Memote: A community driven effort towards a standardized genome-scale metabolic model test suite

Lieven, Christian; Beber, Moritz Emanuel; Olivier, Brett G.; Bergmann, Frank; Ataman, Meriç; Babaei, Parizad; Bartell, Jennifer A.; Blank, Lars M.; Chauhan, Siddharth M.; Correia, Kevin; Diener, Christian; Dräger, Andreas; Ebert, Birgitta E.; Edirisinghe, Janaka N.; Faria, José P.; Feist, Adam M.; Fengos, Georgios; Fleming, Ronan M. T.; García-Jiménez, Beatriz; Hatzimanikatis, Vassily; Helvoirt, Wout van; Henry, Christopher S.; Hermjakob, Henning; Kaafarani, Ali; Kim, Hyun Uk; King, Zachary A.; Klamt, Steffen; Klipp, Edda; Koehorst, Jasper J.; König, Matthias; Lee, Dong-Yup; Lee, Sang Yup; Lee, Sunjae; Lewis, Nathan E.; Liu, Filipe; Ma, Hongwu; Machado, Daniel; Mahadevan, Radhakrishnan; Maia, Paulo; Mardinoğlu, Adil; Medlock, Gregory L.; Monk, Jonathan M.; Nielsen, Jens; Nielsen, Lars K.; Nogales, Juan; Nookaew, Intawat; Reséndis-Antonio, Osbaldo; Palsson, Bernhard Ø.; Papin, Jason A.; Patil, Kiran R.; Poolman, Mark G.; Price, Nathan D.; Richelle, Anne; Rocha, Isabel; Sánchez, Benjamín J.; Schaap, P.J.; Sheriff, Rahuman S. Malik; Shoaie, Saeed; Sonnenschein, Nikolaus; Teusink, Bas; Vilaça, Paulo; Vik, Jon Olav; Wodke, Judith A. H.; Xavier, Joana C.; Yuan, Qianqian; Zakhartsev, Maksim; Zhang, Cheng

doi:10.1101/350991

Cited by 55 publications

(62 citation statements)

References 62 publications

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“…Users are encouraged to report issues with the 205 existing model and contribute to the continuous development, which will result in updated versions of the model. To track model quality, each time a new model version is released a Memote(Lieven et al, 2018) snapshot report is generated, by running a standardized set of metabolic model tests focusing on e.g. annotations and consistency.…”

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confidence: 99%

Genome-scale model of Rhodotorula toruloides metabolism

Tiukova

Prigent

Nielsen

et al. 2019

Preprint

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10The basidiomycete red yeast Rhodotorula toruloides is a promising platform organism for production of biooils. We present rhto-GEM, the first genome-scale model (GEM) of R. toruloides metabolism, that was largely reconstructed using RAVEN toolbox. The model includes 926 genes, 4930 reactions, and 3374 metabolites, while lipid metabolism is described using the SLIMEr formalism allowing direct integration of lipid class and acyl chain experimental distribution data. The simulation results confirmed 15 that the R. toruloides model provides valid growth predictions on glucose, xylose and glycerol, while prediction of genetic engineering targets to increase production of linolenic acid, triacylglycerols and carotenoids highlighted genes that have previously been successfully used to increase production. This renders rtho-GEM valuable for future studies to improve the production of other oleochemicals of industrial relevance including value-added fatty acids and carotenoids, in addition to facilitate system-20 wide omics-data analysis in R. toruloides. Expanding the portfolio of GEMs for lipid accumulating fungi contributes to both understanding of metabolic mechanisms of the oleaginous phenotype but also uncover particularities of the lipid production machinery in R. toruloides.

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confidence: 99%

Genome-scale model of Rhodotorula toruloides metabolism

Tiukova

Prigent

Nielsen

et al. 2019

Preprint

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“…We conducted a stepwise reconstruction of Sco-GEM, the consensus genome-scale metabolic model of S. coelicolor, while tracking development using Git for version control (Figure 1A, Table S1). Sco-GEM is the most comprehensive and highest quality GEM of this organism (Figure 1B), comprising 1777 genes, 2612 reactions, 2073 metabolites and a memote score of 77%, which is indicative of the overall model quality (Lieven et al, 2018). Sco-GEM features accuracy of 96.5% and 74.5% ( Figure 1C) in predicting correct phenotypes for growth environments and knockout mutants, respectively.…”

Section: Reproducible Reconstruction Of Sco-gemmentioning

confidence: 99%

“…The Sco-GEM model is hosted as an open repository as suggested by memote, a recently developed tool for transparent and collaborative model development (Lieven et al, 2018). The memote tool is incorporated in the repository through Travis CI and tracks the model development on every change of the model.…”

