Weis2014 - Data driven Mammalian Cell Cycle Model

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This a model from the article:
A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation.
Michael C. Weis, Jayant Avva, James W. Jacobberger, Sree N. Sreenath PLoS ONE 2014 May 13: 9(5): e97130 24824602 ,
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genome replication and partitioning to daughter cells. From published experimental evidence, we propose a molecular mechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on the synthesis and degradation of three ‘‘master regulator’’ proteins (CtrA, GcrA, and DnaA), is converted into a quantitative model, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts for important details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. It reproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, and predicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating the cell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may be applicable to other species of importance in agriculture and medicine.

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Related Publication
  • A data-driven, mathematical model of mammalian cell cycle regulation.
  • Sreenath SN
  • PloS one , 1/ 2014 , Volume 9 , Issue 5 , pages: e97130 , PubMed ID: 24824602
  • Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States of America.
  • Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours) calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.
Submitter of the first revision: Ashley Xavier
Submitter of this revision: Ashley Xavier
Modellers: Ashley Xavier

Metadata information

is (2 statements)
BioModels Database MODEL1811220001
BioModels Database BIOMD0000000723

hasTaxon (1 statement)
Taxonomy Homo sapiens

isVersionOf (2 statements)
Gene Ontology cell cycle
NCIt Cell Cycle Pathway

isDescribedBy (2 statements)
hasProperty (1 statement)
Reactome Cell Cycle

Curation status


Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

Weis2014.xml SBML lvl2 file containing the model 276.14 KB Preview | Download

Additional files

Weis2014-generate_plot.R R code used to plot the curation data 2.17 KB Preview | Download
Weis2014.cps Copasi file to generate the curation figure data 411.48 KB Preview | Download

  • Model originally submitted by : Ashley Xavier
  • Submitted: Nov 22, 2018 12:24:13 PM
  • Last Modified: Nov 22, 2018 12:24:13 PM
  • Version: 3 public model Download this version
    • Submitted on: Nov 22, 2018 12:24:13 PM
    • Submitted by: Ashley Xavier
    • With comment: Automatically added model identifier BIOMD0000000723
: Variable used inside SBML models

Species Initial Concentration/Amount

Transcriptional regulator ERG
0.0 mmol

0.716055 mmol

1.388537 mmol

Retinoblastoma-associated protein
0.1071 mmol

Cyclin-A2 ; Cyclin-dependent kinase 1
0.003801725709734 mmol

G2/mitotic-specific cyclin-B3
0.01 mmol

0.99736 mmol
Reactions Rate Parameters
DRG => cell*k18*DRG k18 = 176.35
E2F + Rb => E2FRB cell*v48 v48 = 1352.4191649675
pE2F => ; pE2F, Cdh1 cell*kde2fcdh1*pE2F*Cdh1 kde2fcdh1 = 1.7635
Rb => ppRB cell*v43 v43 = 35.2584572674058
ppRB => Rb cell*v44 v44 = 4495.61597904523
actCycACdk1 => ; actCycACdk1 cell*kasa*actCycACdk1*freeCK1 kasa = 19733.57; freeCK1 = 0.55403292
=> actCycB + cycB; mass cell*Vsb*mass*k2 Vsb = 7.16092423585105; k2=2.0
=> Cdh1; Cdc20A cell*(kah1p+kah1pp*Cdc20A)*(1-Cdh1)/((Jah1+1)-Cdh1) kah1p = 155.8708; Jah1 = 0.15; kah1pp = 176350.0
=> DRG cell*k17*(DRG/J17)^2/(1+(DRG/J17)^2) k17 = 2645.25; J17 = 0.3
Curator's comment:
(added: 22 Nov 2018, 12:16:56, updated: 22 Nov 2018, 12:23:33)
Figure 5 of the publication. Due the differences in the implementation of the model in matlab and COPASI there is a small shift in the time axis.