Moment fitting for parameter inference in repeatedly and partially observed stochastic biological models

Area of Science:

• Computational Systems Biology
• Biochemical Reaction Networks
• Statistical Modeling

Background:

• Parameter inference for biochemical networks is crucial in computational systems biology.
• Current methods often rely on comparing experimental data (e.g., concentration traces) with model predictions using techniques like maximum likelihood estimation.
• Existing approaches can suffer from parameter sloppiness and uncertainty, limiting accuracy and efficiency.

Purpose of the Study:

• To develop an improved method for inferring reaction rate parameters in biochemical networks.
• To leverage higher-order statistical moments, in addition to the mean, for more robust parameter estimation.
• To enhance the accuracy and efficiency of both deterministic and stochastic parameter inference algorithms.

Main Methods:

• Derivation of closed-form ordinary differential equations for the time evolution of statistical moments based on the chemical master equation.
• Development of cost functions that incorporate residuals of higher-order moments alongside the mean.
• Application of moment fitting for parameter inference in illustrative stochastic biological models.

Main Results:

• Moment fitting, utilizing higher-order moments, creates cost function landscapes with more pronounced curvatures.
• This approach can mitigate parameter sloppiness and reduce uncertainty in parameter estimation.
• Demonstrated potential for improved accuracy and efficiency in parameter inference algorithms.

Conclusions:

• Moment fitting offers a promising strategy to enhance parameter inference in biochemical network modeling.
• Incorporating higher-order moments can lead to more reliable and accurate estimation of reaction rate parameters.
• This method has the potential to significantly advance computational systems biology research.