Reconstruction and Validation of <i>Saccharomyces cerevisiae</i> iND750, a Fully Compartmentalized Genome-Scale Metabolic Model

Duarte, Natalie C.; Herrgård, Markus J.; Palsson, Bernhard Ø.

doi:10.1101/gr.2250904

Cited by 584 publications

(628 citation statements)

References 48 publications

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“…The co-culture stoichiometric models used in this study were adapted from the iJR904 E. coli (Reed et al, 2003) and iND750 S. cerevisiae (Duarte et al, 2004) genome-scale metabolic reconstructions. The wild-type S. cerevisiae iND750 model consists of 750 genes and 1,149 intracellular reactions, which are divided into seven distinct intracellular compartments.…”

Section: Co-culture Stoichiometric Modelsmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

125

131

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Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.

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Section: Co-culture Stoichiometric Modelsmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

125

131

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“…Let us consider the application of the algorithm to the S. cerevisiae network iND750 (Duarte et al, 2004), consisting of 1266 reactions. The assumption that the network maximizes its biomass production forces 1086 of the reactions to have a fixed value.…”

Section: Resultsmentioning

confidence: 99%

“…Reactions (Feist et al, 2007) 2382 1972 (Duarte et al, 2004) is compared to the flux presented in the original paper. 108 of a possible 180 fluxes were found to differ.…”

Section: Modelmentioning

confidence: 99%

Flux balance analysis: A geometric perspective

Smallbone

Simeonidis

2009

Journal of Theoretical Biology

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a b s t r a c tAdvances in the field of bioinformatics have led to reconstruction of genome-scale networks for a number of key organisms. The application of physicochemical constraints to these stoichiometric networks allows researchers, through methods such as flux balance analysis, to highlight key sets of reactions necessary to achieve particular objectives. The key benefits of constraint-based analysis lie in the minimal knowledge required to infer systemic properties. However, network degeneracy leads to a large number of flux distributions that satisfy any objective; moreover, these distributions may be dominated by biologically irrelevant internal cycles. By examining the geometry underlying the problem, we define two methods for finding a unique solution within the space of all possible flux distributions; such a solution contains no internal cycles, and is representative of the space as a whole. The first method draws on typical geometric knowledge, but cannot be applied to large networks because of the high computational complexity of the problem. Thus a second method, an iteration of linear programs which scales easily to the genome scale, is defined. The algorithm is run on four recent genome-scale models, and unique flux solutions are found. The algorithm set out here will allow researchers in flux balance analysis to exchange typical solutions to their models in a reproducible format. Moreover, having found a single solution, statistical analyses such as correlations may be performed.

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“…A combination of automated and manual procedures can help turn a genome sequence into a metabolic model, and a variety are available (e.g. Refs [26][27][28][29][30]). The qualitative metabolic network or logical graph, popularised in the biochemical wall charts [25] and resources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [31], is then the starting point for metabolic modelling.…”

Section: Metabolic Reconstruction Is Now Mature and Timelymentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

264

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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Reconstruction and Validation of Saccharomyces cerevisiae iND750, a Fully Compartmentalized Genome-Scale Metabolic Model

Cited by 584 publications

References 48 publications

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Flux balance analysis: A geometric perspective

Systems biology, metabolic modelling and metabolomics in drug discovery and development

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