MEMOTE for Standardized Genome-Scale Metabolic Model Testing

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MEMOTE for Standardized Genome-Scale Metabolic Model Testing Providence St. Joseph Health Providence St. Joseph Health Digital Commons Articles, Abstracts, and Reports 3-1-2020 MEMOTE for standardized genome-scale metabolic model testing. Christian Lieven Moritz E Beber Brett G Olivier Frank T Bergmann MericFollow Athistaman and additional works at: https://digitalcommons.psjhealth.org/publications Part of the Endocrinology, Diabetes, and Metabolism Commons, and the Genetics and Genomics SeeCommons next page for additional authors Recommended Citation Lieven, Christian; Beber, Moritz E; Olivier, Brett G; Bergmann, Frank T; Ataman, Meric; Babaei, Parizad; Bartell, Jennifer A; Blank, Lars M; Chauhan, Siddharth; 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; van Helvoirt, Wout; Henry, Christopher S; Hermjakob, Henning; Herrgård, Markus J; Kaafarani, Ali; Kim, Hyun Uk; King, Zachary; Klamt, Steffen; Klipp, Edda; Koehorst, Jasper J; König, Matthias; Lakshmanan, Meiyappan; Lee, Dong-Yup; Lee, Sang Yup; Lee, Sunjae; Lewis, Nathan E; Liu, Filipe; Ma, Hongwu; Machado, Daniel; Mahadevan, Radhakrishnan; Maia, Paulo; Mardinoglu, Adil; Medlock, Gregory L; Monk, Jonathan M; Nielsen, Jens; Nielsen, Lars Keld; Nogales, Juan; Nookaew, Intawat; Palsson, Bernhard O; Papin, Jason A; Patil, Kiran R; Poolman, Mark; Price, Nathan D; Resendis-Antonio, Osbaldo; Richelle, Anne; Rocha, Isabel; Sánchez, Benjamín J; Schaap, Peter J; Malik Sheriff, Rahuman S; Shoaie, Saeed; Sonnenschein, Nikolaus; Teusink, Bas; Vilaça, Paulo; Vik, Jon Olav; Wodke, Judith A H; Xavier, Joana C; Yuan, Qianqian; Zakhartsev, Maksim; and Zhang, Cheng, "MEMOTE for standardized genome-scale metabolic model testing." (2020). Articles, Abstracts, and Reports. 2926. https://digitalcommons.psjhealth.org/publications/2926 This Article is brought to you for free and open access by Providence St. Joseph Health Digital Commons. It has been accepted for inclusion in Articles, Abstracts, and Reports by an authorized administrator of Providence St. Joseph Health Digital Commons. For more information, please contact [email protected]. Authors Christian Lieven, Moritz E Beber, Brett G Olivier, Frank T Bergmann, Meric Ataman, Parizad Babaei, Jennifer A Bartell, Lars M Blank, Siddharth Chauhan, Kevin Correia, Christian Diener, Andreas Dräger, Birgitta E Ebert, Janaka N Edirisinghe, José P Faria, Adam M Feist, Georgios Fengos, Ronan M T Fleming, Beatriz García-Jiménez, Vassily Hatzimanikatis, Wout van Helvoirt, Christopher S Henry, Henning Hermjakob, Markus J Herrgård, Ali Kaafarani, Hyun Uk Kim, Zachary King, Steffen Klamt, Edda Klipp, Jasper J Koehorst, Matthias König, Meiyappan Lakshmanan, Dong-Yup Lee, Sang Yup Lee, Sunjae Lee, Nathan E Lewis, Filipe Liu, Hongwu Ma, Daniel Machado, Radhakrishnan Mahadevan, Paulo Maia, Adil Mardinoglu, Gregory L Medlock, Jonathan M Monk, Jens Nielsen, Lars Keld Nielsen, Juan Nogales, Intawat Nookaew, Bernhard O Palsson, Jason A Papin, Kiran R Patil, Mark Poolman, Nathan D Price, Osbaldo Resendis-Antonio, Anne Richelle, Isabel Rocha, Benjamín J Sánchez, Peter J Schaap, Rahuman S Malik Sheriff, Saeed Shoaie, Nikolaus Sonnenschein, Bas Teusink, Paulo Vilaça, Jon Olav Vik, Judith A H Wodke, Joana C Xavier, Qianqian Yuan, Maksim Zakhartsev, and Cheng Zhang This article is available at Providence St. Joseph Health Digital Commons: https://digitalcommons.psjhealth.org/ publications/2926 correspondence correspondence MEMOTE for standardized genome-scale metabolic model testing To the Editor — Reconstructing metabolic collaboratively develop this package, Stoichiometric inconsistency, erroneously reaction networks enables the development updating it based on user input. It has been produced energy metabolites7 and of testable hypotheses of an organism’s adopted by a wide range of constraint- permanently blocked reactions are identified metabolism under different conditions1. based modeling software and public model by MEMOTE. Errors in stoichiometries may State-of-the-art genome-scale metabolic repositories (http://cbmpy.sourceforge. result in the production of ATP or redox models (GEMs) can include thousands of net/ and refs. 10–15), and should therefore be cofactors from nothing2 and are detrimental metabolites and reactions that are assigned considered the standard for encoding GEMs. to the performance of the model when using to subcellular locations. Gene–protein– Second, we present MEMOTE flux-based analysis4. reaction (GPR) rules and annotations (/’mi:moʊt/ in international phonetic MEMOTE enables a quick comparison of using database information can add meta- alphabet notation), an open-source Python any two given models, in which individual information to