Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multiscale models offer a valuable approach. Owing to the typically nonlinear and stochastic nature of multiscale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multiscale and multicompartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multiscale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.
Bibliographical noteFunding Information:
This research was supported by the following NIH grants awarded to JL and DK: R01 AI123093 and U01 HL131072 .
© 2019 Elsevier Inc.
- Multiscale modeling
- Sensitivity analysis
- Uncertainty quantification