

Background:

• Computer models of bioreactor processes are crucial for optimizing conditions in regenerative medicine.
• Existing mechanistic models are computationally intensive, limiting rigorous optimization.
• Neotissue growth is influenced by scaffold geometry, shear stress, and metabolic factors.

Purpose of the Study:

• To develop a computationally efficient model for simulating neotissue growth in a perfusion bioreactor.
• To apply model reduction techniques to accelerate complex simulations.
• To optimize the medium refreshment strategy for maximizing neotissue growth kinetics.

Main Methods:

• Developed a 3D mechanistic model of neotissue growth.
• Applied model reduction from partial differential equations to ordinary differential equations.
• Utilized Bayesian optimization to determine optimal medium refreshment parameters.
• Validated reduced model against mechanistic and experimental data.

Main Results:

• Achieved a 10^5-fold speed-up in simulation time with the reduced model.
• Identified high-frequency, high-percentage medium replacement as optimal for neotissue growth.
• Demonstrated the efficacy of the in silico optimization strategy.

Conclusions:

• Model reduction significantly enhances computational efficiency for bioreactor process optimization.
• The optimized medium refreshment strategy can accelerate neotissue growth.
• This in silico approach provides a powerful tool for designing robust and economical bioprocesses in regenerative medicine.