MEMOTE for standardized genome-scale metabolic model testing
Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.
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| Format: | carta al director biblioteca |
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Springer Nature
2020-03
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| Subjects: | Biochemical networks, Computational models, Software, |
| Online Access: | http://hdl.handle.net/10261/230245 http://dx.doi.org/10.13039/501100000781 http://dx.doi.org/10.13039/501100002347 http://dx.doi.org/10.13039/501100001659 http://dx.doi.org/10.13039/501100004063 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100000057 http://dx.doi.org/10.13039/100000865 http://dx.doi.org/10.13039/501100003725 http://dx.doi.org/10.13039/501100003627 http://dx.doi.org/10.13039/501100000769 http://dx.doi.org/10.13039/100000888 http://dx.doi.org/10.13039/100001906 http://dx.doi.org/10.13039/100000002 http://dx.doi.org/10.13039/501100000780 |
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Biochemical networks Computational models Software Biochemical networks Computational models Software 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 K. Nogales, Juan Nookaew, Intawat Palsson, Bernhard Ø 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 Zhang, Cheng MEMOTE for standardized genome-scale metabolic model testing |
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Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality. |
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Research Council of Norway |
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Research Council of Norway 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 K. Nogales, Juan Nookaew, Intawat Palsson, Bernhard Ø 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 Zhang, Cheng |
| format |
carta al director |
| topic_facet |
Biochemical networks Computational models Software |
| author |
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 K. Nogales, Juan Nookaew, Intawat Palsson, Bernhard Ø 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 Zhang, Cheng |
| author_sort |
Lieven, Christian |
| title |
MEMOTE for standardized genome-scale metabolic model testing |
| title_short |
MEMOTE for standardized genome-scale metabolic model testing |
| title_full |
MEMOTE for standardized genome-scale metabolic model testing |
| title_fullStr |
MEMOTE for standardized genome-scale metabolic model testing |
| title_full_unstemmed |
MEMOTE for standardized genome-scale metabolic model testing |
| title_sort |
memote for standardized genome-scale metabolic model testing |
| publisher |
Springer Nature |
| publishDate |
2020-03 |
| url |
http://hdl.handle.net/10261/230245 http://dx.doi.org/10.13039/501100000781 http://dx.doi.org/10.13039/501100002347 http://dx.doi.org/10.13039/501100001659 http://dx.doi.org/10.13039/501100004063 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100000057 http://dx.doi.org/10.13039/100000865 http://dx.doi.org/10.13039/501100003725 http://dx.doi.org/10.13039/501100003627 http://dx.doi.org/10.13039/501100000769 http://dx.doi.org/10.13039/100000888 http://dx.doi.org/10.13039/100001906 http://dx.doi.org/10.13039/100000002 http://dx.doi.org/10.13039/501100000780 |
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1816136056079122432 |
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dig-inia-es-10261-2302452024-10-24T13:15:29Z MEMOTE for standardized genome-scale metabolic model testing 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 K. Nogales, Juan Nookaew, Intawat Palsson, Bernhard Ø 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 Zhang, Cheng Research Council of Norway Innovation Fund Denmark European Commission National Institutes of Health (US) German Research Foundation Novo Nordisk Foundation W. M. Keck Foundation Ministerio de Economía y Competitividad (España) Knut and Alice Wallenberg Foundation Federal Ministry of Education and Research (Germany) Federal Ministry of Education and Research (Germany) Bill & Melinda Gates Foundation National Research Foundation of Korea Rural Development Administration (South Korea) Swiss National Science Foundation University of Oxford European Research Council Washington Research Foundation National Institute of General Medical Sciences (US) Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] Biochemical networks Computational models Software Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality. We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503). 2021-02-22T09:00:19Z 2021-02-22T09:00:19Z 2020-03 2021-02-22T09:00:19Z carta al director http://purl.org/coar/resource_type/c_545b Nature Biotechnology 38: 272-276 (2020) 1087-0156 http://hdl.handle.net/10261/230245 10.1038/s41587-020-0446-y 1546-1696 http://dx.doi.org/10.13039/501100000781 http://dx.doi.org/10.13039/501100002347 http://dx.doi.org/10.13039/501100001659 http://dx.doi.org/10.13039/501100004063 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100000057 http://dx.doi.org/10.13039/100000865 http://dx.doi.org/10.13039/501100003725 http://dx.doi.org/10.13039/501100003627 http://dx.doi.org/10.13039/501100000769 http://dx.doi.org/10.13039/100000888 http://dx.doi.org/10.13039/100001906 http://dx.doi.org/10.13039/100000002 http://dx.doi.org/10.13039/501100000780 #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/H2020/686070 info:eu-repo/grantAgreement/EC/H2020/686585 info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BIO2014-59528-JIN info:eu-repo/grantAgreement/EC/H2020/666053 http://dx.doi.org/10.1038/s41587-020-0446-y Sí none Springer Nature |