Planform: an application and database of graph-encoded planarian regenerative experiments

Abstract

Summary: Understanding the mechanisms governing the regeneration capabilities of many organisms is a fundamental interest in biology and medicine. An ever-increasing number of manipulation and molecular experiments are attempting to discover a comprehensive model for regeneration, with the planarian flatworm being one of the most important model species. Despite much effort, no comprehensive, constructive, mechanistic models exist yet, and it is now clear that computational tools are needed to mine this huge dataset. However, until now, there is no database of regenerative experiments, and the current genotype-phenotype ontologies and databases are based on textual descriptions, which are not understandable by computers. To overcome these difficulties, we present here Planform (Planarian formalization), a manually curated database and software tool for planarian regenerative experiments, based on a mathematical graph formalism. The database contains more than a thousand experiments from the main publications in the planarian literature. The software tool provides the user with a graphical interface to easily interact with and mine the database. The presented system is a valuable resource for the regeneration community and, more importantly, will pave the way for the application of novel artificial intelligence tools to extract knowledge from this dataset. Availability: The database and software tool are freely available at

A linear-encoding model explains the variability of the target morphology in regeneration

“…A linear encoding predicts the capability to experimentally produce precise and lasting morphological alterations during the lifetime of a single organism, as we have discussed for deer antlers, planaria and fiddler crabs. Indeed, planaria are some of the most plastic model organisms in regenerative biology, with more than 250 known experimental phenotypes, as recorded in the planarian phenotype database Planform [151]. By contrast, nonlinear encoding models can account only for a much reduced phenotype landscape (those phenotypes that can be produced with a specific set of rules) [119].…”

Physiological controls of large‐scale patterning in planarian regeneration: a molecular and computational perspective on growth and form

“…A few specific areas for future focus include (1) the continued development of comprehensive databases of planarian results, encompassing functional and physiological data, going beyond protein/gene datasets toward a bioinformatics of shape, and a standardization that will facilitate new machine learning approaches (Lobo et al. ; Brandl et al. ); (2) novel monitoring and functional modification techniques, especially for physiological pathways.…”

“…The method takes as input a dataset of planarian experiments formalized with a specifically designed mathematical ontology (Lobo et al. , ). Crucially, this formalization permits the unambiguous specification of precise surgical manipulations, genetic and pharmacological treatments and, using a mathematical graph representation (Lobo et al.…”

Continuous dynamic modeling of regulated cell adhesion

“…morphogen gradients and CAM expression). Crucially, machine learning approaches for the reverse-engineering of the regulation of patterning (53-55) and cancer formation (56, 57) directly from formalized experimental data (58)(59)(60)(61) can be integrated with the proposed model, with the goal to discover the specific regulatory mechanisms of CAMs that give rise to key spatial phenotypes. In summary, the presented continuous modeling approach will pave the way for the understanding of the regulatory dynamics of cell adhesion essential in developmental, regenerative, and cancer biology.…”