A computational dynamic systems model for in silico prediction of neural tube closure defects

TL;DR

This paper introduces a computational model that simulates neural tube closure and predicts defects like spina bifida without using animals.

Contribution

A novel dynamic systems agent-based model for simulating and predicting neural tube defects using cell signaling and biomechanics.

Findings

The model predicts the nature and probability of neural tube defects from genetic perturbations.

Model predictions align with biological phenotypes observed in existing research.

The model offers a promising tool for animal-free developmental toxicity assessment.

Abstract

•Developed a dynamic systems agent-based model of neural tube closure.•Simulates mammalian neural tube closure using cell signaling and biomechanics.•Predicts both the nature and probability of defects from genetic perturbations.•Model predictions align with biological phenotypes observed in existing research.•Promising tool for animal-free, probabilistic assessment of developmental toxicity.

Developed a dynamic systems agent-based model of neural tube closure.

Simulates mammalian neural tube closure using cell signaling and biomechanics.

Predicts both the nature and probability of defects from genetic perturbations.

Model predictions align with biological phenotypes observed in existing research.

Promising tool for animal-free, probabilistic assessment of developmental toxicity.

Neural tube closure is a critical morphogenetic event during early vertebrate development. This complex process is susceptible to perturbation by genetic errors and chemical disruption, which can induce severe neural tube defects (NTDs) such as spina bifida. We built a computational agent-based model (ABM) of neural tube development based on the known biology of morphogenetic signals and cellular biomechanics underlying neural fold elevation, bending and fusion. The computer model functionalizes cell signals and responses to render a dynamic representation of neural tube closure. Perturbations in the control network can then be introduced synthetically or from biological data to yield quantitative simulation and probabilistic prediction of NTDs by incidence and degree of defect. Translational applications of the model include mechanistic understanding of how singular or combinatorial alterations in gene-environmental interactions and animal-free assessment of developmental toxicity for an important human birth defect (spina bifida) and potentially other neurological problems linked to development of the brain and spinal cord.

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