Metabolic modelling of microbes: the flux‐balance approach

Edwards, Jeremy S.; Covert, Markus W.; Palsson, Bernhard Ø.

doi:10.1046/j.1462-2920.2002.00282.x

Cited by 334 publications

(260 citation statements)

References 81 publications

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“…Flux balance analysis is a constraints-based computational approach that requires little experimental data and offers an estimation of the range of feasible flux distributions in steadily growing cells 21,23 . Although it has proven powerful, especially for E. coli 24 , the precise determination of fluxes by FBA relies on an objective function and related assumptions (e.g., that E. coli maximizes biomass yield per molecule of carbon source consumed [23][24][25].…”

Section: Alternative Approachesmentioning

confidence: 99%

Kinetic flux profiling for quantitation of cellular metabolic fluxes

2008

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This protocol enables quantitation of metabolic fluxes in cultured cells. Measurements are based on the kinetics of cellular incorporation of stable isotope from nutrient into downstream metabolites. At multiple time points, after cells are rapidly switched from unlabeled to isotope-labeled nutrient, metabolism is quenched, metabolites are extracted and the extract is analyzed by chromatographymass spectrometry. Resulting plots of unlabeled compound versus time follow variants of exponential decay, with the flux equal to the decay rate multiplied by the intracellular metabolite concentration. Because labeling is typically fast (t 1/2 ≤5 min for central metabolites in Escherichia coli), variations on this approach can effectively probe dynamically changing metabolic fluxes. This protocol is exemplified using E. coli and nitrogen labeling, for which quantitative flux data for ~15 metabolites can be obtained over 3 d of work. Applications to adherent mammalian cells are also discussed.

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Section: Alternative Approachesmentioning

confidence: 99%

Kinetic flux profiling for quantitation of cellular metabolic fluxes

2008

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“…Interestingly, flux-based metabolic modeling of plant systems has recently gained more attention following its tremendous success in elucidating the metabolic capabilities of myriad microbial and mammalian species and rationally engineering them to achieve desirable phenotypes (Edwards et al, 2002;Lee et al, 2005;Lewis et al, 2012). In this regard, metabolic network models for several plants, such as Arabidopsis (Arabidopsis thaliana; Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010a;Saha et al, 2011;Chung et al, 2012;Mintz-Oron et al, 2012), barley (Hordeum vulgare;Grafahrend-Belau et al, 2009), rapeseed (Brassica napus;Hay and Schwender, 2011;Pilalis et al, 2011), maize (Zea mays; Saha et al, 2011), and a general C 4 plant model (de Oliveira Dal'Molin et al, 2010b) have already been developed, and some of them are even in genome scale.…”

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confidence: 99%

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

et al. 2013

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Rice (Oryza sativa) is one of the major food crops in world agriculture, especially in Asia. However, the possibility of subsequent occurrence of flood and drought is a major constraint to its production. Thus, the unique behavior of rice toward flooding and drought stresses has required special attention to understand its metabolic adaptations. However, despite several decades of research investigations, the cellular metabolism of rice remains largely unclear. In this study, in order to elucidate the physiological characteristics in response to such abiotic stresses, we reconstructed what is to our knowledge the first metabolic/regulatory network model of rice, representing two tissue types: germinating seeds and photorespiring leaves. The phenotypic behavior and metabolic states simulated by the model are highly consistent with our suspension culture experiments as well as previous reports. The in silico simulation results of seed-derived rice cells indicated (1) the characteristic metabolic utilization of glycolysis and ethanolic fermentation based on oxygen availability and (2) the efficient sucrose breakdown through sucrose synthase instead of invertase. Similarly, flux analysis on photorespiring leaf cells elucidated the crucial role of plastid-cytosol and mitochondrion-cytosol malate transporters in recycling the ammonia liberated during photorespiration and in exporting the excess redox cofactors, respectively. The model simulations also unraveled the essential role of mitochondrial respiration during drought stress. In the future, the combination of experimental and in silico analyses can serve as a promising approach to understand the complex metabolism of rice and potentially help in identifying engineering targets for improving its productivity as well as enabling stress tolerance.

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“…Although several studies have explored the evolution and organization of the metabolic network, most of them have predominantly studied either the large-scale structure (20) of the network, e.g., degree and flux distribution (21)(22)(23)(24) or mean clustering coefficient (21) or small motifs of 2-5 genes (8, 9, 11). Our focus, in contrast, is on the multiscale nature of the relationships between the metabolic network and genomic associations and, particularly, on the modules of 5-30 enzymes, similar to our study of the network of protein-protein interactions (25).…”

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confidence: 99%

A metabolic network in the evolutionary context: Multiscale structure and modularity

Spirin

Gelfand

Mironov

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

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The enormous complexity of biological networks has led to the suggestion that networks are built of modules that perform particular functions and are ''reused'' in evolution in a manner similar to reusable domains in protein structures or modules of electronic circuits. Analysis of known biological networks has revealed several modules, many of which have transparent biological functions. However, it remains to be shown that identified structural modules constitute evolutionary building blocks, independent and easily interchangeable units. An alternative possibility is that evolutionary modules do not match structural modules. To investigate the structure of evolutionary modules and their relationship to functional ones, we integrated a metabolic network with evolutionary associations between genes inferred from comparative genomics. The resulting metabolic-genomic network places metabolic pathways into evolutionary and genomic context, thereby revealing previously unknown components and modules. We analyzed the integrated metabolic-genomic network on three levels: macro-, meso-, and microscale. The macroscale level demonstrates strong associations between neighboring enzymes and between enzymes that are distant on the network but belong to the same linear pathway. At the mesoscale level, we identified evolutionary metabolic modules and compared them with traditional metabolic pathways. Although, in some cases, there is almost exact correspondence, some pathways are split into independent modules. On the microscale level, we observed high association of enzyme subunits and weak association of isoenzymes independently catalyzing the same reaction. This study shows that evolutionary modules, rather than pathways, may be thought of as regulatory and functional units in bacterial genomes.clustering ͉ evolution ͉ modules R ecent studies of biological networks have revealed structural modules and ubiquitous motifs, many of which have transparent biological functions. However, it remains to be shown that identified structural modules constitute evolutionary building blocks, independent and easily interchangeable units. An alternative possibility is that evolutionary modules do not match structural modules. Comparative genomics and analysis of biological networks provide tools to address this question. Here, we study one of the most accurately assembled networks, the metabolic network of Escherichia coli. To reveal evolutionary modules, we integrate metabolic network with evolutionary associations between genes inferred by comparative genomics of multiple bacterial species. Two genes are associated if (i) they have conserved proximity in distantly related genomes; and͞or (ii) demonstrate co-occurrence (i.e., both present or both absent) in most genomes; and͞or (iii) have been found fused together. The frequency of these events provides a measure of evolutionary association between the genes. We combine this measure with the structure of the metabolic network to identify evolutionary modules as regions of the network tha...

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Metabolic modelling of microbes: the flux‐balance approach

Cited by 334 publications

References 81 publications

Kinetic flux profiling for quantitation of cellular metabolic fluxes

Kinetic flux profiling for quantitation of cellular metabolic fluxes

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

A metabolic network in the evolutionary context: Multiscale structure and modularity

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