The phenotypes emerging under certain growth conditions vary among different organisms, despite sharing common metabolic pathways. These differences occur due to the evolutionary divergence of the genome of each organism and the optimization of these pathways. Thus, understudying the source of these differences can help us decode the observed phenotypes and suggest the most candidate organisms to perform a desired metabolic task, e.g., the design of synthetic microbiomes for therapeutic purposes or degradation of pollutants. Genome-scale metabolic models (GEMs) are biochemical databases that encapsulate all known metabolic capabilities of an organism. They serve as powerful tools to study cellular metabolism and microbial communities, and they have been successfully used in the last two decades in the fields of metabolic engineering and synthetic biology. However, metabolic networks, even for the most simple organisms, contain hundreds of metabolites and reactions interlinked into a network. Due to the size and inherent complexity of metabolic networks, comparison of GEMs by simple inspection can be cumbersome, and in some cases, not possible. In this study, we introduce a workflow to systematically compare the metabolic capabilities of different organisms. Our approach incorporates the in-house published methods redGEM and lumpGEM, which allow the systematic reduction of GEMs to core metabolic models while minimizing the loss of information. Our workflow allows the direct extraction and comparison of the subsystems of interest, the connections among them, and the elementally balanced subnetworks for the biosynthesis of target metabolites. We use our workflow and GEMs generated through the CarveMe pipeline to study and compare the metabolic capabilities of the members of different microbial communities.

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity

ACS Spring 2022

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