Structural and practical identifiability analysis in bioengineering: a beginner's guide

Area of Science:

• Bioengineering
• Mathematical Modeling
• Computational Biology

Background:

• Mathematical models, often using ordinary differential equations, are vital in many scientific fields.
• Parameter estimation calibrates these models with experimental data, but identifiability is often overlooked.
• Identifiability analysis (structural and practical) is essential for reliable parameter estimates and model validation.

Purpose of the Study:

• To introduce and perform structural and practical identifiability analyses.
• To apply these analyses to established bioengineering models.
• To enhance awareness and usability of identifiability analysis in bioengineering research.

Main Methods:

• Structural identifiability analysis to assess theoretical parameter estimability.
• Practical identifiability analysis to evaluate parameter estimability with specific experimental data.
• Application to well-established bioengineering models.

Main Results:

• Demonstrated the impact of identifiability on parameter estimate reliability.
• Highlighted the role of identifiability analysis in model design and data collection.
• Provided accessible methods for performing these analyses.

Conclusions:

• Identifiability analysis is a critical, yet often neglected, step in mathematical modeling.
• Accessible application of these analyses can improve bioengineering model development and data interpretation.
• This work empowers researchers to better utilize identifiability insights for robust model building.