  Dynamic models of biochemical networks typically consist of sets of
non-linear ordinary differential equations involving states (concentrations or
amounts of the components of the network) and parameters describing the
reaction kinetics. Unfortunately, in most cases the parameters are completely
unknown or only rough estimates of their values are available. Therefore, their
values must be estimated from time-series experimental data.
  In recent years, it has been suggested that dynamic systems biology models
are universally sloppy so their parameters cannot be uniquely estimated. In
this work, we re-examine this concept, establishing links with the notions of
identifiability and experimental design. Further, considering a set of
examples, we address the following fundamental questions: i) is sloppiness
inherent to model structure?; ii) is sloppiness influenced by experimental data
or noise?; iii) does sloppiness mean that parameters cannot be identified?, and
iv) can sloppiness be modified by experimental design?
  Our results indicate that sloppiness is not equivalent to lack of structural
or practical identifiability (although they can be related), so sloppy models
can be identifiable. Therefore, drawing conclusions about the possibility of
estimating unique parameter values by sloppiness analysis can be misleading.
Checking structural and practical identifiability analyses is a better approach
to asses the uniqueness and confidence in parameter estimation.
