A global sensitivity analysis approach for morphogenesis models

TL;DR

This paper introduces a global sensitivity analysis workflow for morphogenesis models, enabling understanding of parameter impacts and interactions, validated on a vascular morphogenesis model, aiding model validation and reduction.

Contribution

The paper presents a novel workflow for global sensitivity analysis applicable to black-box morphogenesis models, including high-throughput experimental models.

Findings

Identified key parameters influencing vascular morphogenesis.

Revealed low-impact but insightful parameter interactions.

Suggested model simplification by removing one parameter.

Abstract

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.