Dataset Information

Bier2000_GlycolyticOscillation

ABSTRACT: This a model from the article: How yeast cells synchronize their glycolytic oscillations: a perturbation analytic treatment Bier M, Bakker BM, Westerhoff HV. Biophys. J2000 Mar;78(3):1087-93. 10692299, Abstract: Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating metabolite at the bottom of the anaerobic part of the glycolytic chain. Here we address the issue of how a metabolite remote from the pacemaking origin of the oscillation may nevertheless control the synchronization. We present a quantitative model for glycolytic oscillations and their synchronization in terms of chemical kinetics. We show that, in essence, the common acetaldehyde concentration can be modeled as a small perturbation on the "pacemaker" whose effect on the period of the oscillations of cells in the same suspension is indeed such that a synchronization develops.

SUBMITTER: Vijayalakshmi Chelliah

PROVIDER: BIOMD0000000254 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000000254?filename=BIOMD0000000254-biopax2.owl	Owl
	BIOMD0000000254?filename=BIOMD0000000254-biopax3.owl	Owl
	BIOMD0000000254?filename=BIOMD0000000254.m	Other
	BIOMD0000000254?filename=BIOMD0000000254.pdf	Pdf
	BIOMD0000000254?filename=BIOMD0000000254.png	Other

Items per page:

1 - 5 of 11

Publications

How yeast cells synchronize their glycolytic oscillations: a perturbation analytic treatment.

Bier M M Bakker B M BM Westerhoff H V HV

Biophysical journal 20000301 3

Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating me ...[more]

PMID: 10692299

Similar Datasets

Project description:Wolf2000 - Cellular interaction on glycolytic oscillations in yeast A two-cell model of glycolysis. This model is described in the article: Effect of cellular interaction on glycolytic oscillations in yeast: a theoretical investigation. Wolf J, Heinrich R. Biochem. J. 2000 Jan; 345 Pt 2: 321-334 Abstract: On the basis of a detailed model of yeast glycolysis, the effect of intercellular dynamics is analysed theoretically. The model includes the main steps of anaerobic glycolysis, and the production of ethanol and glycerol. Transmembrane diffusion of acetaldehyde is included, since it has been hypothesized that this substance mediates the interaction. Depending on the kinetic parameter, the single-cell model shows both stationary and oscillatory behaviour. This agrees with experimental data with respect to metabolite concentrations and phase shifts. The inclusion of intercellular coupling leads to a variety of dynamical modes, such as synchronous oscillations, and different kinds of asynchronous behavior. These oscillations can co-exist, leading to bi- and tri-rhythmicity. The corresponding parameter regions have been identified by a bifurcation analysis. The oscillatory dynamics of synchronized cell populations are investigated by calculating the phase responses to acetaldehyde pulses. Simulations are performed with respect to the synchronization of two subpopulations that are oscillating out of phase before mixing. The effect of the various process on synchronization is characterized quantitatively. While continuous exchange of acetaldehyde might synchronize the oscillations for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization. for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization. This model is hosted on BioModels Database and identified by: BIOMD0000000691. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:As a result of ancestral whole genome and small-scale duplication events, the genome of Saccharomyces cerevisiaeM-bM-^@M-^Ys, and of many eukaryotes, still contain a substantial fraction of duplicated genes. In all investigated organisms, metabolic pathways, and more particularly glycolysis, are specifically enriched for functionally redundant paralogs. In ancestors of the Saccharomyces lineage, the duplication of glycolytic genes is purported to have played an important role leading to S. cerevisiae current lifestyle favoring fermentative metabolism even in the presence of oxygen and characterized by a high glycolytic capacity. In modern S. cerevisiae, the 12 glycolytic reactions leading to the biochemical conversion from glucose to ethanol are encoded by 27 paralogs. In order to experimentally explore the physiological role of this genetic redundancy, a yeast strain with a minimal set of 14 paralogs was constructed (MG strain). Remarkably, a combination of quantitative, systems approach and of semi-quantitative analysis in a wide array of growth environments revealed the absence of phenotypic response to the cumulative deletion of 13 glycolytic paralogs. This observation indicates that duplication of glycolytic genes is not a prerequisite for achieving the high glycolytic fluxes and fermentative capacities that are characteristic for S. cerevisiae and essential for many of its industrial applications and argues against gene dosage effects as a means for fixing minor glycolytic paralogs in the yeast genome. MG was carefully designed and constructed to provide a robust, prototrophic platform for quantitative studies, and is made available to the scientific community. The goals of the present study are to experimentally explore genetic redundancy in yeast glycolysis by cumulative deletion of minor paralogs and to provide a new experimental platform for fundamental yeast research by constructing a yeast strain with a functional M-bM-^@M-^Xminimal glycolysisM-bM-^@M-^Y. To this end, we deleted 13 minor paralogs, leaving only the 14 major paralogs for the S. cerevisiae glycolytic pathway. The cumulative impact of deleting all minor paralogs was investigated by two complementary approaches. A first, quantitative analysis focused on the impact on glycolytic flux under a number of controlled cultivation conditions that, in wild-type strains, result in different glycolytic fluxes. These quantitative growth studies were combined with transcriptome, enzyme-activity and intracellular metabolite assays to capture potential small phenotypic effects. A second, semi-quantitative characterization explored the phenotype of the M-bM-^@M-^Xminimal glycolysisM-bM-^@M-^Y strain under a wide array of experimental conditions to identify potential context-dependent phenotypes

