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Ontologies as the basis for in silico reconstruction of tissue dynamics
Citation:
Knudsen, T. Ontologies as the basis for in silico reconstruction of tissue dynamics. Eurotox 2023, Ljubljana, SLOVENIA, September 10 - 13, 2023. https://doi.org/10.23645/epacomptox.24201369
Impact/Purpose:
This is an invited presentation to the 57th Congress of the European Toxicologists and European Societies of Toxicology (EUROTOX) September 2023 symposiuum entitled "Ontologies as Translational Tools in Toxicology". An ontology is defined as an area of knowledge that is formalised such that the individual terms or concepts can be delineated by a set of assertions that connect them to other terms. In a biological context, ontologies are used as a way to classify terms, how they relate to broader concepts and their interrelationships. As such, ontologies provide a means to deal with knowledge in a structured manner in view of data integration. Ontologies have many applications, including genome annotation, interpretation of omics findings, knowledge integration across species and biological levels of organisation, information retrieval and semantic computing. In the field of toxicology, ontologies can help to (i) predict and explain which chemicals are likely to induce specific types of toxicity, (ii) overcome some of the limitations of current safety testing by exploiting the state-of-the-art of science and the increasing amounts of data that can inform about mechanisms leading to adverse outcomes, (iii) enable the design of more informative and predictive models and assessment strategies for toxicity of chemicals, (iv) improve public health protection through increased relevance and accuracy of testing, (v) facilitate the design of chemicals so that they are unlikely to have the potential for inducing toxicity in humans, and (vi) save resources and animals. This symposium will give an introduction to the basics of ontologies and their use in toxicology with focus on aspects relevant for the areas of in vitro and in silico toxicology.
Description:
Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. While New Approach Methods (NAM) based on in vitro chemical effects data offer alternatives to pregnant animal testing, adverse fetal outcomes in vivo result from complex interactions of chemicals with maternal physiology, cell signaling pathways, and tissue-level damage responses. This poses a challenge to NAMs since most in vitro platforms lack positional information, physical constraints, and regional organization of the mammalian embryo. Embryologically-inspired virtual tissue models engineered to re-establish positional information and self-organize emergent phenotypes can be utilized to integrate toxicodynamics with morphogenetic dynamics. Ontologies play a key role in formal representation of entity selection (eg, molecular targets), critical phenomena (eg, pathway responses), and their hierarchical relationships in mapping chemical bioactivity data to the molecular logic of signaling networks. This presentation will focus on the ontologies needed to build and test computational dynamic models for complex phenomena across multiple scales to iterate the mechanistic value of: (i) stochastic tissue models to unravel multicellular complexity; (ii) probabilistic and quantitative support of deterministic models driven by experimental data; (iii) case studies for model calibration to recapitulate known effects in silico (cybermorphs); and (iv) expanding the interface between toxicokinetics and toxicodynamics as a systems-based kinematic domain for integrating in vitro data and in silico models. Ontology-based computer models that mechanistically drive biomolecular lesions into higher levels of biological organization (eg, virtual tissues) serve as translational tools by putting chemical effects data (eg, ToxCast/Tox21) into motion for probabilistic rendering of critical phenomena in toxicological outcomes. This abstract does not necessarily reflect Agency policy.
URLs/Downloads:
DOI: Ontologies as the basis for in silico reconstruction of tissue dynamics
EUROTOX_2023_KNUDSEN.PDF (PDF, NA pp, 7211.23 KB, about PDF)