A dynamic, genome-scale flux model of Lactococcus lactis to increase specific recombinant protein expression

Oddone, Gian M.; Mills, David A.; Block, David E.

doi:10.1016/j.ymben.2009.07.007

Cited by 44 publications

(21 citation statements)

References 43 publications

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“…dFBA models have been developed to describe the dynamic metabolism of E.coli [10], Saccharomyces cerevisiae [19], Lactococcus lactis [12], and even for a more complicated coculture system of E.coli and Saccharomyces cerevisiae [20]. In this study, we developed dFBA for analyzing metabolic states of S. oneidensis MR-1.…”

Section: Discussionmentioning

confidence: 99%

“…However, since cells may show suboptimal metabolism and reprogram their metabolic fluxes under different environmental conditions, the commonly used objective function is insufficient to describe cell physiologies [7], [8], [9]. Furthermore, FBA assumes steady-state metabolic conditions, and thus is unable to directly analyze the transience of cell metabolism [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Feng

Chen

et al. 2012

PLoS Comput Biol

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Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Feng

Chen

et al. 2012

PLoS Comput Biol

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“…Other abbreviations are as follows: AA, amino acid; ox., oxidative; transport p , transport per , and transport c , transport into plastid, peroxisome, and cytosol, respectively. a large number of kinetic parameters (Mahadevan et al, 2002;Hjersted and Henson, 2006;Oddone et al, 2009), in this study, SOA-based dFBA was computed by integrating a dynamic FPM. A similar approach has been presented by Feng et al (2012), who integrated FBA into a kinetic model to get insight into the dynamics of the metabolism of the unicellular Shewanella oneidensis.…”

Section: MMMmentioning

confidence: 99%

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

et al. 2013

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Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.

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“…GEMs have primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions (McCloskey et al, 2013). Initially, these models only considered well -characterized organisms; nevertheless, the interest in the generation of metabolic models of less characterized and complex biological systems has progressively increased, including the GEMs of several lactic acid bacteria, such as Lactococcus lactis (Oliveira et al, 2005; Oddone et al, 2009; Verouden et al, 2009; Flahaut et al, 2013), Lactobacillus plantarum (Teusink et al, 2006) and Streptococcus thermophilus (Pastink et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Genome-Scale Reconstruction of the Metabolic Network in Oenococcus oeni to Assess Wine Malolactic Fermentation

et al. 2017

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Oenococcus oeni is the main responsible agent for malolactic fermentation in wine, an unpredictable and erratic process in winemaking. To address this, we have constructed and exhaustively curated the first genome-scale metabolic model of Oenococcus oeni, comprising 660 reactions, 536 metabolites and 454 genes. In silico experiments revealed that nutritional requirements are predicted with an accuracy of 93%, while 14 amino acids were found to be essential for the growth of this bacterial species. When the model was applied to determine the non-growth associated maintenance, results showed that O. oeni grown at 12% ethanol concentration spent 30 times more ATP to stay alive than in the absence of ethanol. Most of this ATP is employed for extruding protons outside of the cell. A positive relationship was also found between specific consumption rates of fructose, amino acids, oxygen, and malic acid and the specific production rates of erythritol, lactate, and acetate, according to the ethanol content of the medium. The metabolic model reconstructed here represents a unique tool to predict the successful completion of wine malolactic fermentation carried out either by different strains of Oenococcus oeni, as well as at any particular physico-chemical composition of wine. It will also allow the development of consortium metabolic models that could be applied to winemaking to simulate and understand the interactions between O. oeni and other microorganisms that share this ecological niche.

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A dynamic, genome-scale flux model of Lactococcus lactis to increase specific recombinant protein expression

Cited by 44 publications

References 43 publications

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Genome-Scale Reconstruction of the Metabolic Network in Oenococcus oeni to Assess Wine Malolactic Fermentation

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