Efficient parameter sensitivity computation for spatially extended reaction networks.

Area of Science:

• Computational Chemistry
• Chemical Kinetics
• Mathematical Modeling

Background:

• Reaction-diffusion models are essential for studying spatially extended chemical systems.
• Understanding parameter influence on model dynamics requires efficient parametric sensitivity computation.
• Stochastic models of spatially extended chemical reactions often involve domain partitioning into voxels.

Purpose of the Study:

• To develop efficient methods for computing parametric sensitivities in stochastic reaction-diffusion models.
• To adapt existing finite difference schemes for robust sensitivity estimation in spatially extended networks.
• To introduce a hybrid technique for dynamically selecting optimal simulation methods.

Main Methods:

• Adaptation of finite difference schemes to exploit dynamics of spatially extended reaction networks.
• Utilizing variance reduction techniques to decrease computational expense of Monte Carlo simulations.
• Development of a hybrid method that dynamically selects simulation strategies based on network characteristics.

Main Results:

• Successfully adapted finite difference schemes for robust parametric sensitivity estimation.
• Demonstrated that algorithmic performance is dependent on network dynamics and summary statistics.
• Validated the hybrid technique's functionality and accuracy across various scenarios.

Conclusions:

• The developed methods offer efficient computation of parametric sensitivities for stochastic reaction-diffusion models.
• Exploiting network dynamics and employing a hybrid simulation approach enhances computational efficiency.
• The findings contribute to a better understanding of parameter impacts in complex chemical systems.