Dataset Information

Moreno2019 - Stochastic model of G1 arrest due to proteostasis decline delimits replicative lifespan in yeast

ABSTRACT: This model is described within the paper: A G1 arrest due to proteostasis decline delimits replicative lifespan in yeast David F. Moreno, Kirsten Jenkins, Sandrine Morlot, Gilles Charvin, Attila Csikász-Nagy, Martí Aldea To be implemented in Copasi. Missing Event due to difficulties in SBML conversion to run model upon loading model in COPASI, create an event called Budding. Set the trigger expression to: {Whi5.ParticleNumber} lt 250*({Whi5i.ParticleNumber}+{Whi5.ParticleNumber})/({Whi5.InitialParticleNumber}+{Whi5i.InitialParticleNumber}) AND {Values[Budding]} lt 1. Uncheck boxes for fire at initial time if true and trigger must remain true. Leave priority box blank. Set delay to None. There are 3 targets: the value of budding which should be set to one; the time at budding which should be set to the time; and the unsynced generation which should be set to generation.

SUBMITTER: Kirsten Jenkins

PROVIDER: MODEL1901210001 | BioModels | 2019-11-29

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MODEL1901210001?filename=CellCycleProteinAggregation.xml	Xml
	MODEL1901210001?filename=ProteinAggregationCellCycle.cps	Other

Items per page:

1 - 2 of 2

Publications

Proteostasis collapse, a hallmark of aging, hinders the chaperone-Start network and arrests cells in G1.

Moreno David F DF Jenkins Kirsten K Morlot Sandrine S Charvin Gilles G Csikasz-Nagy Attila A Aldea Martí M

eLife 20190913

Loss of proteostasis and cellular senescence are key hallmarks of aging, but direct cause-effect relationships are not well understood. We show that most yeast cells arrest in G1 before death with low nuclear levels of Cln3, a key G1 cyclin extremely sensitive to chaperone status. Chaperone availability is seriously compromised in aged cells, and the G1 arrest coincides with massive aggregation of a metastable chaperone-activity reporter. Moreover, G1-cyclin overexpression increases lifespan in ...[more]

PMID: 31518229

Similar Datasets

Project description:This model is from the article: Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics. Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 , Abstract: Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination. Note: Developer of the model: Zhike Zi Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011 1. The global parameter that set the type of stimulation (a) for sustained TGF-beta stimulation: set stimulation_type = 1. (b) for single pulse of TGF-beta stimulation: set stimulation_type = 2. parameter "single_pulse_duration" is for the duration of stimulation, for example, single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation. *Note: make sure that the time course cover the time point when the event is triggered. (c) for single pulse of TGF-beta stimulation in COPASI change the trigger of event "single_pulse_TGF_beta_washout" from "and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT) to "and(eq(stimulation_type, 2), gt(time, single_pulse_duration))" (for COPASI) 2. Notes for TGF-beta dose in terms of molecules per cell (a) The following equation applies for conversion of TGF-beta dose in molecules per cell TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23 (b) for standard experimental setup 1e6 cells in 2 mL medium 0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell 0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell (c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed. initial Vmed = 1e-8 (1e6 cells in 10 mL medium) Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium) 3. Please note that this model contains events and the medium compartment size is varied. 4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.

Project description:Proctor2016 - Circadian rhythm of PTH and the dynamics of signaling molecules on bone remodeling This model is described in the article: Simulated Interventions to Ameliorate Age-Related Bone Loss Indicate the Importance of Timing. Proctor CJ, Gartland A. Front Endocrinol (Lausanne) 2016; 7: 61 Abstract: Bone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-?, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing. This model is hosted on BioModels Database and identified by: BIOMD0000000612. 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.