Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve...

Area of Science:

• Biomedical Engineering
• Computational Biology
• Neuroscience

Background:

• Cellular engineered neural tissues hold promise for peripheral nerve repair.
• Current methods struggle with overarching tissue design frameworks due to isolated experimental data.
• Mathematical cell-solute models offer a computational approach to test mechanisms and design scenarios.

Purpose of the Study:

• To develop a pipeline for deriving experimentally informed cell-solute models for nerve tissue engineering.
• To address uncertainties arising from limited experimental data in model development.
• To enable quantitative comparison of therapeutic cells and improve experimental design.

Main Methods:

• Utilized Bayesian inference to integrate experimental data with mathematical cell-solute models.
• Developed a pipeline for parameter estimation, model selection, and experiment utility quantification.
• Applied the pipeline to three relevant cell types for nerve tissue engineering.

Main Results:

• Successfully derived experimentally informed cell-solute models for therapeutic cell behavior.
• Demonstrated the pipeline's capability to simulate nerve repair scenarios.
• Quantitatively compared the efficacy of different therapeutic cells.

Conclusions:

• The proposed Bayesian pipeline enhances the development of predictive models for nerve tissue engineering.
• This approach accelerates the design and optimization of novel treatment strategies for peripheral nerve repair.
• Improved model formulation and experimental design are key outcomes for advancing the field.