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Published November 10, 2023 | Published
Journal Article Open

Semantic representation of neural circuit knowledge in Caenorhabditis elegans

  • 1. ROR icon California Institute of Technology


AbstractIn 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 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.

Copyright and License

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.


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.


KV.A. and D.P.H. are funded by the National Human Genome Research Institute (U24HG012212). S.J.P. and P.W.S. are funded by NIH U24HG010859-03S2.


PWS and SJP conceived and designed the study, SJP, KVA and DPH conducted the study. SJP, KVA, DPH and PWS wrote and revised the manuscript. SJP prepared all the figures. All authors approved the manuscript.

Data Availability

All data generated or analysed during this study are included in this published article (and its Additional file information files).

Conflict of Interest

The authors declare that they have no competing interests.


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Additional details

November 16, 2023
January 9, 2024