GEMsembler:共识模型组装和基因组规模的代谢模型在工具之间进行结构性比较,提高功能性能
Elena K Matveishina1,2, Bartosz J Bartmanski1, Sara Benito-Vaquerizo1
1Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
mSystems
|September 8, 2025
概括
GEMsembler集成了多个基因组规模的代谢模型 (GEMs),以构建更准确的共识模型. 该工具增强了系统生物学的预测能力,改善了基因本质性和auxotrophy预测.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
- 计算生物学是一种计算生物学.
背景情况:
- 基因组规模的代谢模型 (GEMs) 对于理解细胞代谢和预测对干扰的反应至关重要.
- 自动化的GEM重建工具产生了各种各样的模型,具有不同的特性和预测能力.
- 结合多个GEM可以提高代谢网络的确定性和整体模型性能.
研究的目的:
- 介绍GEMsembler,这是一个用于比较,分析和整合跨工具GEM的Python包.
- 通过结合输入模型的子集来促进共识GEM的构建.
- 提高系统生物学应用中GEM的准确性和生物相关性.
主要方法:
- 开发了GEMsembler,用于在不同的GEM中比较和跟踪功能.
- 从选定的输入模型构建共识模型的实现功能.
- 嵌入式分析工具用于路径识别,增长评估和GPR优化.
- 使用基于协议的策划工作流程.
主要成果:
- 对于*Lactiplantibacillus plantarum*和*Escherichia coli*的GEMsembler精心策划的共识模型,在辅助性和基因基本性预测方面表现优于黄金标准模型.
- 在共识模型中优化基因-蛋白质反应 (GPR) 组合进一步改善了基因本质性预测.
- GEMsembler确定了关键的代谢途径和GPR替代品,有助于解决模型不确定性.
结论:
- GEMsembler可以创建更准确,更有生物学信息的代谢模型.
- 该工具促进了来自不同GEM的信息合成,加速了模型开发.
- 类似的GEM有助于识别知识缺口和优先考虑实验,以推进系统生物学研究.
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