Project description:Cytosolic acetyl-coenzyme A is a precursor for many biotechnologically relevant compounds produced by Saccharomyces cerevisiae. In this yeast, cytosolic acetyl-CoA synthesis and growth strictly depend on expression of either the Acs1 or Acs2 isoenzyme of acetyl-CoA synthetase (ACS). Since hydrolysis of ATP to AMP and pyrophosphate in the ACS reaction constrains maximum yields of acetyl-CoA-derived products, this study explores replacement of ACS by two ATP-independent pathways for acetyl-CoA synthesis. After evaluating expression of different bacterial genes encoding acetylating acetaldehyde dehydrogenase (A-ALD) and pyruvate-formate lyase (PFL), acs1M-NM-^T acs2M-NM-^T S. cerevisiae strains were constructed in which A-ALD or PFL successfully replaced ACS. In A-ALD-dependent strains, aerobic growth rates of up to 0.27 h-1 were observed, while anaerobic growth rates of PFL-dependent S. cerevisiae (0.21 h-1) were stoichiometrically coupled to formate production. In glucose-limited chemostat cultures, intracellular metabolite analysis did not reveal major differences between A-ALD-dependent and reference strains. However, biomass yields on glucose of A-ALD- and PFL-dependent strains were lower than those of the reference strain. Transcriptome analysis suggested that reduced biomass yields were caused by acetaldehyde and formate in A-ALD- and PFL-dependent strains, respectively. Transcript profiles also indicated that a previously proposed role of Acs2 in histone acetylation is probably linked to cytosolic acetyl-CoA levels rather than to direct involvement of Acs2 in histone acetylation. While, for the first time, demonstrating that yeast ACS can be fully replaced by alternative reactions, this study demonstrates that further modifications are needed to achieve optimal in vivo efficiencies of the supply of acetyl-CoA as product precursor. To investigate the impact of introduced changes in native pathway of cytosolic acetyl-CoA formation in S. cerevisiae, a DNA microarray-based transcriptome analysis was performed on aerobic or anaerobic, glucose-limited chemostat cultures.

Project description:Jiang2007 - GSIS system, Pancreatic Beta Cells Description of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells. This model is described in the article: A kinetic core model of the glucose-stimulated insulin secretion network of pancreatic beta cells. Jiang N, Cox RD, Hancock JM. Mamm Genome 2007 Jul; 18(6-7):508-20. Abstract: The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic beta cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state. The model reproduces Figure 2 of the paper, and is built using files 'ModelNNT11.xml' and 'changed.m' available from http://www.har.mrc.ac.uk/research/bioinformatics/research_areas/systems_biology.html . A couple of small errors in the model (in the original SBML file 'ModelNNT11.xml') have been corrected. The errors are: v44 now produces Pyr rather than PYR the kinetic law of v27 is now dependent on cytoplasmic (rather than mitochondrial) acetyl CoA and OXA This model is hosted on BioModels Database and identified by: BIOMD0000000239 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.