Functional Alignment of Metabolic Networks

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

doi:10.1089/cmb.2015.0203

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Functional alignment is challenging because even phylogenetically closely-related organisms can have very different metabolic repertoires 24 , and structurally similar network parts (e.g., parallel pathways) could have too similar functions to be resolved unambiguously by sensitivity correlations. A previous method for functional network alignment 25 reported 85% correct alignments for 100% common reactions when aligning the yeast GSM iMM904 26 with itself. In contrast, more than 92% of the metabolic reactions were correctly aligned even when using only 1% of the reactions to compute sensitivity correlations (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Green: average ± s.d. ; red: method by Mazza et al 25 b Average sensitivity correlations (Pearson, green) and Jaccard indices (pink) for model pairs as a function of divergence time. Red circles: comparisons between a subset of the Gram-positive bacteria ( B. subtilis and Mycobacterium tuberculosis) and the Gram-negative bacteria; blue circles: comparisons between yeast and bacteria.…”

Section: Resultsmentioning

confidence: 99%

“…Compared to ref. 25 , our method is faster (30 min versus 48 h per alignment) because it requires only one optimization per reaction. It returns a set of mapped reactions with their individual sensitivity distances, and a set of unmapped reactions in the larger GSM.…”

Section: Methodsmentioning

confidence: 99%

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Functional comparison of metabolic networks across species

Ramon

Stelling

2023

Nat Commun

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Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Functional comparison of metabolic networks across species

Ramon

Stelling

2023

Nat Commun

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“…As a result of this contraction, the metabolic network is converted into a simple structure, a metabolic DAG to easily visualise the network connectivity. As far as the comparison of metabolic networks or pathways goes, most tools compare two metabolic pathways by means of their networks alignment [28][29][30][31], while others define similarity measures based on their reactions similarity or the topological properties of the networks [32][33][34]. However, they do not provide a graphical visualisation of the obtained results on metabolic networks comparison.…”

Section: Introductionmentioning

confidence: 99%

MetNet: A two-level approach to reconstructing and comparing metabolic networks

et al. 2021

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Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.

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