Abstract
With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.
Highlights
Cell behaviours are determined by the characteristics of individual biological components and by the interactions of such components acting together as a system
We focus on the sensitivity analysis approaches applied to systems biology models
We have introduced different sensitivity analysis methods that are applied to systems biology models
Summary
Cell behaviours are determined by the characteristics of individual biological components and by the interactions of such components acting together as a system. The development of predictive dynamic models requires the information about the initial conditions and kinetic parameters that characterise the biological systems. Sensitivity analysis is a classic technique to determine how the fluctuations in mathematical model outputs can be apportioned to the variations in the model inputs [2]. One can pinpoint which model inputs contribute most to the variation in model outputs (experimental observations). The most sensitive model input parameters and their corresponding biological processes are the potential targets for further experimental analysis. We limit the discussion of relevant model inputs to the kinetic parameters and the initial conditions of systems biology models. The systems biology models discussed here are in the format of ordinary differential equations (ODEs), the sensitivity analysis approaches are generally applicable to other formats of biological models.
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