  Morphogenesis is a developmental process in which cells organize into shapes
and patterns. Complex, multi-factorial models are commonly used to study
morphogenesis. It is difficult to understand the relation between the
uncertainty in the input and the output of such `black-box' models, giving rise
to the need for sensitivity analysis tools. In this paper, we introduce a
workflow for a global sensitivity analysis approach to study the impact of
single parameters and the interactions between them on the output of
morphogenesis models. To demonstrate the workflow, we used a published,
well-studied model of vascular morphogenesis. The parameters of the model
represent cell properties and behaviors that drive the mechanisms of angiogenic
sprouting. The global sensitivity analysis correctly identified the dominant
parameters in the model, consistent with previous studies. Additionally, the
analysis provides information on the relative impact of single parameters and
of interactions between them. The uncertainty in the output of the model was
largely caused by single parameters. The parameter interactions, although of
low impact, provided new insights in the mechanisms of \emph{in silico}
sprouting. Finally, the analysis indicated that the model could be reduced by
one parameter. We propose global sensitivity analysis as an alternative
approach to study and validate the mechanisms of morphogenesis. Comparison of
the ranking of the impact of the model parameters to knowledge derived from
experimental data and validation of manipulation experiments can help to
falsify models and to find the operand mechanisms in morphogenesis. The
workflow is applicable to all `black-box' models, including high-throughput
\emph{in vitro} models in which an output measure is affected by a set of
experimental perturbations.
