Sensitivity Analysis for Stochastic Networks with a High-Dimensional Parameter Space

• Activity Number: 350
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Weilong Hu*+ and Yannis Pantazis and Markos Katsoulakis and Dionisios Vlachos
• Companies: University of Massachusetts, Amherst and University of Massachusetts, Amherst and University of Massachusetts, Amherst and University of Delaware
• Keywords: Biochemical reaction networks ; sensitivity analysis ; relative entropy rate ; pathwise Fisher information matrix ; p53 model ; EGFR model

Abstract

We develop a sensitivity analysis methodology for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. The sensitivity analysis method is realized by employing the rigorously-derived pathwise Relative Entropy Rate , which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. The knowledge of the structure of the FIM can allow to efficiently address questions on parameter identifiability, estimation and robustness. The proposed method is tested and validated on several biochemical systems.