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.
Keywords: Formalization; Inference; Modeling; Regeneration.
© 2022. The Author(s).