The paper explores the application of loop eigenvalue elasticity analysis (LEEA) to three models in order
to reveal the potential of the method for generating insights about model behavior and to uncover
issues in developing the method further. The results indicate that the utility of the method depends
upon the character of the model and dynamics involved. In models where the transient behavior is of
interest, the method yields insights on par with the pathway participation method, though better tools
to link the method to time paths of particular variables is needed. In models involving near-equilibrium
oscillation, LEEA is clearly the most powerful, though more efficient computer programs are needed to
handle large-scale models. In highly non-linear models exhibiting deterministic chaos, LEEA, being
based upon linear concepts, does not appear to yield any insight because the eigenvalues may change
substantially even when the mode of behavior appears constant. The paper also describes the set of
tools and processes that we have developed and the design for a web-based toolbox to make the
methods readily available to a wider audience in the hope that others will join the efforts to develop
analytical methods for interpreting model behavior.