MitoCore: A curated constraint-based model for simulating human central metabolism

Smith, Anthony C.; Eyassu, Filmon; Mazat, Jean‐Pierre; Robinson, Alan J.

doi:10.1101/141101

Cited by 11 publications

(27 citation statements)

References 37 publications

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“…A second characteristic of this model is to consider mitochondria as an isolated compartment with the relevant transporters. This led us to the third characteristics, to develop a relevant model of oxidative phosphorylation taking into account the mitochondrial ΔµH + in the form of pseudo substrates DPH and DPSI as was already done in MitoCore [11] and in [2]. The last characteristic of C2M2N is that it takes into account nitrogen metabolism in addition to carbon metabolism.…”

Section: Resultsmentioning

confidence: 99%

“…MitoCore [11] is a manually curated constraint-based computer model of human metabolism that incorporates 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for an easier interpretation of results. The representation of the proton gradient is nearly the same as in C2M2N with the pseudo substrates PMF (Proton Motive Force) with DPH = 0.18 PMF and DPSI = 0.82 PMF.…”

Section: Mitocore Modelmentioning

confidence: 99%

“…77 reactions among 37 are irreversible and 54 internal metabolites are taken into account. Mitochondrial metabolism (OXPHOS) and mitochondrial transports, are often inaccurately represented in GEM with some exceptions [2,11]. In C2M2N, the model of OXPHOS functioning is based on a proton gradient through the inner mitochondrial membrane, represented here by two pseudo-metabolites DPH (∆pH) and DPSI (∆ψ).…”

Section: Introductionmentioning

confidence: 99%

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The Fate of Glutamine in Human Metabolism. The interplay with glucose in proliferating cells

Mazat

Ransac

2018

Preprint

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Genome-scale models of metabolism (GEM) are used to study how metabolism varies in different physiological conditions. However, the great number of reactions involved in GEM makes it difficult to understand these variations. In order to have a more understandable tool, we developed a reduced metabolic model of central carbon and nitrogen metabolism, C2M2N with 77 reactions, 54 internal metabolites and 3 compartments, taking into account the actual stoichiometry of the reactions, including the stoichiometric role of the cofactors and the irreversibility of some reactions. In order to model OXPHOS functioning, the proton gradient through the inner mitochondrial membrane is represented by two pseudo-metabolites DPH (∆pH) and DPSI (∆ψ).To illustrate the interest of such a reduced and quantitative model of metabolism in mammalian cells, we used Flux Balance Analysis (FBA), to study all the possible fates of glutamine in metabolism. Our analysis shows that glutamine can supply carbon sources for cell energy production and can be used as carbon and nitrogen sources to synthesize essential metabolites. Finally, we studied the interplay between glucose and glutamine for the formation of cell biomass according to ammonia microenvironment. We then propose a quantitative analysis of the Warburg effect.

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Section: Resultsmentioning

confidence: 99%

Section: Mitocore Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Fate of Glutamine in Human Metabolism. The interplay with glucose in proliferating cells

Mazat

Ransac

2018

Preprint

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“…(B) A qualitative validation of iCardio using ATP yields for a variety of common carbon sources. The ATP yields for iCardio (y-axis) are compared to another recently published, but smaller, metabolic model, MitoCore (Smith et al, 2017), which contains 324 reactions. The agreement between the models demonstrates the lack of energy generating cycles and infinite loops in iCardio.…”

Section: Figure 2 Validating Icardio (A) Descriptively Using the Nummentioning

confidence: 99%

“…To provide a quantitative validation of iCardio, ATP yields were predicted for a number of carbon substrates and amino acids and compared to another metabolic model, MitoCore (Smith et al, 2017). The MitoCore model was chosen for comparison because of its focus on cardiomyocyte mitochondrial metabolism.…”

Section: Figure 2 Validating Icardio (A) Descriptively Using the Nummentioning

confidence: 99%

Identifying functional metabolic shifts in heart failure with the integration of omics data and a cardiomyocyte-specific, genome-scale model

Dougherty

Rawls

Kolling

et al. 2020

Preprint

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SummaryThe heart is a metabolic omnivore, known to consume many different carbon substrates in order to maintain function. In diseased states, the heart’s metabolism can shift between different carbon substrates; however, there is some disagreement in the field as to the metabolic shifts seen in end-stage heart failure and whether all heart failure converges to a common metabolic phenotype. Here, we present a new, validated cardiomyocyte-specific GEnome-scale metabolic Network REconstruction (GENRE), iCardio, and use the model to identify common shifts in metabolic functions across heart failure omics datasets. We demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying Tasks Inferred from Differential Expression (TIDEs) which represent metabolic functions associated with changes in gene expression. We identify decreased NO and Neu5Ac synthesis as common metabolic markers of heart failure across datasets. Further, we highlight the differences in metabolic functions seen across studies, further highlighting the complexity of heart failure. The methods presented for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissue and diseases of interest.

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Closing the therapeutic loop

Campbell

Yengo

Lee

et al. 2019

Archives of Biochemistry and Biophysics

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One of our clinical colleagues once told us, "There are PhD problems, and there are important problems. You should work on important problems." We reject our colleague's blunt criticism of basic science but we recognize that, as fields advance, researchers can do more to increase the tangible impact of their work. It seems to us that the field of myocardial contraction has reached this point. Decades of work by thousands of scientists has provided us with detailed information about the molecular, cellular, and tissue-level mechanisms that drive and regulate ventricular function. In our opinion, it is time for researchers to put more emphasis on leveraging that knowledge to improve patient care. This article outlines one potential approach based on multiscale computer modeling of cardiac function. Our vision is a transdisciplinary team that optimizes treatment plans for patients who have cardiac disease by predicting how the heart will respond to each of the potential therapeutic options using personalized computer models that integrate genomic, proteomic, imaging, and functional data. Over the next few years, we want to work towards that moonshot goal by planning a clinical trial that tests whether implementing model-predicted therapies helps patients more than the current standard of care.

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MitoCore: A curated constraint-based model for simulating human central metabolism

Abstract: Background: The complexity of metabolic networks can make the origin and impact of

Cited by 11 publications

References 37 publications

The Fate of Glutamine in Human Metabolism. The interplay with glucose in proliferating cells

The Fate of Glutamine in Human Metabolism. The interplay with glucose in proliferating cells

Identifying functional metabolic shifts in heart failure with the integration of omics data and a cardiomyocyte-specific, genome-scale model

Closing the therapeutic loop

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