Temporal Partitioning of the Yeast Cellular Network

Murray, Douglas B.; Amariei, Cornelia; Sasidharan, Kalesh; Machné, Rainer; Aon, Miguel A.; Lloyd, David

doi:10.1007/978-3-642-38505-6_12

Cited by 14 publications

(13 citation statements)

References 84 publications

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“…Inhibiting mitochondrial respiration at the level of cytochrome oxidase with hydrogen sulfide (H2S) abated all oscillatory frequencies including the 40 min-period ultradian clock. This result provided proof of principle in support of the idea that multi-scale timekeeping is an emergent property of the overall network involved in metabolism, growth and proliferation of yeast [44,60]. This finding also poses the interesting possibility that, in principle, multi-rhythmic processes could be modulated over the whole frequency range.…”

Section: Prediction Control and Modulation Of Complex Dynamic Behaviorsupporting

confidence: 68%

“…The full power of systems biology approaches can be unveiled by the use of quantitative methods of model validation and their ability to predict and analyze complex dynamic behavior, e.g., rhythms [42][43][44]. The systole-diastole cycle of heart dynamics [10,45], nutrition and circadian dynamics [46], plant cell growth and differentiation [47] or microbial transitions from simple to mixed substrate utilization [48,49] are relevant examples from the biomedical, plant biology and microbial physiology fields.…”

Section: Prediction Control and Modulation Of Complex Dynamic Behaviormentioning

confidence: 99%

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Systems Biology of the Fluxome

Aon

Cortassa

2015

Processes

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Abstract:The advent of high throughput -omics has made the accumulation of comprehensive data sets possible, consisting of changes in genes, transcripts, proteins and metabolites. Systems biology-inspired computational methods for translating metabolomics data into fluxomics provide a direct functional, dynamic readout of metabolic networks. When combined with appropriate experimental design, these methods deliver insightful knowledge about cellular function under diverse conditions. The use of computational models accounting for detailed kinetics and regulatory mechanisms allow us to unravel the control and regulatory properties of the fluxome under steady and time-dependent behaviors. This approach extends the analysis of complex systems from description to prediction, including control of complex dynamic behavior ranging from biological rhythms to catastrophic lethal arrhythmias. The powerful quantitative metabolomics-fluxomics approach will help our ability to engineer unicellular and multicellular organisms evolve from trial-and-error to a more predictable process, and from cells to organ and organisms.

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Section: Prediction Control and Modulation Of Complex Dynamic Behaviorsupporting

confidence: 68%

Section: Prediction Control and Modulation Of Complex Dynamic Behaviormentioning

confidence: 99%

Systems Biology of the Fluxome

Aon

Cortassa

2015

Processes

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“…Further technical details are provided in publications cited in the reference section. 59,[128][129][130] In summary, temporal organization and compartmentation are heterarchically ordered on many timescales: i.e., network controls operate from the overall cell system downward as well as from the molecular levels upward. Oscillations and rhythms provide synchronization within and between these levels and determine the coherent operation of the whole, its maintenance, and survival.…”

Section: Further Analytical Refinements At the University Of Vienna Amentioning

confidence: 99%

“…The respiratory oscillation of a self-synchronized yeast culture. The black trace is that for residual dissolved O 2 on all panels 59. …”

mentioning

confidence: 99%

Temporal metabolic partitioning of the yeast and protist cellular networks: the cell is a global scale-invariant (fractal or self-similar) multioscillator

et al. 2018

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Britton Chance, electronics expert when a teenager, became an enthusiastic student of biological oscillations, passing on this enthusiasm to many students and colleagues, including one of us (DL). This historical essay traces BC's influence through the accumulated work of DL to DL's many collaborators. The overall temporal organization of mass-energy, information, and signaling networks in yeast in self-synchronized continuous cultures represents, until now, the most characterized example of in vivo elucidation of time structure. Continuous online monitoring of dissolved gases by direct measurement (membrane-inlet mass spectrometry, together with NAD(P)H and flavin fluorescence) gives strain-specific dynamic information from timescales of minutes to hours as does two-photon imaging. The predominantly oscillatory behavior of network components becomes evident, with spontaneously synchronized cellular respiration cycles between discrete periods of increased oxygen consumption (oxidative phase) and decreased oxygen consumption (reductive phase). This temperature-compensated ultradian clock provides coordination, linking temporally partitioned functions by direct feedback loops between the energetic and redox state of the cell and its growing ultrastructure. Multioscillatory outputs in dissolved gases with 13 h, 40 min, and 4 min periods gave statistical self-similarity in power spectral and relative dispersional analyses: i.e., complex nonlinear (chaotic) behavior and a functional scale-free (fractal) network operating simultaneously over several timescales.

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“…A key example of the association between rhythmic biological signals and networks can be found in mammals where crosstalk between the cell‐autonomous circadian core clock and metabolic transcription networks has been well documented, and in yeast, a unicellular eukaryote that in synchronized cultures exhibits genome‐wide oscillations of transcription factors, RNAs and metabolism …”

Section: Detection and Characterization Of Temporal Dynamicsmentioning

confidence: 99%

Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back

Kurz

Kembro

Flesia

et al. 2016

WIREs Mechanisms of Disease

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Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental-computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics-fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O and CO levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. WIREs Syst Biol Med 2017, 9:e1352. doi: 10.1002/wsbm.1352 For further resources related to this article, please visit the WIREs website.

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Temporal Partitioning of the Yeast Cellular Network

Cited by 14 publications

References 84 publications

Systems Biology of the Fluxome

Systems Biology of the Fluxome

Temporal metabolic partitioning of the yeast and protist cellular networks: the cell is a global scale-invariant (fractal or self-similar) multioscillator

Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back

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