GEMs. GEMs with metadata software that represents a unified approach test results are quantified and condensed to can be built using standard reconstruction to ensure the formally correct definition of calculate an overall score (Supplementary protocols2, and guidelines have been put in SBML3FBC and provides quality control and Note 1). In addition to these consensus place for tracking provenance and enabling continuous quality assurance of metabolic tests, researchers can supply experimental interoperability, but a standardized means models with tools and best practices already data from growth and gene perturbation of quality control for GEMs is lacking3. Here used in software development16,17. MEMOTE studies in a range of input formats (.csv, we report a community effort to develop a accepts stoichiometric models encoded .tsv, .xls or .xslx) in MEMOTE. To support test suite named MEMOTE (for metabolic in SBML3FBC and previous versions as reproducibility, researchers can configure model tests) to assess GEM quality. input. In addition to structural validation MEMOTE to recognize specific data types Incompatible description formats and analogous to the SBML validator18, as input to predefined experimental tests for missing annotations4 limit GEM reuse. MEMOTE benchmarks metabolic models model validation (Supplementary Note 2). Moreover, numerical errors5 and omission using consensus tests from four general There are two main workflows for of essential cofactors6 in a single biomass areas: annotation, basic tests, biomass MEMOTE (Fig. 1a and Supplementary objective function can have substantial reaction and stoichiometry. Figs. 1–3). For peer review, MEMOTE impact on the predictive performance of Annotation tests check that a model is can produce either a ‘snapshot report’ or a GEM. Failure to make checks for flux annotated according to community standards a ‘diff report’ that display MEMOTE test cycles and imbalances can render model with minimum information required in results of one single or multiple models, predictions untrustworthy7. annotation of models (MIRIAM)-compliant respectively. For model reconstruction, Every year, increasing numbers of cross-references19, that all primary identifiers MEMOTE helps users to create a version- manually curated and automatically belong to the same namespace rather than controlled repository of the model and generated GEMs are published, including being fractured across several namespaces, to activate continuous integration toward those for human and cancer tissue models8. and that components are described using building a ‘history report’ that records We believe that it is essential to optimize Systems Biology Ontology (SBO) terms20. the results of each tracked edit of the GEM reproducibility and reuse. Researchers A lack of explicit, standardized annotations model. Although a model repository can need models that are software-agnostic, complicates the use, comparison and be used offline, we encourage community with components that have standardized, extension of GEMs, and thus strongly collaboration via distributed version control database-independent identifiers. Default hampers collaboration3,4. development platforms, such as GitHub conditions and mathematically specified Basic tests check the formal correctness (https://github.com), GitLab (https://gitlab. modeling formulations must be precisely of a model and verify the presence com/) or BioModels12 (http://wwwdev.ebi. defined to allow reproduction of the original of components such as metabolites, ac.uk/biomodels/). MEMOTE is tightly model predictions. Models must produce compartments, reactions and genes. These integrated with GitHub. Models generated feasible phenotypes under various conditions. tests also check for metabolite formula and and versioned in MEMOTE can easily Finally, data used to build any model must be charge information, and GPR rules. General be uploaded to GitLab and BioModels. made available in a reusable format. quality metrics, such as the degree of Collaborative model reconstruction with A dual approach could be used to improve metabolic coverage representing the ratio of MEMOTE as benchmark can occur using all GEM reuse and reproducibility. First, we reactions and genes21, are also checked. three software platforms (Fig. 1b). advocate adoption of the latest version of the A model is tested for production of We validated MEMOTE using models Systems Biology Markup Language (SBML) biomass precursors in different conditions, from seven GEM collections (Fig. 2, level 3 flux balance constraints (SBML3FBC) for biomass consistency, for nonzero Supplementary Table 1 and Supplementary package9 as the primary description and growth rate and for direct precursors. The Methods),
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