Evaluating Accessibility, Usability and Interoperability of Genome-Scale Metabolic Models for Diverse Yeasts Species
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Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species Downloaded from: https://research.chalmers.se, 2021-09-30 12:00 UTC Citation for the original published paper (version of record): Domenzain Del Castillo Cerecer, I., Li, F., Kerkhoven, E. et al (2021) Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species FEMS Yeast Research, 21(1) http://dx.doi.org/10.1093/femsyr/foab002 N.B. When citing this work, cite the original published paper. research.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004. research.chalmers.se is administrated and maintained by Chalmers Library (article starts on next page) FEMS Yeast Research, 21, 2021, foab002 doi: 10.1093/femsyr/foab002 Advance Access Publication Date: 11 January 2021 Minireview MINIREVIEW Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species Ivan´ Domenzain1,2,†,‡,FeiranLi1,2,†,§, Eduard J. Kerkhoven1,2 and Verena Siewers1,2,* 1Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivagen¨ 10, SE-412 96, Gothenburg, Sweden and 2Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivagen¨ 10, SE-412 96, Gothenburg, Sweden ∗Corresponding author: Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivagen¨ 10, SE-412 96 Gothenburg, Sweden. E-mail: [email protected] One sentence summary: Review on computational models of metabolism for diverse yeast species, their development history, applications and critical assessment of their accessibility, usability and interoperability. †Both authors contributed equally to this work. Editor: Jean-Marc Daran ‡Ivan´ Domenzain, http://orcid.org/0000-0002-5322-2040 §Feiran Li, http://orcid.org/0000-0001-9155-5260 ABSTRACT Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM)was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that—despite this long modeling history—the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public. Keywords: genome-scale metabolic models; yeast species; systems biology; accessibility; usability; interoperability INTRODUCTION capabilities of a living organism to reconstruct a complete metabolic map (Nielsen 2017). Another widely used approach Genome-scale metabolic model reconstruction has been estab- for model reconstruction consists of the use of one or multiple lished as one of the major modeling approaches for systems- well-curated networks as scaffolds, due to the high degree of level metabolic studies (Gu et al. 2019). These models are mainly conservation of metabolism for phylogenetically close species. built in a bottom-up approach, in which genome information is Metabolic models have been proven to be useful as knowledge combined with the accumulated knowledge about the metabolic databases (Herrgard˚ et al. 2008), tools for contextualization of Received: 14 September 2020; Accepted: 8 January 2021 C The Author(s) 2021. Published by Oxford University Press on behalf of FEMS. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] 1 2 FEMS Yeast Research, 2021, Vol. 21, No. 1 omics data (Kerkhoven et al. 2016) and for guiding metabolic connectivity and the number of included metabolites, mak- engineering projects (Meadows et al. 2016), enabling systematic ing it a suitable model for simulation purposes (Dobson et al. explorations of the relationship between genotypes and pheno- 2010). Further updates to the consensus metabolic network have types. shown to improve predictions on gene essentiality, induced aux- The metabolic model iFF708 (Forster¨ et al. 2003)ofSaccha- otroph phenotypes and cellular growth on diverse environments romyces cerevisiae, the genome of which was the first eukaryotic (Yeast5 (Heavner et al. 2012), Yeast6 (Heavner et al. 2013)and one to be sequenced (Goffeau et al. 1996), was the first published Yeast7 (Aung, Henry and Walker 2013)). In 2019, a new ver- GEM for its entire domain in 2003. This model has been used as a sion of the consensus metabolic network, Yeast8, was published scaffold for further network refinements (Duarte, Herrgard˚ and (Lu et al. 2019), its reconstruction process combined informa- Palsson 2004; Kuepfer, Sauer and Blank 2005; Herrgard˚ et al. 2006; tion from previous GEMs, different curated databases such as Nookaew et al. 2008), which has facilitated the development of KEGG (Kanehisa et al. 2016), SGD (Hellerstedt et al. 2017), Bio- metabolic models for several other budding yeast species over Cyc (Karp et al. 2019), Reactome (Fabregat et al. 2018 )andUniProt the years, due to their well evolutionarily-conserved metabolic (The UniProt Consortium 2017) and experimental data on sub- capabilities (Shen et al. 2018). strate usage. Furthermore, Yeast8 provides an ecosystem of mul- Multiple model reconstructions exist, not just for S. cerevisiae, tilayer models suited for different kinds of phenotype predic- but for several other yeast species. These reconstructions have tions, ranging from 1011 strain-specific models to incorpora- usually been carried out by different research groups, resulting tion of enzyme constraints (ecYeast8) and protein 3D structures in specific network improvements according to their scientific (proYeast; Lu et al. 2019). interests, but at the same time yielding incompatible identifiers In parallel with the development of the consensus net- for reactions and metabolites hampering any systematic com- work, iSce926 (Chowdhury, Chowdhury and Maranas 2015)was parison and evaluation across models (Herrgard˚ et al. 2008). derived from Yeast7 (Aung, Henry and Walker 2013) in 2015, As GEMs are valuable tools for a wide variety of applications, incorporating gene essentiality and synthetic lethality informa- their end users vary from academic researchers with different tion to curate gene-reaction rules. The model iSc-AMRS-1 (Wich- backgrounds and levels of computational skills, to professionals mann et al. 2016) was developed from iLL672 (Kuepfer, Sauer and in the biotechnology and pharmaceutical industries. Therefore, Blank 2005) in 2016, mainly by curation of proton balancing for there is a strong need for computational metabolic models to be mitochondrial ATP production and reaction reversibility, aiming accessible and published in a ready-to-use format, which facili- to improve flux distribution predictions in order to investigate tates their utilization by non-expert users. Additionally, the use production of isopropenoids. of consistent and standardized identifiers for their components The model SpoMBEL1693 for Schizosaccharomyces pombe,a enables comparisons across models, thus simplifying the pro- model organism for eukaryotic cell cycle studies, was devel- cess of finding the best model for a given application. oped in 2012 using annotated genes and reactions from the KEGG database as a draft network (Sohn et al. 2012). iNX804, Latest developments on yeasts GEMs a metabolic model for Candida glabrata, known as a platform organism for pyruvate production, was reconstructed in 2013 The development, interconnections and applications of and used for identification of gene targets for enhanced pro- metabolic models for different yeast species have been reviewed duction of pyruvate-derived fine chemicals (Xu et al. 2013). The extensively (Sanchez´ and Nielsen 2015; Lopes and Rocha 2017; metabolism of Candida tropicalis, known as a promising host for Castillo, Patil and Jouhten 2019; Chen, Li and Nielsen 2019)how- α, ω-dicarboxylic acids production, has been studied with the ever, the list of yeast GEM models is continuously increasing model iCT646, reconstructed through the collection of multiple both in number of GEMs and encompassed species. Here we database information in 2016 (Mishra et al. 2016). The model briefly summarize the development history of all models for iOD907, a metabolic network for Kluyveromyces lactis, a yeast diverse yeast species that are currently available in the scientific commonly used in the dairy industry, was published in 2014 literature. The validation strategies and main applications of (Dias et al. 2014). Its reconstruction process used iMM904, for these models, as described in their original publications, are S. cerevisiae, as a scaffold