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Mechanistic Modeling of Developmental Defects through Computational Embryology
Citation:
Knudsen, T. Mechanistic Modeling of Developmental Defects through Computational Embryology. Presented at Society of Toxicology, San Antonio, TX, March 11 - 15, 2018.
Impact/Purpose:
Here, we demonstrate practical application of in silico agent-based models (ABMs) in an integrative computational platform for mechanistic modeling of developmental toxicity. ABMs offer a heuristic approach to reconstruct tissue dynamics from the bottom-up, cell-by-cell and interaction-by-interaction to return probabilistic predictions of adverse outcomes (eg, cybermorphs) based upon emergent properties of the system.
Description:
Significant advances in the genome sciences, in automated high-throughput screening (HTS), and in alternative methods for testing enable rapid profiling of chemical libraries for quantitative effects on diverse cellular activities. While a surfeit of HTS data and information is now available for thousands of chemicals in the ToxCast/Tox21 databases, integrative computational models are needed to translate these data into predictions of toxicity. Here, we demonstrate practical application of in silico agent-based models (ABMs) in an integrative computational platform for mechanistic modeling of developmental toxicity. ABMs offer a heuristic approach to reconstruct tissue dynamics from the bottom-up, cell-by-cell and interaction-by-interaction to return probabilistic predictions of adverse outcomes (eg, cybermorphs) based upon emergent properties of the system. Two HTS-ABM use-cases will be demonstrated for specific ToxCast compounds in a virtual embryo. The first will utilize an ABM to forward-engineer a ToxCast biomolecular lesion(s) through simulated anatomical development to yield a probabilistic prediction of altered phenotype following a progression of key events. In the second case example, the prediction scenario will utilize an ABM to reverse-engineer an altered phenotype led by the regression of key events to a biomolecular lesion. Both prediction scenarios are amenable to integrating HTS data with diverse physiological stressors, including genetic variation and mixtures. An integrative HTS-ABM platform provides a synthetic means to predict what, when and how biomolecular lesion(s) from alternative testing methods interact with the underlying biological regulation of anatomical development for use in chemical safety assessments. This abstract does not reflect US EPA policy.
URLs/Downloads:
KNUDSEN_CE_ABSTRACT_2018.PDF (PDF, NA pp, 109.369 KB, about PDF)KNUDSEN_CE_SOT_2018_FINAL_BOOK_M.PDF (PDF, NA pp, 2844.019 KB, about PDF)