Mofidifar Sepideh, Tefagh Mojtaba
Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 14176-14335, Iran.
Department of Mathematical Sciences, Sharif University of Technology, Tehran, 14588-89694, Iran.
Bioinformatics. 2025 Jul 1;41(7). doi: 10.1093/bioinformatics/btaf328.
Metabolic modeling has emerged as a powerful tool for predicting community functions. However, current modeling approaches face significant challenges in balancing the metabolic trade-offs between individual and community-level growth. In this study, we investigated the effect of metabolic relatedness among taxa on growth rate calculations by merging related taxa based on their metabolic similarity, introducing this approach as PhyloCOBRA.
This approach enhanced the accuracy and efficiency of microbial community simulations by combining genome-scale metabolic models (GEMs) of closely related organisms, aligning with the concepts of niche differentiation and nestedness theory. To validate our approach, we implemented PhyloCOBRA within the MICOM and OptCom package (creating PhyloMICOM and PhyloOptCom, respectively), and applied it to metagenomic data from 186 individuals and four-species synthetic community (SynCom). Our results demonstrated significant improvement in the accuracy and reliability of growth rate predictions compared to the standard methods. Sensitivity analysis revealed that PhyloMICOM models were more robust to random noise, while Jaccard index calculations showed a reduction in redundancy, highlighting the enhanced specificity of the generated community models. Furthermore, PhyloMICOM reduced the computational complexity, addressing a key concern in microbial community simulations. This approach marks a significant advancement in community-scale metabolic modeling, offering a more stable, efficient, and ecologically relevant tool for simulating and understanding the intricate dynamics of microbial ecosystems.
PhyloCOBRA implementations are available as extensions to the MICOM packages and can be accessed at https://github.com/sepideh-mofidifar/PhyloCOBRA.
代谢建模已成为预测群落功能的强大工具。然而，当前的建模方法在平衡个体与群落水平生长之间的代谢权衡方面面临重大挑战。在本研究中，我们通过基于代谢相似性合并相关分类群来研究分类群间代谢相关性对生长速率计算的影响，并将此方法称为系统发育约束性通量平衡分析（PhyloCOBRA）。
该方法通过结合密切相关生物体的基因组规模代谢模型（GEMs），提高了微生物群落模拟的准确性和效率，符合生态位分化和嵌套理论的概念。为了验证我们的方法，我们在MICOM和OptCom软件包中实现了PhyloCOBRA（分别创建了PhyloMICOM和PhyloOptCom），并将其应用于来自186个个体的宏基因组数据和四物种合成群落（SynCom）。我们的结果表明，与标准方法相比，生长速率预测的准确性和可靠性有显著提高。敏感性分析表明，PhyloMICOM模型对随机噪声更具鲁棒性，而杰卡德指数计算显示冗余度降低，突出了所生成群落模型增强的特异性。此外，PhyloMICOM降低了计算复杂性，解决了微生物群落模拟中的一个关键问题。这种方法标志着群落规模代谢建模的重大进展，为模拟和理解微生物生态系统的复杂动态提供了一个更稳定、高效且与生态相关的工具。
PhyloCOBRA的实现可作为MICOM软件包的扩展获取，可在https://github.com/sepideh - mofidifar/PhyloCOBRA上访问。
