Genome-scale metabolic networks in time and space

Øyås, Ove; Stelling, Jörg

doi:10.1016/j.coisb.2017.12.003

Cited by 31 publications

(24 citation statements)

References 62 publications

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“…Constraint-based models (CBM) have enormous potential in enhancing the understanding of reconstructed/predicted metabolic networks and predictive computational models by integrating biological evidence [42][43][44]. Omics studies can address important questions, as biosynthetic pathways, a selection of plants of interest, the environmental influence on the gene expression and the metabolome profile, and many further questions.…”

Section: Discussionmentioning

confidence: 99%

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

et al. 2019

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Plants produce a diverse portfolio of sesquiterpenes that are important in their response to herbivores and the interaction with other plants. Their biosynthesis from farnesyl diphosphate depends on the sesquiterpene synthases that admit different cyclizations and rearrangements to yield a blend of sesquiterpenes. Here, we investigate to what extent sesquiterpene biosynthesis metabolic pathways can be reconstructed just from the knowledge of the final product and the reaction mechanisms catalyzed by sesquiterpene synthases. We use the software package MedØlDatschgerl (MØD) to generate chemical networks and to elucidate pathways contained in them. As examples, we successfully consider the reachability of the important plant sesquiterpenes β -caryophyllene, α -humulene, and β -farnesene. We also introduce a graph database to integrate the simulation results with experimental biological evidence for the selected predicted sesquiterpenes biosynthesis.

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Section: Discussionmentioning

confidence: 99%

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

et al. 2019

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“…By exploring the possible chemical mechanisms of these reactions, it is reasonable to establish connections with experimental results to draw from this universe of possibilities, those that can occur naturally or synthetically under certain circumstances. Constraint-based models (CBM) have enormous potential to enhance the understanding of reconstructed/predicted metabolic networks and predictive computational models by integrating biological evidence [47], [48], [49]. Studies combining genome, transcriptome, and metabolome data can address important questions, as biosynthetic pathways, selection of plants of interest, the environment influence in the gene expression and the metabolome profile, and many further questions.…”

Section: Discussionmentioning

confidence: 99%

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

Silva¹,

Andersen

Holanda³

et al. 2019

Preprint

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Plants produce a diverse portfolio of sesquiterpenes that are important in their response to herbivores and the interaction with other plants. Their biosynthesis from farnesyl diphosphate depends on the sesquiterpene synthases. Here, we investigate to what extent metabolic pathways can be reconstructed just from knowledge of the final product and the reaction mechanisms catalyzed by sesquiterpene synthases. We use the software package MedØlDatschgerl (MØD) to generate chemical networks and elucidate pathways contained in them. As examples, we successfully consider the reachability of the important plant sesquiterpenes β-caryophyllene, α-humulene, and β-farnesene. We also introduce a graph database to integrate simulation results with experimental biological evidence for selected predicted sesquiterpenes biosynthesis.

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“…Large collections of curated models 1,2 and tools for automated model generation 3 make CBMs available for a wide range of organisms. Their scope of application is also expanding from unicellular to multicellular systems such as microbial communities or human tissues, increasing the need for analysis methods that scale to large metabolic networks 4 .…”

Section: Introductionmentioning

confidence: 99%

Scalable metabolic pathway analysis

Øyås

Stelling

2020

Preprint

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The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them eciently using iterative minimization and a simple graph representation of MPs. These methods outper-form the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia coli’s central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.

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Genome-scale metabolic networks in time and space

Cited by 31 publications

References 62 publications

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

Scalable metabolic pathway analysis

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