Tyson2001_Cell_Cycle_Regulation
Ontology highlight
ABSTRACT:
SUBMITTER: 
Nicolas Le Novère
PROVIDER: BIOMD0000000195 | BioModels | 2009-02-26
REPOSITORIES: BioModels
Publications
Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions.
Journal of theoretical biology 20010501 2
In recent years, molecular biologists have uncovered a wealth of information about the proteins controlling cell growth and division in eukaryotes. The regulatory system is so complex that it defies understanding by verbal arguments alone. Quantitative tools are necessary to probe reliably into the details of cell cycle control. To this end, we convert hypothetical molecular mechanisms into sets of nonlinear ordinary differential equations and use standard analytical and numerical methods to stu ...[more]
Similar Datasets
Project description:This a model from the article:
Kinetic analysis of a molecular model of the budding yeast cell cycle.
Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. Mol Biol Cell
2000 Jan;11(1):369-91 10637314
,
Abstract:
The molecular machinery of cell cycle control is known in more detail for
budding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism.
In recent years, many elegant experiments on budding yeast have dissected the
roles of cyclin molecules (Cln1-3 and Clb1-6) in coordinating the events of DNA
synthesis, bud emergence, spindle formation, nuclear division, and cell
separation. These experimental clues suggest a mechanism for the principal
molecular interactions controlling cyclin synthesis and degradation. Using
standard techniques of biochemical kinetics, we convert the mechanism into a set
of differential equations, which describe the time courses of three major
classes of cyclin-dependent kinase activities. Model in hand, we examine the
molecular events controlling "Start" (the commitment step to a new round of
chromosome replication, bud formation, and mitosis) and "Finish" (the transition
from metaphase to anaphase, when sister chromatids are pulled apart and the bud
separates from the mother cell) in wild-type cells and 50 mutants. The model
accounts for many details of the physiology, biochemistry, and genetics of cell
cycle control in budding yeast.
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
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.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2019-08-22 | BIOMD0000000675 | BioModels
Project description:This a model from the article:
Mathematical model of the morphogenesis checkpoint in budding yeast.
Ciliberto A, Novak B, Tyson JJ. J Cell Biol
2003 Dec 22;163(6):1243-54 14691135
,
Abstract:
The morphogenesis checkpoint in budding yeast delays progression through the
cell cycle in response to stimuli that prevent bud formation. Central to the
checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts
cell cycle progression in G2. We propose a molecular network for Swe1 control,
based on published observations of budding yeast and analogous control signals
in fission yeast. The proposed Swe1 network is merged with a model of
cyclin-dependent kinase regulation, converted into a set of differential
equations and studied by numerical simulation. The simulations accurately
reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions,
the model attributes a new role to Hsl1, a kinase known to play a role in Swe1
degradation: Hsl1 must also be indirectly responsible for potent inhibition of
Swe1 activity. The model supports the idea that the morphogenesis checkpoint,
like other checkpoints, raises the cell size threshold for progression from one
phase of the cell cycle to the next.
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Ciliberto A, Novak B, Tyson JJ. (2003) - version03
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
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.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL0913285268 | BioModels
Project description:This model is decribed in the article:
Dilution and titration of cell-cycle regulators may control cell size in budding yeast
Frank S. Heldt, Reece Lunstone, John J. Tyson, Bela Novak
PLoS Comput Biol, October 2018, 14(10), e1006548, doi: 10.1371/journal.pcbi.1006548
Abstract:
The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the START transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cell’s overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect ‘sizer’ behaviour in G1 and a ‘timer’ in S/G2/M, which combine to yield an ‘adder’ over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.
2019-01-09 | MODEL1803220002 | BioModels
Project description:This model is decribed in the article:
Dilution and titration of cell-cycle regulators may control cell size in budding yeast
Frank S. Heldt, Reece Lunstone, John J. Tyson, Bela Novak
PLoS Comput Biol, October 2018, 14(10), e1006548, doi: 10.1371/journal.pcbi.1006548
Abstract:
The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the START transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cell’s overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect ‘sizer’ behaviour in G1 and a ‘timer’ in S/G2/M, which combine to yield an ‘adder’ over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.
2019-11-13 | BIOMD0000000856 | BioModels
Project description:This a model from the article:
Mathematical model of the morphogenesis checkpoint in budding yeast.
Ciliberto A, Novak B, Tyson JJ J. Cell Biol.
[2003 Dec; Volume: 163 (Issue: 6 )] Page info: 1243-54 14691135
,
Abstract:
The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next.
The model reproduces Fig 3 of the paper.
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use
.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2018-04-18 | BIOMD0000000297 | BioModels
Project description:Chen2004 - Cell Cycle Regulation
This is a hypothetical model of cell cycle that describes the molecular mechanism for regulating DNA synthesis, bud emergence, mitosis, and cell division in budding yeast.
This model is described in the article:
Integrative analysis of cell cycle control in budding yeast.
Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ
Mol. Biol. Cell. [2004 Aug; Volume: 15 (Issue: 8 )] Page info: 3841-62
Abstract:
The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.
The model reproduces the time profiles of the different species in Figure 2 of the paper. The figure depicts the cycle of a daughter cell. Since the Mass Doubling Time (MDT) is 90 minutes, time t=90 from the model simulation will correspond to time t=0 in the paper. The model was successfully tested using MathSBML and SBML odeSolver.
To create a valid SBML file, a local parameter k=1 was added in the reaction 'Inactivation_274_CDC20'. Also, in order to annotate the protein and to have the interaction in the reaction graph to match figure 1 of the article, the reaction rate constants k_{mad2}, k_{bub2} and k_{lte1} are considered as species and renamed as MAD2, BUB2 and LTE1 in the model.
This model is hosted on BioModels Database
and identified by: BIOMD0000000056
.
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.
2006-05-08 | BIOMD0000000056 | BioModels
Project description:cell cycle studies in the budding yeast Saccharomyces cerevisiae.
| MSV000084302 | MassIVE
Project description:Cell-cycle transcript dynamics from two species of wild-type budding yeast growing at 30 degrees Celsius in rich media: Saccharomyces cerevisiae (BF264-15D background) and Cryptococcus neoformans var. grubii (H99F background). We compared programs of cell-cycle-regulated genes between distantly related budding yeasts.
2016-12-07 | GSE80474 | GEO
Project description:EpRAS cells undergo EMT transition through hysteresis showing bistability of the system. The hysteresis transition is controlled by the negative feedback loop between miR-200s and Zeb1. Disruption of the feedback loop through the deletion of Z-box2 in the miR-200s cluster-2 promoter region changes the transition to unimodal without hysteresis. To determine the consequence of cells undergoing through hysteresis or unimodal transitions of EMT, herein we determined the transcriptomic profiles of EpRAS wild type and EpRAS-delZbox2 mutant cells, (designated as EpRAS-C2-2). To induce EMT, the cells were treated with 100pM TGFbeta for 3 days. Mock PBS treatment is used as control.
2018-11-08 | GSE106533 | GEO
Project description:A better understanding of the molecular mechanisms underlying the development and progression of diabetic neuropathy (DN) is essential for the design of mechanism-based therapies. We examined changes in global gene expression to define pathways regulated by diabetes in peripheral nerve. Microarray data for 24 week-old BKS db/db and db/+ mouse sciatic nerve were analyzed to define significantly differentially expressed genes (DEGs); DEGs were further used for functional enrichment analysis and network analysis to identify biological processes and pathways differentially regulated in the db/db nerve. Expression profile clustering was performed to identify co-expressed DEGs. A set of co-expressed lipid metabolism genes was used for promoter sequence analysis.We identified 4,017 DEGs; 2,122 genes were up-regulated and 1,895 genes were down-regulated in the db/db relative to the db+ samples. Over-represented biological processes identified by the functional enrichment analysis include cell cycle, lipid metabolic process, lipid transport, carbohydrate metabolic process, response to stress, apoptosis, axonogenesis and cell adhesion. Pathways regulated in the db/db nerve include lipid metabolism, carbohydrate metabolism, energy metabolism, PPAR signaling, apoptosis, and axon guidance. The majority of DEGs in the glycolysis, TCA cycle, oxidative phosphorylation, fatty acid metabolism, glycerolipid metabolism, mitochondrial fatty acid elongation, lipid transport, adipocytokine signaling, PPAR signaling, and apoptosis pathways are up-regulated, whereas most of the axonogenesis-related genes are down-regulated in db/db nerve. A network of DEGs based on their co-citation in literature identified regulatory relationship between Tnf-α and key genes from the regulated pathways. Promoter sequence analysis identified over-represented transcription factor binding site (TFBS) motifs in the promoter regions of twenty two co-expressed lipid metabolism-related genes suggesting coordinated regulation of these genes by multiple transcription factors (TF). Furthermore, TF binding to these TFBS and differentially regulated in our data are annotated with nervous system development and immune response suggesting possible co-regulation of lipid metabolism, nervous system development and stress response genes. Gene expression changes in our data are consistent with pathological characteristics observed in DN including axon degeneration and demyelination, and support existing hypotheses regarding hyperglycemia mediated nerve damage in DN. Our findings support the role of hyperglycemia-induced oxidative stress and ischemia in nerve injury. Our results also support the hypothesis of oxidized lipid-mediated nerve injury and increased mitochondrial oxidative stress in dyslipidemia. Moreover, our analyses revealed a possible co-regulation mechanism connecting hyperlipidemia, stress response and axonal degeneration. Microarray data for 24 week-old BKS db/db and db/+ mouse sciatic nerve were analyzed to define significantly differentially expressed genes
2011-02-18 | E-GEOD-27382 | biostudies-arrayexpress