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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.
Abstract:
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