ICON-GEMs:将共同表达网络集成到基因组规模的代谢模型中,通过系统生物学来阐明
Thummarat Paklao1, Apichat Suratanee2, Kitiporn Plaimas3,4
1Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
BMC bioinformatics
|December 22, 2023
概括
这项研究介绍了ICON-GEMs,一种新的基于约束的模型,将基因协同表达网络集成到流量平衡分析 (FBA) 中,以更准确地预测代谢流量. 在代谢建模中,ICON-GEMs通过利用基因协同表达数据来提高预测准确度.
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 计算生物学
背景情况:
- 流平衡分析 (FBA) 是在稳定状态条件下模拟细胞代谢的核心方法.
- 现有的FBA策略整合了转录基因和蛋白质基因数据,以预测流量分布和表型.
- 基因共同表达模式为更深入的代谢洞察提供了尚未开发的潜力.
研究的目的:
- 开发一种基于约束的创新模型,ICON-GEMs,将基因共同表达网络集成到FBA中.
- 为了提高流量分布的精度和功能路径的确定.
- 用基因共同表达数据提高代谢建模中的预测准确度.
主要方法:
- 从*Escherichia coli*和*Saccharomyces cerevisiae*的综合转录基因组数据转化为基因组规模的代谢模型.
- 构建了一个全面的基因共同表达网络来代表细胞代谢机制.
- 利用二次编程来调整反应流与共同表达网络中的基因相关性.
主要成果:
- 与现有方法相比,ICON-GEMs的预测准确度更高.
- 分析了各子系统和功能模块的流量变化,显示出有希望的结果.
- 将共同表达网络与蛋白质与蛋白质相互作用和随机网络进行比较,强调共同表达数据的实用性.
结论:
- ICON-GEMs提供了一个创新的受约束模型,用于整合基因共同表达网络.
- 该模型适用于各种转录基因数据集和多个生物体.
- 作为开源软件,ICON-GEMs可用于广泛的应用.
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