Spectral expansion methods for prediction uncertainty quantification in systems biology
Anna Deneer1, Jaap Molenaar1, Christian Fleck2
1Mathematical and Statistical Methods Group, Wageningen University and Research, Wageningen, Netherlands.
Frontiers in Systems Biology
|August 14, 2025
Summary
This study introduces a novel spectral expansion (SE) method to efficiently quantify prediction uncertainty in complex biological models. The new scheme significantly reduces computational costs by minimizing model evaluations, crucial for stochastic systems.
Area of Science:
- Systems Biology
- Computational Mathematics
- Stochastic Systems
Background:
- Biological systems exhibit inherent uncertainty due to factors like gene expression noise.
- Predictive models for stochastic systems must account for parameter variability.
- Quantifying how parameter uncertainties impact model predictions is vital for applications like experimental design.
Purpose of the Study:
- To develop an efficient method for calculating spectral expansion (SE) coefficients.
- To reduce the computational cost associated with quantifying prediction uncertainty in complex models.
- To enable accurate uncertainty quantification even in challenging scenarios like high computational costs, bifurcations, and discontinuities.
Main Methods:
- Developed an innovative scheme for calculating spectral expansion coefficients.
- Ensured a restricted number of model evaluations are required.
- Applied the scheme to diverse examples, including complex models with slow convergence.
Main Results:
- The proposed SE scheme significantly reduces the number of model evaluations needed.
- This leads to substantial computational advantages, especially for high-complexity models.
- The method effectively handles challenging situations, demonstrating its power and efficiency.
Conclusions:
- The novel SE scheme provides an efficient and powerful approach for uncertainty quantification in systems biology.
- It overcomes the limitations of traditional methods like Monte Carlo for computationally expensive models.
- This advancement facilitates more reliable model-based decision-making in biological research.
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