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 MT 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 AH Wodke, Joana C Xavier, Qianqian Yuan, Maksim Zakhartsev, Cheng Zhang
Nature Biotechnology. 2020 Mar 2:1-5.
Abstract
Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions. 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 protocols, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.
Read the publication here: https://www.nature.com/articles/s41587-020-0446-y
