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Published June 29, 2023 | Submitted + Supplemental Material
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Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans

  • 1. ROR icon California Institute of Technology


In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans (C. elegans Neural Circuit Causal Activity Modelling (CeN-CAM)). We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also generated representations of multisensory integration and sensory adaptation, and we discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. We thank all members of the Sternberg lab at Caltech for their feedback during the course of the project. We also thank Raymond Lee (WormBase) and members of the Gene Ontology Consortium, as well as Susan Bello (Mouse Genome Informatics) and members of the Unified Phenotype Ontology working group for helpful discussions. K.V.A. & D.P.H. are funded by the National Human Genome Research Institute (U24HG012212). S.J.P. is funded by NIH U24HG010859-03S2. Data & Materials Availability. All data generated or analysed during this study are included in this published article (and its supplementary information files). The authors declare that they have no competing interests.

Attached Files

Submitted - nihpp-2023.04.28.538760v1.pdf

Supplemental Material - media-1.docx

Supplemental Material - media-2.xlsx

Supplemental Material - media-3.xlsx

Supplemental Material - media-4.docx


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Additional details

August 20, 2023
December 22, 2023