Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

Area of Science:

• Computational systems biology
• Mathematical modeling
• Network analysis

Background:

• Parametric uncertainty is crucial for systems biology inference and prediction.
• High-dimensional parameter spaces in complex network models challenge current analysis methods like local approximations and Monte-Carlo sampling.

Purpose of the Study:

• To develop a deterministic computational interpolation scheme for global analysis of system behavior across the entire parameter space.
• To address the limitations of existing methods in handling high-dimensional parameter spaces.

Main Methods:

• Utilizes sparse polynomial approximations and adaptive Smolyak interpolation.
• Identifies significant expansion coefficients adaptively.
• Applies the method to kinetic model equations in computational systems biology.

Main Results:

• Achieves numerical approximations of the parametric solution over the entire parameter space.
• Demonstrates higher convergence rates compared to Monte-Carlo sampling.
• The method is non-intrusive and suitable for massively parallel implementation.

Conclusions:

• The proposed methodology offers a scalable solution for large-scale dynamic network analysis.
• Enables advancements in parameter estimation, uncertainty quantification, and systems design for complex biological models.