Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
Methods Mol Biol. 2022;2450:663-679. doi: 10.1007/978-1-0716-2172-1_36.
Regeneration experiments can produce complex phenotypes including morphological outcomes and gene expression patterns that are crucial for the understanding of the mechanisms of regeneration. However, due to their inherent complexity, variability between individuals, and heterogeneous data spreading across the literature, extracting mechanistic knowledge from them is a current challenge. Toward this goal, here we present protocols to unambiguously formalize the phenotypes of regeneration and their experimental procedures using precise mathematical morphological descriptions and standardized gene expression patterns. We illustrate the application of the methodology with step-by-step protocols for planaria and limb regeneration phenotypes. The curated datasets with these methods are not only helpful for human scientists, but they represent a key formalized resource that can be easily integrated into downstream reverse engineering methodologies for the automatic extraction of mechanistic knowledge. This approach can pave the way for discovering comprehensive systems-level models of regeneration.
再生实验可以产生复杂的表型，包括形态学结果和基因表达模式，这些对于理解再生机制至关重要。然而，由于其固有的复杂性、个体之间的可变性以及文献中分散的异质数据，从这些实验中提取机制知识是当前的一个挑战。为此，我们在这里提出了使用精确的数学形态描述和标准化的基因表达模式来明确形式化再生表型及其实验程序的方案。我们使用扁形动物和肢体再生表型的分步协议说明了该方法的应用。使用这些方法生成的经过整理的数据集不仅对人类科学家有帮助，而且还代表了一种重要的形式化资源，可以轻松集成到下游的反向工程方法中，以自动提取机制知识。这种方法可以为发现全面的再生系统级模型铺平道路。
