Boas Sonja E M, Navarro Jimenez Maria I, Merks Roeland M H, Blom Joke G
Life Sciences, CWI, Science Park 123, Amsterdam, 1098XG, The Netherlands.
Mathematical Institute, University of Leiden, Niels Bohrweg 1, Leiden, 2333CA, The Netherlands.
BMC Syst Biol. 2015 Nov 21;9:85. doi: 10.1186/s12918-015-0222-7.
Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output 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 this cellular Potts model (CPM) 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 provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of 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 the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from 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 in vitro models in which output measures are affected by a set of experimental perturbations.
形态发生是一个细胞组织成特定形状和模式的发育过程。通常使用以图像为输出的复杂、非线性和多因素模型来研究形态发生。理解此类“黑箱”模型输入与输出之间的不确定性关系很困难,因此需要敏感性分析工具。在本文中,我们介绍了一种全局敏感性分析方法的工作流程,以研究单个参数及其之间的相互作用对形态发生模型输出的影响。
为了演示该工作流程,我们使用了一个已发表且经过充分研究的血管形态发生模型。这个细胞Potts模型(CPM)的参数代表驱动血管生成芽生机制的细胞特性和行为。全局敏感性分析正确识别了模型中的主要参数,与先前的研究一致。此外,该分析提供了关于单个参数及其相互作用的相对影响的信息。这非常重要,因为参数之间的相互作用阻碍了对单个参数预测效果的实验验证。参数之间的相互作用虽然影响较小,但也为计算机模拟芽生机制提供了新的见解。最后,分析表明该模型可以减少一个参数。
我们提出全局敏感性分析作为研究形态发生机制的一种替代方法。将模型参数影响的排名与从实验数据和操纵实验中获得的知识进行比较,有助于对模型进行证伪,并找到形态发生中的操作机制。该工作流程适用于所有“黑箱”模型,包括高通量体外模型,其中输出测量受一组实验扰动的影响。