MEMOTE for standardized genome-scale metabolic model testing

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; Herrgård, Markus J.; Kaafarani, Ali; Kim, Hyun Uk; King, Zachary A.; 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; Mardinoğlu, Adil; Medlock, Gregory L.; Monk, Jonathan M.; Nielsen, Jens; Nielsen, Lars K.; Nogales, Juan; Nookaew, Intawat; Palsson, Bernhard Ø.; Papin, Jason A.; Patil, Kiran Raosaheb; Poolman, Mark G.; Price, Nathan D.; Reséndis-Antonio, Osbaldo; Richelle, Anne; Rocha, Isabel; Sánchez, Benjamín J.; Schaap, Peter 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 Cheng

doi:10.1038/s41587-020-0446-y

Cited by 386 publications

(446 citation statements)

References 30 publications

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“…Predicted and measured growth rates correspond to the values included in b . ( d ) Scores provided by the quality control tool Memote 47 for iCBI655 and iML1515. “Overall score” is shown in the legend.…”

Section: Resultsmentioning

confidence: 99%

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Development of a genome-scale metabolic model ofClostridium thermocellumand its applications for integration of multi-omics datasets and strain design

Garcia¹,

Thompson

Giannone

et al. 2020

Preprint

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22Solving environmental and social challenges such as climate change requires a shift 23 from our current non-renewable manufacturing model to a sustainable bioeconomy. 24 To lower carbon emissions in the production of fuels and chemicals, plant biomass 25 feedstocks can replace petroleum using microorganisms as catalysts. The anaerobic 26 thermophile Clostridium thermocellum is a promising bacterium for bioconversion due 27 to its capability to efficiently degrade untreated lignocellulosic biomass. However, the 28 complex metabolism of C. thermocellum is not fully understood, hindering metabolic 29 engineering to achieve high titers, rates, and yields of targeted molecules. In this 30 study, we developed an updated genome-scale metabolic model of C. thermocellum 31 that accounts for recent metabolic findings, has improved prediction accuracy, and is 32 standard-conformant to ensure easy reproducibility. We illustrated two applications of 33 the developed model. We first formulated a multi-omics integration protocol and used 34 it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) 35 production in C. thermocellum. Second, we used the metabolic model to design mod-36 ular cells for efficient production of alcohols and esters with broad applications as 37 flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive 38 push-and-pull metabolic engineering strategies, but also novel manipulations around 39 important central metabolic branch-points. We anticipate the developed genome-scale 40 metabolic model will provide a useful tool for system analysis of C. thermocellum 41 metabolism to fundamentally understand its physiology and guide metabolic engineer-42 ing strategies to rapidly generate modular production strains for effective biosynthesis 43 of biofuels and biochemicals from lignocellulosic biomass. 44 Keywords-Clostridium thermocellum, biofuels, genome-scale model, metabolic model, omics 45 integration, modular cell design, ModCell 46 47Global oil reserves will be soon depleted, 1 and climate change could become a major driver of civil 48 conflict. 2 These challenges to security and the environment need to be addressed by replacing our 49 current non-renewable production of energy and materials for a renewable and carbon neutral ap-50 proach. 3 The gram-positive, thermophilic, cellulolytic, strict anaerobe C. thermocellum is capable 51 of efficient degradation of lignocellulosic biomass to produce biofuels and biomaterial precursors, 52 making this organism an ideal candidate for consolidated bioprocessing (CBP), where saccharifi-53 cation and fermentation take place in a single step. 4 However, its complex and poorly understood 54 metabolism remains the main roadblock to achieve industrially competitive titers, rates, and yields 55 of biofuels such as ethanol 5 and isobutanol. 6 56For the past decade, significant efforts have been dedicated to characterize and manipulate 57 C. thermocellum's central metabolism, due to increasing inter...

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Section: Resultsmentioning

confidence: 99%

“…To address this issue, Lieven et al recently developed the Memote framework that systematically tests for standards and best practices in GSMs. 47 We applied Memote to both the iCBI655 and E. coli iML1515 models for comparison (Figure 1 d). This analysis produces five independent scores that assess model quality.…”

Section: Resultsmentioning

confidence: 99%

Development of a genome-scale metabolic model ofClostridium thermocellumand its applications for integration of multi-omics datasets and strain design

Garcia¹,

Thompson

Giannone

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In the case of constraint-based modelling, flux values resulting from flux balance analysis are commonly reported in manuscripts and these values are not unique solutions and cannot be directly reproduced. The community tool MEMOTE 47 was developed primarily to quality control constraint based models. We are currently collaborating with the constraint-based modellers to develop tools and procedures to test reproducibility of these models.…”

Section: Discussionmentioning

confidence: 99%

Reproducibility in systems biology modelling

Tiwari

Kananathan

Roberts

et al. 2020

Preprint

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The reproducibility crisis has emerged as an important concern across many fields of science including life science, since many published results failed to reproduce. Systems biology modelling, which involves mathematical representation of biological processes to study complex system behaviour, was expected to be least affected by this crisis. While lack of reproducibility of experimental results and computational analysis could be a repercussion of several compounded factors, it was not fully understood why systems biology models with well defined mathematical expressions fail to reproduce and how prevalent it is. Hence, we systematically attempted to reproduce 455 kinetic models of biological processes published in peer-reviewed research articles from 152 journals; which is collectively a work of about 1400 scientists from 49 countries. Our investigation revealed that about half (49%) of the models could not be reproduced using the information provided in the published manuscripts. With further effort, an additional 12% of the models could be reproduced either by empirical correction or support from authors. The other 37% remained non-reproducible models due to missing parameter values, missing initial concentration, inconsistent model structure, or a combination of these factors. Among the corresponding authors of the non-reproducible model we contacted, less than 30% responded. Our analysis revealed that models published in journals across several fields of life science failed to reproduce, revealing a common problem in the peer-review process. Hence, we propose an 8-point reproducibility scorecard that can be used by authors, reviewers and journal editors to assess each model and address the reproducibility crisis.

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“…chemicals and materials production (39)(40)(41)(42) , drug targeting (43,44) , human metabolism, disease understanding (45,46) and, multi-organism interaction modeling (47,48) . MNXref is now used within tools for testing GSMNs such as MEMOTE (49) , and has been proposed as a reference for minimal standard content for metabolic network reconstruction (50) . Finally, the ability to accurately reconcile metabolites and reactions within GSMNs paves the way for applications such as multi-omics data interpretation (51,52) , and white-box AI models (53) .…”

Section: Discussionmentioning

confidence: 99%

MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

Moretti

Tran

Mehl

et al. 2020

Preprint

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MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

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MEMOTE for standardized genome-scale metabolic model testing

Cited by 386 publications

References 30 publications

Development of a genome-scale metabolic model ofClostridium thermocellumand its applications for integration of multi-omics datasets and strain design

Development of a genome-scale metabolic model ofClostridium thermocellumand its applications for integration of multi-omics datasets and strain design

Reproducibility in systems biology modelling

MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

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