The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism

Nookaew, Intawat; Jewett, Michael C.; Meechai, Asawin; Thammarongtham, Chinae; Laoteng, Kobkul; Cheevadhanarak, Supapon; Nielsen, Jens; Bhumiratana, Sakarindr

doi:10.1186/1752-0509-2-71

Cited by 154 publications

(167 citation statements)

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“…A clear example is lipid metabolism in S. cerevisiae, where a recent update to the existing genome-scale metabolic reconstruction, iN795, included 118 previously unreported lipid reactions relative to iND750 (see Table II). Of those 118 lipid metabolism participating reactions, 28 were assigned to ergosterol esterification and lipid degradation-previously not represented (Nookaew et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Industrial systems biology

Otero

Nielsen

2009

Biotech & Bioengineering

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134

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ABSTRACT:The chemical industry is currently undergoing a dramatic change driven by demand for developing more sustainable processes for the production of fuels, chemicals, and materials. In biotechnological processes different microorganisms can be exploited, and the large diversity of metabolic reactions represents a rich repository for the design of chemical conversion processes that lead to efficient production of desirable products. However, often microorganisms that produce a desirable product, either naturally or because they have been engineered through insertion of heterologous pathways, have low yields and productivities, and in order to establish an economically viable process it is necessary to improve the performance of the microorganism. Here metabolic engineering is the enabling technology. Through metabolic engineering the metabolic landscape of the microorganism is engineered such that there is an efficient conversion of the raw material, typically glucose, to the product of interest. This process may involve both insertion of new enzymes activities, deletion of existing enzyme activities, but often also deregulation of existing regulatory structures operating in the cell. In order to rapidly identify the optimal metabolic engineering strategy the industry is to an increasing extent looking into the use of tools from systems biology. This involves both x-ome technologies such as transcriptome, proteome, metabolome, and fluxome analysis, and advanced mathematical modeling tools such as genome-scale metabolic modeling. Here we look into the history of these different techniques and review how they find application in industrial biotechnology, which will lead to what we here define as industrial systems biology.

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Section: Discussionmentioning

confidence: 99%

Industrial systems biology

Otero

Nielsen

2009

Biotech & Bioengineering

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134

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“…0 and P l b l ¼ 1, and we further suppose, as a second simplifying assumption, that these mass fractions are constant. The biomass composition has been empirically determined for several microorganisms, usually for a specific growth condition [82][83][84]. The incorporation of the precursor metabolites into the biomass, in the proportions b l in which they compose the latter, can be seen as a macroreaction.…”

Section: Connecting Metabolism and Growth: Flux Balance Analysismentioning

confidence: 99%

Mathematical modelling of microbes: metabolism, gene expression and growth

Jong

Casagranda

Giordano

et al. 2017

J. R. Soc. Interface.

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The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.

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“…For examples, in the reconstructed metabolic networks of yeast Saccharomyces cerevisiae [2], filamentous fungi Aspergillus oryzae [3], Aspergillus nidulans [4], and Aspergillus niger [5], and bacterium Streptomyces coelicolor [6], between 6% to 19% of the biochemical reactions are metabolic gaps.…”

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

“…However, we have also run MeGafiller on the other four networks, those for S. cerevisiae iIN800 [2], A. niger iMA871 [5], A. nidulans iHD666 [4], as well as S. coelicolor iIB711 [6]. These metabolic networks were obtained from BioMet Toolbox [29] website (http://biomet-toolbox.…”

Section: Data Sourcesmentioning

confidence: 99%

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Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

Nguyen¹,

Vongsangnak²,

Shen³

et al. 2014

J Proteomics Bioinform

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BackgroundThe metabolic network of a cell is the complete set of interconnected metabolic processes that determine the physiological and biochemical properties of the cell. In recent years, metabolic networks have enormously contributed to our understanding of metabolic genotype and phenotype relationship. This leads to important applications through systems biology and metabolic engineering. Recently, metaproteome-scale metabolic network reconstruction has also emerged as a promising and challenging approach for investigating the metabolism of microbial communities [1].In recent years, there has been an effort to reconstruct the genomescale metabolic networks for hundreds species [2][3][4][5][6][7]. In principle, the reconstruction of metabolic networks is an iterative multi-stage process [8,9], which starts from gene annotation, and goes all the way to network development. Several sophisticated techniques have been developed for metabolic network reconstruction [10][11][12][13][14][15]. Metabolic Gaps and their ImplicationsHowever, most of the reconstructed networks remain incomplete, namely there are significant numbers of metabolic gaps [16,17]. A metabolic gap in a network for genome (G) is a metabolic reaction (R) (described by its EC number) that is present in the network. But the annotation through the network reconstruction methods have failed to find the corresponding gene in G that is responsible for that reaction R. We distinguish two types of metabolic gaps: local metabolic gap where the corresponding gene responsible for R can be found in other related organisms and global metabolic gap where the corresponding gene responsible for R has not been found in any known organism or have not been so annotated in any genome. [5], and bacterium Streptomyces coelicolor [6], between 6% to 19% of the biochemical reactions are metabolic gaps. Metabolic gaps impede downstream biological analysis of these metabolic networks. For examples, in the reconstructed metabolic networks of yeast Saccharomyces cerevisiae [2], filamentous fungi Aspergillus oryzae [3], Aspergillus nidulans [4], and Aspergillus nigerFilling these metabolic gaps (and thus, enhancing these networks) is the most time-consuming task that may take years to complete since there is a lot of manual curation involved [16][17][18]. This can be a bottleneck for gaining high quality metabolic networks. Our work therefore proposes to fill those metabolic gaps by considering new algorithmic methods. Current Metabolic Gap Filling MethodsCurrent direct methods for filling local metabolic gaps (i.e. genes that are un-annotated in the target organism, but have been found in Abstract Background: A bottleneck in investigating the cellular metabolism and physiology of organisms is the presence of metabolic gaps in the genome-scale metabolic networks. Metabolic gaps are reactions in the network that the corresponding genes have not yet been identified. Previous gap filling methods are generally based on identifying protein family in related organisms and then use ...

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The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism

Cited by 154 publications

References 51 publications

Industrial systems biology

Industrial systems biology

Mathematical modelling of microbes: metabolism, gene expression and growth

Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

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