Efficient global sensitivity analysis of biochemical networks using Gaussian process regressionKurdyaeva, T. & Milias-Argeitis, A., 18-Jan-2019, 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., p. 2673-2678 6 p. 8618902. (Proceedings of the IEEE Conference on Decision and Control).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
A key objective of systems biology is to understand how the uncertainty in parameter values affects the responses of biochemical networks. Variance-based sensitivity analysis is a powerful approach to address this question. However, commonly used implementations based on (Quasi-) Monte Carlo require a very large number of model evaluations, and are thus impractical for computationally expensive models. Here, we present an alternative method for variance-based sensitivity analysis that uses Gaussian process regression. Thanks to the appealing mathematical properties of Gaussian processes, we are able to derive exact analytic formulas for the required sensitivity indices. In this way our approach yields more accurate estimates with significantly less computational cost compared to conventional methods, as we demonstrate for a nonlinear model of a bacterial signaling system.
|Title of host publication||2018 IEEE Conference on Decision and Control, CDC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 18-Jan-2019|
|Event||57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States|
Duration: 17-Dec-2018 → 19-Dec-2018
|Name||Proceedings of the IEEE Conference on Decision and Control|
|Conference||57th IEEE Conference on Decision and Control, CDC 2018|
|Period||17/12/2018 → 19/12/2018|
57th IEEE Conference on Decision and Control, CDC 2018
17/12/2018 → 19/12/2018Miami, United States