Towards a bioinformatics of patterning: a computational approach to understanding regulative morphogenesis

Area of Science:

• Developmental Biology
• Bioinformatics
• Computational Biology

Background:

• Understanding regeneration mechanisms in model organisms like planaria and salamanders is crucial for basic pattern formation knowledge.
• Current research, despite molecular and bioinformatics efforts, lacks comprehensive models for regeneration due to limitations in representing morphology and experimental data.
• Existing knowledge from regeneration experiments is difficult to store, search, and mine, hindering fundamental insights.

Purpose of the Study:

• To introduce a novel ontology for formally encoding diverse morphologies, experimental manipulations, and results in regeneration.
• To develop computational tools for facilitating the formalization and mining of experimental knowledge in regeneration.
• To enable top-down approaches for discovering comprehensive mechanistic models of pattern regulation.

Main Methods:

• Development of a new ontology class for formal representation of morphology and experimental procedures.
• Creation of a software tool for formalizing and mining planarian regeneration experimental data.
• Curation of a database containing experiments from key planarian regeneration publications.

Main Results:

• A proof-of-principle using the planarian regeneration dataset demonstrates the novel bioinformatics of shape.
• A freely available software tool and curated database empower the regeneration community to access and analyze experimental data.
• The framework facilitates the identification of specific functional data and aids in understanding pattern perturbations.

Conclusions:

• The presented ontology and tools provide a robust framework for formalizing knowledge about functional perturbations of morphogenesis.
• This approach is widely applicable to various model systems beyond planaria, including developmental, regenerative, and evolutionary biology.
• The resources pave the way for developing comprehensive, top-down mechanistic models of regeneration.