Metabolic dynamics in cells viewed as multilayered, distributed, mass‐energy‐information networks

Aon, Miguel A.; Cortassa, Sonia

doi:10.1002/047001153x.g308206

Cited by 4 publications

(6 citation statements)

References 59 publications

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“…The in phase behaviour of the TMRM fluorophore and the NAD(P)H autofluorescence signals is in agreement with TCA cycle and respiration‐driven proton pumps recharging of ΔΨm and reduction of the NAD(P)H pool after its oxidation due to ΔΨm depolarisation. Very recent data show that cardiac mitochondria behave collectively as a network of coupled oscillators whose power spectrum is characterized by a large number of frequencies in time scales spanning at least three orders of magnitude (from ms to a few min) [25,26].…”

Section: Discussionmentioning

confidence: 99%

Single and cell population respiratory oscillations in yeast: A 2‐photon scanning laser microscopy study

et al. 2006

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Two-photon scanning laser and confocal microscopies were used to image metabolic dynamics of single or cell populations of Saccharomyces cerevisiae strain 28033. Autofluorescence of reduced nicotinamide nucleotides, and mitochondrial membrane potential (DWm), were simultaneously monitored. Spontaneous, large-scale synchronized oscillations of NAD(P)H and DWm throughout the entire population of yeasts occurred under perfusion with aerated buffer in a continuous single-layered film of organisms. These oscillations stopped in the absence of perfusion and the intracellular NAD(P)H pool became reduced. Individual mitochondria within a single yeast also showed in-phase synchronous responses with the cell population, in both tetramethylrhodamine ethyl ester (or tetramethylrhodamine methyl ester) and autofluorescence. A single, localized, laser flash also triggered mitochondrial oscillations in single cells suggesting that the mitochondrion may behave as an autonomous oscillator. We conclude that spontaneous oscillations of S. cerevisiae mitochondrial redox states and DWm occur within individual yeasts, and synchrony of populations of organisms indicates the operation of an efficient system of cell-cell interaction to produce concerted metabolic multicellular behaviour on the minute time scale in both cases.

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Section: Discussionmentioning

confidence: 99%

Single and cell population respiratory oscillations in yeast: A 2‐photon scanning laser microscopy study

et al. 2006

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“…to ANT and ATPase), associated with a high NADH/NAD + ratio (reduced redox potential). In this context, it is instructive and useful to emphasize the differences in the meaning of the terms control and regulation, a key conceptual advance contributed by MCA [1,7]. The definitions we use here are somewhat different from that proposed in [25].…”

Section: Control and Regulation Of Oxidative Phosphorylation And Atp mentioning

confidence: 99%

“…In the midst of a transition between analytical and integrative periods in biology, cell physiology is moving from the unraveling of most metabolic pathways, along with their constituent enzymes, towards quantification of integrated networks of reactions [1]. In this new direction, Systems Bioenergetics focuses on metabolism, not only as a mesh of biochemical reactions, but also as an evolving, dynamic, and spatially organized, mass-energy-information network [2].…”

Section: Introductionmentioning

confidence: 99%

Control and Regulation of Integrated Mitochondrial Function in Metabolic and Transport Networks

Cortassa

O’Rourke

Winslow³

et al. 2009

IJMS

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Abstract:The pattern of flux and concentration control coefficients in an integrated mitochondrial energetics model is examined by applying a generalized matrix method of control analysis to calculate control coefficients, as well as response coefficients The computational model of Cortassa et al. encompasses oxidative phosphorylation, the TCA cycle, and Ca 2+ dynamics. Control of ATP synthesis, TCA cycle, and ANT fluxes were found to be distributed among various mitochondrial processes. Control is shared by processes associated with ATP/ADP production and transport, as well as by Ca 2+ dynamics. The calculation also analyzed the control of the concentrations of key regulatory ions and metabolites (Ca 2+ , NADH, ADP). The approach we have used demonstrates how properties of integrated systems may be understood through applications of computational modeling and control analysis.

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“…The emergence of “Systems Biology” signals the recognition of an integrative era in Biology, by contrast to the analytical period of biochemistry, cellular, and molecular biology (1–4). Systems biology encompasses a series of integrative approaches attempting to describe extensive changes in gene expression at the RNA, protein, or in the metabolite cellular makeup.…”

Section: Introductionmentioning

confidence: 99%

“…The following set of criteria specify the reliability of a computational model (1) sound physicobiochemical basis, (2) ability to reproduce qualitative and quantitative experimental data, (3) provision of meaningful explanation of the simulated experimental behavior, and (4) predictive power (4). Along the build-up and testing of our models we emphasize how we account for these criteria of reliability.…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling of Mitochondrial Function

Cortassa

Aon

2011

Mitochondrial Bioenergetics

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The advent of techniques with the ability to scan massive changes in cellular makeup (genomics, proteomics, etc.) has revealed the compelling need for analytical methods to interpret and make sense of those changes. Computational models built on sound physico-chemical mechanistic basis are unavoidable at the time of integrating, interpreting, and simulating high-throughput experimental data. Another powerful role of computational models is predicting new behavior provided they are adequately validated. Mitochondrial energy transduction has been traditionally studied with thermodynamic models. More recently, kinetic or thermo-kinetic models have been proposed, leading the path toward an understanding of the control and regulation of mitochondrial energy metabolism and its interaction with cytoplasmic and other compartments. In this work, we outline the methods, step-by-step, that should be followed to build a computational model of mitochondrial energetics in isolation or integrated to a network of cellular processes. Depending on the question addressed by the modeler, the methodology explained herein can be applied with different levels of detail, from the mitochondrial energy producing machinery in a network of cellular processes to the dynamics of a single enzyme during its catalytic cycle.

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Metabolic dynamics in cells viewed as multilayered, distributed, mass‐energy‐information networks

Cited by 4 publications

References 59 publications

Single and cell population respiratory oscillations in yeast: A 2‐photon scanning laser microscopy study

Single and cell population respiratory oscillations in yeast: A 2‐photon scanning laser microscopy study

Control and Regulation of Integrated Mitochondrial Function in Metabolic and Transport Networks

Computational Modeling of Mitochondrial Function

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