INCA: a computational platform for isotopically non-stationary metabolic flux analysis

Young, Jamey D.

doi:10.1093/bioinformatics/btu015

Cited by 335 publications

(313 citation statements)

References 14 publications

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“…Glucose isotopomer distribution in arterial plasma was determined in the Vanderbilt Hormone Assay and Analytical Services Core using Agilent 5977A MSD GC/MS (Agilent Technologies) according to the method of Antoniewicz et al (54) and analyzed using isotopomer computational analysis software (55). Glucose fluxes were assessed using nonsteady-state equations (volume of distribution of glucose= 130 ml/kg) (56,57).…”

Section: Discussionmentioning

confidence: 99%

Hepatic Gi signaling regulates whole-body glucose homeostasis

Rossi¹,

Zhu²,

McMillin³

et al. 2018

Journal of Clinical Investigation

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Section: Discussionmentioning

confidence: 99%

Hepatic Gi signaling regulates whole-body glucose homeostasis

Rossi¹,

Zhu²,

McMillin³

et al. 2018

Journal of Clinical Investigation

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“…Non-stationary 13 C-MFA was performed using the publicly available software package INCA [40], which implements the elementary metabolite units framework [41]. Briefly, fluxes are estimated by minimizing the variance-weighted sum of squared residuals (SSR) between experimental measurements and model predictions using least-squares regression.…”

Section: Metabolic Flux Determinationmentioning

confidence: 99%

Quantification of Metabolic Rearrangements During Neural Stem Cells Differentiation into Astrocytes by Metabolic Flux Analysis

et al. 2016

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Proliferation and differentiation of neural stem cells (NSCs) have a crucial role to ensure neurogenesis and gliogenesis in the mammalian brain throughout life. As there is growing evidence for the significance of metabolism in regulating cell fate, knowledge on the metabolic programs in NSCs and how they evolve during differentiation into somatic cells may provide novel therapeutic approaches to address brain diseases. In this work, we applied a quantitative analysis to assess how the central carbon metabolism evolves upon differentiation of NSCs into astrocytes. Murine embryonic stem cell (mESC)-derived NSCs and astrocytes were incubated with labelled [1-13 C]glucose and the label incorporation into intracellular metabolites was followed by GC-MS. The obtained 13 C labelling patterns, together with uptake/secretion rates determined from supernatant analysis, were integrated into an isotopic non-stationary metabolic flux analysis ( 13 C-MFA) model to estimate intracellular flux maps. Significant metabolic differences between NSCs and astrocytes were identified, with a general downregulation of central carbon metabolism during astrocytic differentiation. While glucose uptake was 1.7-fold higher in NSCs (on a per cell basis), a high lactate-secreting phenotype was common to both cell types. Furthermore, NSCs consumed glutamine from the medium; the highly active reductive carboxylation of alpha-ketoglutarate indicates that this was converted to citrate and used for biosynthetic purposes. In astrocytes, pyruvate entered the TCA cycle mostly through pyruvate carboxylase (81%). This pathway supported glutamine and citrate secretion, recapitulating well described metabolic features of these cells in vivo. Overall, this fluxomics study allowed us to quantify the metabolic rewiring accompanying astrocytic lineage specification from NSCs.

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“…Recently, the technique has been implemented successfully in Arabidopsis (Arabidopsis thaliana), a technical and computational tour de force that allowed the quantification of 54 fluxes in illuminated leaves labeled with 13 CO 2 (Ma et al, 2014). The availability of specialized software, INCA (Young, 2014) and OpenMeBius (Kajihata et al, 2014), will greatly facilitate the implementation of INST-MFA by automatically generating the system of ordinary differential equations that describes the metabolic network under consideration (defined by the user and including information about the carbon transitions between metabolites) and estimating metabolic fluxes by nonlinear optimization of the parameter fit to the labeling time course of the measured isotopomers. The inclusion of metabolite pool size data may improve the accuracy of flux estimates, especially at branch points (Heise et al, 2015).…”

Section: General Principles Of Inference and The Prediction Of Metabomentioning

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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INCA: a computational platform for isotopically non-stationary metabolic flux analysis

Cited by 335 publications

References 14 publications

Hepatic Gi signaling regulates whole-body glucose homeostasis

Hepatic Gi signaling regulates whole-body glucose homeostasis

Quantification of Metabolic Rearrangements During Neural Stem Cells Differentiation into Astrocytes by Metabolic Flux Analysis

Inference and prediction of metabolic network fluxes

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