A Sensitivity Analysis Methodology for Rule-Based Stochastic Chemical Systems

Area of Science:

• Computational Chemistry
• Chemical Kinetics
• Systems Biology

Background:

• Stochastic systems in chemistry are common, but analyzing their parameter sensitivity is challenging.
• Traditional methods may not fully capture the impact of intrinsic noise on system dynamics.
• A robust and flexible sensitivity analysis is needed for complex chemical and biochemical models.

Purpose of the Study:

• To develop a novel sensitivity analysis methodology for stochastic chemical systems.
• To provide an intuitive and computationally efficient approach for identifying sensitive parameters and regions.
• To enable the analysis of diverse, problem-specific output observables.

Main Methods:

• Gradient estimation using finite differences and averaging simulation outcomes.
• Characterization of gradient uncertainty as an angular range.
• Adaptive guidance of simulation numbers based on uncertainty.
• Vector field plots for visualizing local sensitivity and computation of global sensitivity coefficients.
• Application to Michaelis-Menten kinetics and the formose reaction using MØD software.

Main Results:

• The methodology adaptively optimizes computational cost while maintaining robustness.
• Identification of sensitive parameters and distinct regions of sensitivity within the parameter space.
• Demonstrated broad applicability through successful application to diverse chemical systems.
• Intuitive visualization of local sensitivity across parameter space.

Conclusions:

• The proposed sensitivity analysis method offers a robust, efficient, and flexible framework for stochastic chemical systems.
• It enhances understanding of parameter importance and system behavior across different regions of the parameter space.
• The approach is broadly applicable to various chemical and biochemical modeling scenarios, facilitating deeper insights into complex reaction dynamics.