In Silico Reconstruction of the Metabolic Pathways of
            <i>Lactobacillus plantarum</i>
            : Comparing Predictions of Nutrient Requirements with Those from Growth Experiments

Teusink, Bas; Enckevort, Frank H. J. van; Francke, Christof; Wiersma, Anne; Wegkamp, Arno; Smid, Eddy J.; Siezen, Roland J.

doi:10.1128/aem.71.11.7253-7262.2005

Cited by 161 publications

(181 citation statements)

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“…Genome-scale Metabolic Model: Features-We constructed a genome-scale model on the basis of the metabolic reconstruction reported previously (7). Such a model defines all geneprotein reaction associations, which can be complex (6,7).…”

Section: Resultsmentioning

confidence: 99%

“…Such a model defines all geneprotein reaction associations, which can be complex (6,7). The reactions form a metabolic network that needs to be carefully curated for the purpose of constraint-based modeling (see Refs.…”

Section: Resultsmentioning

confidence: 99%

“…Modeling-The genome-scale metabolic model was based on a metabolic reconstruction that was described in detail elsewhere (7). The model was developed within the Simpheny TM software environment (Genomatica Inc., San Diego, CA).…”

Section: Construction Of a Genome-scale Model And Constraint-basedmentioning

confidence: 99%

“…L. plantarum grows only in rich media, such as decomposed plant materials and meat, and consequently has become auxotrophic for many vitamins and amino acids. Hence, the chemically defined medium used in our laboratory contains glucose, citrate, acetate, nucleosides and nucleobases, vitamins, and 18 amino acids (7). We have recently performed a metabolic reconstruction, based on a careful annotation of the genomic content (8), combined with single omission experiments to infer functional pathways (7).…”

mentioning

confidence: 99%

“…Hence, the chemically defined medium used in our laboratory contains glucose, citrate, acetate, nucleosides and nucleobases, vitamins, and 18 amino acids (7). We have recently performed a metabolic reconstruction, based on a careful annotation of the genomic content (8), combined with single omission experiments to infer functional pathways (7). Here we report on the construction and analysis of a genomescale metabolic model of L. plantarum, and the comparison with experimental fermentation data.…”

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confidence: 99%

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Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using a Genome-scale Metabolic Model

Teusink

Wiersma

Molenaar

et al. 2006

Journal of Biological Chemistry

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A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.Genome-scale metabolic models have become available for an increasing number of organisms (1). These models link genomic information to metabolic reaction networks and have gained considerable attention, not only within the context of metabolic engineering (2, 3), but also from the bioinformatics field (4, 5). An increasing repertoire of modeling techniques is available for exploring the capabilities of the metabolic network, and for understanding genotype-phenotype relationships (2, 6). These techniques are often referred to as constraint-based modeling techniques.So far, constraint-based modeling techniques have mainly been applied to microorganisms that grow on a minimal salt medium containing a single carbon source (1). However, in many biological niches, as well as in multicellular organisms, cells encounter more complex nutritional environments. In analogy, in many industrial and biotechnological applications of microorganisms and cell lines, complex media are used, either because the cells are auxotrophic for nutrients or such media are cheaper. Multiple inputs for the metabolic network complicate constraint-based modeling approaches considerably. Moreover, one may expect quite different metabolic ...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Construction Of a Genome-scale Model And Constraint-basedmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using a Genome-scale Metabolic Model

Teusink

Wiersma

Molenaar

et al. 2006

Journal of Biological Chemistry

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261

271

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Construction and Applications of Genome‐Scale in silico Metabolic Models for Strain Improvement

Lee

Kim

Yun

et al. 2009

Systems Biology and Synthetic Biology

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Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Masoudi‐Nejad

Salehzadeh‐Yazdi

Akbari‐Birgani

et al. 2013

Biological Knowledge Discovery Handbook

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INTRODUCTIONGenome-scale metabolic reconstruction is the ultimate goal of system biologists for finding out the genotype-phenotype relationship. For achieving this purpose, we need enough knowledge about the metabolic pathways and genome annotation. During the present study, we aim to describe the foundational concepts, central to omics data, model formulation, history, method, and applications of metabolic network reconstruction as well as some related sources. In Section 45.2, we describe omics data and high-throughput technologies for gaining these data. In section 45.3, we present different ways for metabolic network modeling. In section 45.4, we summarize the history of genome-scale modeling as one of the most important methods for metabolic network modeling. In sections 45.5 and 45.6, we elucidate how genome-scale metabolic models could be generated and what their applications are. Finally, in section 45.7, we review biochemical pathways and genome annotation databases. OMICs ERAInnovative omics technologies such as genomics, transcriptomics, proteomics, and metabolomics facilitate a strategy toward the simultaneous analysis of the large number of genes, transcripts, proteins, and metabolites. Therefore, a huge volume of data has generated about the make-up of cells and their behavior at various cellular levels and different environmental conditions, which enable us to reconstruct genome-scale biomolecular networks (e.g., transcriptional regulatory networks, interactomic networks, and metabolic networks)Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, First Edition. Edited by Mourad Elloumi and Albert Y. Zomaya.

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In Silico Reconstruction of the Metabolic Pathways of Lactobacillus plantarum : Comparing Predictions of Nutrient Requirements with Those from Growth Experiments

Cited by 161 publications

References 60 publications

Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using a Genome-scale Metabolic Model

Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using a Genome-scale Metabolic Model

Construction and Applications of Genome‐Scale in silico Metabolic Models for Strain Improvement

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

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