Planform: an application and database of graph-encoded planarian regenerative experiments by Daniel Lobo, Taylor J. Malone and Michael Levin. Bioinformatics (2013) 29 (8): 1098-1100. doi: 10.1093/bioinformatics/btt088
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 http://planform.daniel-lobo.com.
Watch the video tour for an example of a domain specific authoring tool.
It does not use any formal graph notation/terminology or attempt a new form of ASCII art.
Users can enter data about worms with four (4) heads. That bodes well for new techniques to author topic maps.
On the use of graphs, the authors write:
We have created a formalism based on graphs to encode the resultant morphologies and manipulations of regenerative experiments (Lobo et al., 2013). Mathematical graphs are ideal to encode relationships between individuals and have been previously used to encode morphologies (Lobo et al., 2011). The formalism divided a morphology into adjacent regions (graph nodes) connected to each other (graph edges). The geometrical characteristics of the regions (connection angles, distances, shapes, type, etc.) are stored as node and link labels. Importantly, the formalism permits automatic comparisons between morphologies: we implemented a metric to quantify the difference between two morphologies based on the graph edit distance algorithm.
The experiment manipulations are encoded in a tree structure. Nodes represent specific manipulations (cuts, irradiation and transplantations) where links define the order and relations between manipulations. This approach permits encode the majority of published planarian regenerative experiments.
The graph vs. relational crowd will be disappointed to learn the project uses SQLite (“the most widely deployed SQL database engine in the world”) for the storage/access to its data.
You were aware that hypergraphs were used to model relational databases in the “old days.” Yes?
I will try to pull together some of those publications in the near future.