Identifying the sensitive parameter(s) in a system of equations is not so much difficult as not often considered during the problem solving process.

As the term implies, the degree to which a parameter is sensitive will be visible by the effect it’s values have on the simulation. It is analogous to the effect of a single observation on an overfit model in statistics.

However, unlike statistics, the sensitive parameter may not be the most *important* parameter. Statistical models tend to give an estimate of the relative importance of a factor (at least linear/nonlinear models). However, the model may be sensitive to the synthesis or degradation rate of a protein, but clearly this is a small part of the conceptual system. So we should not assume sensitivity means importance.

Sensitivity of a parameter is important when estimating parameter values. If a parameter estimation greatly changes the simulation, an algorithm may have great difficulty optimizing the entire parameter set. I will discuss a solution to this problem in a post on iterative curve fitting.

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