将时间分辨的 $nrf2$ 基因表达数据整合到完整 GUTS 模型中，作为斑马鱼胚胎毒效动力学损伤的代理指标
Integrating time-resolved $nrf2$ gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo
AI总结：
本文提出具有生物学锚定的 TKTD/GUTS 模型，将时间分辨 $nrf2$ 表达作为毒效动力学损伤代理，结合贝叶斯推断评估其对斑马鱼胚胎致死性的预测价值。
摘要：
化学工业的巨大生产规模要求改进预测性风险评估，使其能够应对不断变化的挑战，同时降低风险评估对动物实验的依赖。将“组学”数据整合到机制模型中，提供了一种有前景的解决方案，可将化学物暴露后触发的细胞过程与在生物体中观察到的效应联系起来。随着时间分辨 RNA 数据的日益可得，将基因表达数据整合到机制模型中的目标已具备实现条件。我们提出了一种具有生物学锚定的 TKTD 模型，该模型通过将毒效动力学损伤与 $nrf2$ 表达相关联，描述了将应激调节因子 $nrf2$ 的基因表达水平与解毒作用和致死性联系起来的关键过程。将此类模型拟合到由多个终点组成的复杂数据集，需要结合分子生物学、机制动力系统建模和贝叶斯推断方法。在本研究中，我们成功地将时间分辨基因表达数据整合到 TKTD 模型中，从而提供了一种评估分子标志物对存活影响的方法。这一新方法被用于检验 $nrf2$ 是否可用于预测斑马鱼胚胎的致死性。借助所提出的方法，我们勾勒出一条逐步实现基于分子数据进行预测性风险评估这一目标的途径。
英文摘要：
The immense production of the chemical industry requires an improved predictive risk assessment that can handle constantly evolving challenges while reducing the dependency of risk assessment on animal testing. Integrating 'omics data into mechanistic models offers a promising solution by linking cellular processes triggered after chemical exposure with observed effects in the organism. With the emerging availability of time-resolved RNA data, the goal of integrating gene expression data into mechanistic models can be approached. We propose a biologically anchored TKTD model, which describes key processes that link the gene expression level of the stress regulator $nrf2$ to detoxification and lethality by associating toxicodynamic damage with $nrf2$ expression. Fitting such a model to complex datasets consisting of multiple endpoints required the combination of methods from molecular biology, mechanistic dynamic systems modeling and Bayesian inference. In this study we successfully integrate time-resolved gene expression data into TKTD models, and thus provide a method for assessing the influence of molecular markers on survival. This novel method was used to test whether, $nrf2$, can be applied to predict lethality in zebrafish embryos. With the presented approach we outline a method to successively approach the goal of a predictive risk assessment based on molecular data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
