摘 要
随着高通量测序技术的快速发展，生物信息学在解析复杂生物系统中的作用日益凸显，特别是在基因组规模代谢模型（GEM）的构建中发挥了关键支撑作用本研究旨在探讨生物信息学方法如何有效整合多组学数据以优化GEM的构建过程，并提升其预测能力通过结合基因注释、代谢通路分析及网络重建等技术手段，研究开发了一套基于机器学习算法的自动化建模框架该框架能够显著提高代谢反应注释的准确性，并有效减少人工干预通过对多个模式生物的数据验证，结果表明该方法能够在保持高精度的同时大幅缩短模型构建周期此外，研究还引入了动态代谢模拟策略，进一步增强了模型对环境变化的适应性预测能力总体而言，本研究不仅为GEM的高效构建提供了新思路，还为后续的个性化药物设计和合成生物学应用奠定了理论基础其创新点在于首次将深度学习与传统代谢网络分析相结合，实现了从静态到动态、从单一组学到多组学融合的跨越，从而推动了系统生物学领域的技术进步
关键词：基因组规模代谢模型；生物信息学；机器学习；多组学数据整合；动态代谢模拟
Abstract
With the rapid development of high-throughput sequencing technologies, the role of bioinformatics in deciphering complex biological systems has become increasingly prominent, particularly in the construction of genome-scale me tabolic models (GEMs), where it provides critical support. This study aims to explore how bioinformatics approaches can effectively integrate multi-omics data to optimize the GEM construction process and enhance its predictive capabilities. By incorporating techniques such as gene annotation, me tabolic pathway analysis, and network reconstruction, a machine-learning-based automated modeling fr amework was developed. This fr amework significantly improves the accuracy of me tabolic reaction annotations while effectively reducing manual intervention. Validation using data from multiple model organisms demonstrated that this method maintains high precision while substantially shortening the model construction cycle. Furthermore, a dynamic me tabolic simulation strategy was introduced, which further strengthens the model's ability to predict adaptability to environmental changes. Overall, this study not only offers new insights into the efficient construction of GEMs but also lays a theoretical foundation for subsequent applications in personalized drug design and synthetic biology. Its innovation lies in the first integration of deep learning with traditional me tabolic network analysis, achieving a transition from static to dynamic modeling and from single-omics to multi-omics integration, thereby advancing technical progress in the field of systems biology.
Keywords: Genome-Scale me tabolic Model; Bioinformatics; Machine Learning; Multi-Omics Data Integration; Dynamic me tabolic Simulation
目 录
1绪论 1
1.1基因组规模代谢模型的研究背景 1
1.2生物信息学在代谢模型中的意义 1
1.3当前研究现状与挑战 1
1.4本文研究方法概述 2
2数据获取与基因注释 2
2.1基因组数据的收集与处理 2
2.2基因功能注释的方法与工具 3
2.3数据质量评估的重要性 3
2.4生物信息学在数据整合中的作用 4
2.5数据库资源的应用与选择 4
3代谢网络的构建与优化 5
3.1代谢反应的识别与分类 5
3.2反向推导代谢路径的策略 5
3.3网络拓扑结构的分析方法 6
3.4模型优化的技术手段 6
3.5生物信息学算法的贡献 7
4模型验证与应用分析 7
4.1实验数据的模拟与验证 7
4.2通量平衡分析的应用场景 8
4.3动态模型的构建与解析 8
4.4生物信息学在预测中的价值 9
4.5模型改进的方向与思路 9
结论 11
参考文献 12
致 谢 13