Section: Framework For Further Development Of Sco-gem By the Communitymentioning

confidence: 99%

“…To increase the rate and quality of model reconstruction, in this study two research groups of the Sco-GEM community, responsible for two of the latest model updates (Kumelj et al, 2018;Wang et al, 2018), have joined forces to merge existing GEMs of S. coelicolor into one consensus-model that is publicly hosted on GitHub and can be continuously updated and improved by all members of the community. Hosting the model on GitHub has many advantages: 1) Open access and contribution; 2) Version control; 3) Continuous quality control with memote (Lieven et al, 2018); 4) Continuous development and improvement; 5) New improvements released instantly (no publication lag time); 6) Complete documentation of model reconstruction. Such an approach has historic precedents: model reconstruction as a community effort has been a success for the human GEM (Thiele et al, 2013), baker's yeast (Aung et al, 2013;Dobson et al, 2010;Heavner et al, 2012Heavner et al, , 2013Herrgård et al, 2008;Lu et al, 2019) and Chinese Hamster Ovary cells (Hefzi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production

Sulheim

Kumelj

Dissel

et al. 2019

Preprint

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Streptomyces coelicolor M1152 is a widely used host strain for the heterologous production of novel small molecule natural products, genetically engineered for this purpose through e.g. deletion of four of its native biosynthetic gene clusters (BGCs) for improved precursor supply. Regardless of its potential, a systems understanding of its tight regulatory network and the effects of the significant genomic changes in M1152 is missing. In this study, we compare M1152 to its ancestor M145, thereby connecting observed phenotypic differences to changes on transcription and translation. Measured protein levels are connected to predicted metabolic fluxes, facilitated by an enzyme-constrained genome-scale model (GEM), that by itself is a consensus result of a community effort. This approach connects observed differences in growth rate and glucose consumption to changes in central carbon metabolism, accompanied by differential expression of important regulons. Results suggest that precursors supply is not limiting secondary metabolism, informing that alternative strategies will be beneficial for further development of S. coelicolor for heterologous production of novel compounds.

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“…Indeed, it would seem that if we know the chemical formula for each species in the reaction, then it should be trivial to check mass balance by comparing the total atomic mass of the reactants with the total atomic mass of the products. This approach is used by the memote system (Lieven et al [2018]). Knowledge of formulas for chemical species is provided through model meta data, information about the elements of the biochemical model.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Isolating Mass Balance Errors in Reaction Based Models in Systems Biology

Shin

Hellerstein

2019

Preprint

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Motivation:The growing complexity of reaction-based models necessitates early detection and resolution of model errors. This paper addresses mass balance errors, discrepancies between the mass of reactants and products in reaction specifications. One approach to detection is atomic mass analysis, which uses meta-data to expose atomic formulas of chemical species. Atomic mass analysis isolates errors to individual reactions. However, this approach burdens modelers with expressing model meta-data and writing excessively detailed reactions that include implicit chemical species (e.g., water). Moreover, atomic mass analysis has the shortcoming of not being applicable to large molecules because of the limitations of current annotation techniques. The second approach, Linear Programming (LP) analysis, avoids using model meta-data by checking for a weaker condition, stoichiometric inconsistency. But this approach suffers from false negatives and has large isolation sets (the set of reactions implicated in the stoichiometric inconsistency). Results: We propose alternatives to both approaches. Our alternative to atomic mass analysis is moiety analysis. Moiety analysis uses model meta-data in the form of moieties present in chemical species. Moiety analysis avoids excessively detailed reaction specifications, and can be used with large molecules. Our alternative to LP analysis is Graphical Analysis of Mass Equivalence Sets (GAMES). GAMES has a slightly higher false negative rate than LP analysis, but it provides much better error isolation. In our studies of the BioModels Repository, the average size of isolation sets for LP analysis is 55.5; for GAMES, it is 5.4. We have created open source codes for moiety analysis and GAMES. Availability and Implementation: Our project is hosted at https://github.com/ModelEngineering/SBMLLint, which contains examples, documentation, source code files, and build scripts used to create SBMLLint. Our source code is licensed under the MIT open source license.

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Memote: A community driven effort towards a standardized genome-scale metabolic model test suite

Cited by 55 publications

References 62 publications

Genome-scale model of Rhodotorula toruloides metabolism

Genome-scale model of Rhodotorula toruloides metabolism

Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production

Detecting and Isolating Mass Balance Errors in Reaction Based Models in Systems Biology

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