Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE

Wang, Yuliang; Eddy, James A.; Price, Nathan D.

doi:10.1186/1752-0509-6-153

Cited by 256 publications

(297 citation statements)

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“…Several sophisticated techniques have been developed to constrain metabolic models using experimental data. Transcriptional microarray data, for example, has been used to build integrated metabolic and regulatory models to study cells in differing states of gene regulation (15)(16)(17). As systems-level models become more complete, an increasingly large amount of experimental data is required to parameterize them.…”

mentioning

confidence: 99%

Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population

Labhsetwar

Cole²,

Roberts

et al. 2013

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

115

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The authors note, "For the 'hybrid' location discrimination task, we report data obtained from 27 electrodes, 16 of which were in area 1; the 11 electrodes in area 3b were divided evenly across the two animals (6 and 5). We had previously tested all of the electrodes, including those in area 3b, in the detection and discrimination tasks (as shown in Fig. 3) and found them all to yield approximately equivalent performance (see Fig 3A). We noticed in the hybrid location discrimination task, however, that one of the animals performed much more poorly based on stimulation of area 3b than it did based on stimulation of area 1 (while the other animal performed better based on stimulation of area 1). Having no reason to question any of the arrays, we attributed this discrepancy to differences across animals and arrived at the conclusion, based on pooled data from both animals, that stimulation of the two areas yields equivalent performance in the 'hybrid location discrimination' task. The overall conclusion, then, was that stimulation of neurons in area 3b and 1 evokes percepts that are equally localized on the skin."Shortly after publication of the paper, we repeated detection experiments across the arrays and found that the animal could no longer detect stimulation through the array in area 3b that had yielded poor performance in the hybrid location discrimination task. It is therefore likely that this array had failed between the time we conducted the initial detection and discrimination experiments and the time we conducted the hybrid location discrimination task (which required 2-3 months of retraining). If this is the case, and we eliminate data from that bad array, then the median performance on hybrid trials is 83% (up from the 80% that was originally reported), which is still statistically poorer than that on the location-matched mechanical trials [median difference between performance on mechanical and hybrid trials was 3.3% rather than 5.6%, t (119) = 6.1, P < 0.001] (see the corrected Fig. 2). Thus, we probably underestimated overall performance on hybrid trials, and thus the degree to which artificial percepts are localized, in the original publication. Importantly, however, performance on hybrid trials based on stimulation of area 3b was significantly better than performance based on stimulation of area 1 [median Δp = 0.028 and 0.054 for areas 3b and 1, respectively; t test: t (76) = 2.8, P < 0.01]. Thus, based on the data obtained from only one animal, it seems as though stimulation of area 3b elicits more localized percepts than does stimulation of area 1, as might be expected given that neurons in area 3b tend to have smaller receptive fields than their counterparts in area 1 (1, 2)."As a result of this error, Fig. 2 and its legend appeared incorrectly. The corrected figure and its corresponding legend appear below. On both mechanical and hybrid trials, the relative locations of stimuli applied to widely spaced digits were more accurately discriminated than were the relative locations of stimuli applie...

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mentioning

confidence: 99%

Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population

Labhsetwar

Cole²,

Roberts

et al. 2013

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

115

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“…biochemical pathways) that must carry nonzero flux can be added as further constraints. The third group, composed of MBA (Jerby et al, 2010), mCADRE (Wang et al, 2012), and FastCORE (Vlassis et al, 2014), first define a core set of reactions, classified as active in a given context according to experimental data, and then find the minimum set of reactions outside the core required to satisfy the model consistency condition (i.e. all reactions in the model must be able to carry a nonzero flux in at least one of the allowed steady-state distributions).…”

Section: Building and Analyzing Context-specific Metabolic Modelsmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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“…OptStrain [67], OptReg [68], OptForce [72], k-OptForce [16], OptORF [44], CosMos [20] Omics data integration Transcriptome GIMME [5], iMAT [82], GIM 3 E [76], E-Flux [18], PROM [13], MADE [38], tFBA [90], RELATCH [45], TEAM [19], AdaM [89], GX-FBA [60], mCADRE [92], FCGs [43], EXAMO [75], TIGER [37] Proteome GIMMEp [6] Pathway prediction BNICE [29], Cho et al [14], RetroPath [11], PathPred [59], DESHARKY [74], BioPath [94], XTMS [12], GEM-Path [56] phenotype and gene essentiality [24]. Even further, taking advantage of a large set of genome sequences available for various E. coli strains, the GEMs for 55 E. coli strains were used to investigate the variations in gene, reaction and metabolite contents, and the capabilities to adapt to different nutritional environments among the strains [40].…”

Section: Genome-scale Metabolic Networkmentioning

confidence: 99%

Applications of genome-scale metabolic network model in metabolic engineering

Kim

et al. 2015

Journal of Industrial Microbiology and Biotechnology

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Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

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Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE

Cited by 256 publications

References 58 publications

Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population

Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population

Inference and prediction of metabolic network fluxes

Applications of genome-scale metabolic network model in metabolic engineering

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