Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

Area of Science:

• Computational Biology
• Systems Biology
• Biophysics

Background:

• Stochastic models are crucial for understanding gene regulatory networks.
• Parameter calibration and sensitivity analysis pose significant computational challenges.
• Identifying bifurcations in stochastic models is essential for understanding cellular behavior.

Purpose of the Study:

• To develop a novel computational method for analyzing stochastic models of biochemical networks.
• To address challenges in parameter estimation, robustness, sensitivity, and bifurcation analysis.
• To enable efficient computation of model properties across a parameter space.

Main Methods:

• Development of tensor-structured parametric analysis (TPA).
• Utilizing low-parametric tensor-structured representations of matrices and vectors.
• Simultaneous computation of model properties for all parameter values.

Main Results:

• TPA effectively handles large numbers of parameters in stochastic models.
• The method allows for comprehensive analysis of parameter dependencies and bifurcations.
• Demonstrated application in parameter estimation, robustness, sensitivity, and bifurcation structure analysis.

Conclusions:

• TPA offers a computationally efficient solution for analyzing stochastic gene regulatory network models.
• This approach facilitates a deeper understanding of how molecular noise impacts cellular functions.
• The developed method enhances the study of complex biological systems and their parameter spaces.