Optimizing neotissue growth inside perfusion bioreactors with respect to culture and labor cost: a multi-objective...

Area of Science:

• Tissue engineering
• Biotechnology
• Computational modeling

Background:

• Tissue engineering aims to repair or replace damaged tissues and organs.
• Computer models are crucial for optimizing production processes in tissue engineering.
• Neotissue growth within 3D scaffolds in bioreactors is a key area of study.

Purpose of the Study:

• To develop a multi-objective optimization strategy for bioreactor cultures.
• To identify the most cost-effective medium refreshment strategy.
• To maximize neotissue growth while minimizing experimental costs.

Main Methods:

• Utilized a computationally efficient model of neotissue growth in a perfusion bioreactor.
• Applied a multi-objective optimization approach.
• Employed four evolutionary optimization algorithms (NSGAII, MOPSO, MOEA/D, GDEIII) to compute the Pareto frontier.

Main Results:

• All tested optimization algorithms yielded similar outcomes regarding the Pareto frontier.
• Convergence speeds varied among the different algorithms.
• Simulation results indicated that minimizing refreshment frequency and maximizing refreshment volume is most cost-effective.

Conclusions:

• The study identified an optimal medium refreshment strategy for bioreactor cultures.
• Cost-effectiveness is achieved by adjusting refreshment frequency and volume, considering labor costs.
• Computational modeling and optimization are valuable tools for advancing tissue engineering production.