Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach

TL;DR

This paper introduces PhyloCOBRA, a new method that improves microbial community metabolic modeling by merging related organisms, enhancing accuracy and efficiency.

Contribution

PhyloCOBRA merges related taxa based on metabolic similarity, improving community metabolic modeling accuracy and reducing redundancy.

Findings

PhyloCOBRA improved growth rate prediction accuracy compared to standard methods.

PhyloMICOM models showed greater robustness to random noise and reduced redundancy.

The approach reduced computational complexity in microbial community simulations.

Abstract

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.

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