MinReact: a systematic approach for identifying minimal metabolic networks

On 5 May, 2020

MOTIVATION:

Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimise the metabolic networks into smaller networks that retain the functionality of the original network.

RESULTS:

Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidates that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrate that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner.

AVAILABILITY:

The MATLAB implementation of the algorithm is available from https://github.com/RamanLab/MinReact.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

Original Paper: 

  • [DOI] G. Sambamoorthy and K. Raman, “MinReact: A Systematic Approach for Identifying Minimal Metabolic Networks,” Bioinformatics (Oxford, England), 2020.
    [bibtex]
    @article{Sambamoorthy2020MinReact,
      title = {{{MinReact}}: A Systematic Approach for Identifying Minimal Metabolic Networks},
      shorttitle = {{{MinReact}}},
      author = {Sambamoorthy, Gayathri and Raman, Karthik},
      year = {2020},
      month = may,
      issn = {1367-4811},
      doi = {10.1093/bioinformatics/btaa497},
      abstract = {MOTIVATION: Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimise the metabolic networks into smaller networks that retain the functionality of the original network.
    RESULTS: Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrate that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner.
    AVAILABILITY: Algorithm is available from https://github.com/RamanLab/MinReact.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
      journal = {Bioinformatics (Oxford, England)},
      language = {eng},
      pmid = {32407533}
    }

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