Organism-specific genome-scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time-series transcriptomics in GSMM simulations. Yeast is an easy-to-manipulate model organism for tumor research. Here, a novel approach (TS-GSMM) was proposed to integrate time-series transcriptomics with GSMMs to narrow down the feasible solution space of all possible flux distributions and attain time-series flux samples. The flux samples were clustered using machine learning techniques, and the clusters' functional analysis was performed using reaction set enrichment analysis. A time series transcriptomics response of Yeast cells to a chemotherapeutic reagent—doxorubicin—was mapped onto a Yeast GSMM. Eleven flux clusters were obtained with our approach, and pathway dynamics were displayed. Induction of fluxes related to bicarbonate formation and transport, ergosterol and spermidine transport, and ATP production were captured. Integrating time-series transcriptomics data with GSMMs is a promising approach to reveal pathway dynamics without any kinetic modeling and detects pathways that cannot be identified through transcriptomics-only analysis. The codes are available at https://github.com/karabekmez/TS-GSMM.

中文翻译：

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通过时间序列基因组规模代谢模型了解酵母对化疗药物的反应


生物体特异性基因组规模代谢模型 （GSMM） 可以揭示细胞内的分子机制，通常用于各种应用，从合成生物学、生物技术和系统生物学到代谢工程。在 GSMM 模拟中纳入时间序列转录组学的研究有限。酵母是一种易于操作的模式生物，用于肿瘤研究。在这里，提出了一种新方法 （TS-GSMM） 将时间序列转录组学与 GSMM 集成，以缩小所有可能的通量分布的可行解空间并获得时间序列通量样本。使用机器学习技术对通量样品进行聚类，并使用反应集富集分析进行聚类的功能分析。将酵母细胞对化疗试剂 - 阿霉素 - 的时间序列转录组学反应映射到酵母 GSMM 上。使用我们的方法获得了 11 个通量簇，并显示了通路动力学。捕获与碳酸氢盐形成和运输、麦角甾醇和亚精胺运输以及 ATP 产生相关的通量的诱导。将时间序列转录组学数据与 GSMM 集成是一种很有前途的方法，可以在没有任何动力学建模的情况下揭示通路动力学，并检测无法通过纯转录组学分析识别的通路。这些代码可在 https://github.com/karabekmez/TS-GSMM 处获得。