Sible2007 - Mitotic cell cycle mecanism in Xenopus Laevis

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Model Identifier
BIOMD0000000942
Short description 
Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.
Format
SBML (L2V4)
Related Publication
  • Mathematical modeling as a tool for investigating cell cycle control networks.
  • Sible JC
  • Methods (San Diego, Calif.) , 2/ 2007 , Volume 41 , Issue 2 , pages: 238-247 , PubMed ID: 17189866
  • Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0406, USA. siblej@vt.edu
  • Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.
Contributors
Submitter of the first revision: Matthieu MAIRE
Submitter of this revision: Ahmad Zyoud
Modellers: Matthieu MAIRE, Ahmad Zyoud

Metadata information

is (2 statements)
BioModels Database MODEL1809060005
BioModels Database BIOMD0000000942

isDescribedBy (1 statement)
PubMed 17189866

hasTaxon (1 statement)
Taxonomy Xenopus laevis

hasPart (1 statement)
Gene Ontology regulation of cell cycle

hasProperty (1 statement)
Mathematical Modelling Ontology Ordinary differential equation model

Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags

Connected external resources

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Model files
Sible2007.xml SBML L2V4 representation of Sible2007 - Mitotic cell cycle mecanism in Xenopus Laevis 88.76 KB Preview | Download

Additional files
Sible2007.cps COPASI version 4.27 (Build 217) for reproducing figure 4A in the reference publication. 126.06 KB Preview | Download
Sible2007.sedml sed-ml L1V2 file for reproducing figure 4A in the reference publication. 2.99 KB Preview | Download

  • Model originally submitted by : Matthieu MAIRE
  • Submitted: Sep 6, 2018 2:18:25 PM
  • Last Modified: May 1, 2020 11:38:01 AM
Revisions
  • Version: 4 public model Download this version
    • Submitted on: May 1, 2020 11:38:01 AM
    • Submitted by: Ahmad Zyoud
    • With comment: Automatically added model identifier BIOMD0000000942
  • Version: 2 public model Download this version
    • Submitted on: Sep 6, 2018 2:18:25 PM
    • Submitted by: Matthieu MAIRE
    • With comment: Edited model metadata online.

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
Cyclin Cdk1 MPF

G2/mitotic-specific cyclin-B1 ; Cyclin-dependent kinase 1-A 		0.0 mmol
Cyclin

G2/mitotic-specific cyclin-B1 		0.0 mmol
Cdk1

Cyclin-dependent kinase 1-A 		100.0 mmol
IE 0.0 mmol
IE phosphorylated

phosphorylated 		1.0 mmol
Cyclin Cdk1 preMPF

G2/mitotic-specific cyclin-B1 ; Cyclin-dependent kinase 1-A ; phosphorylated 		0.0 mmol
Reactions
Reactions Rate Parameters
Cyclin_Cdk1_preMPF => Cyclin_Cdk1_MPF; Cdc25_phosphorylated nuclear*k25*Cyclin_Cdk1_preMPF k25 = 0.017
Cyclin => Cyclin_Cdk1_MPF; Cdk1 nuclear*k3*Cdk1*Cyclin k3 = 0.005
Cyclin => nuclear*k2*Cyclin k2 = 0.25
Cdk1 = Cdk1_total [] []
IE = IE_total-IE_phosphorylated [] []
IE_phosphorylated => ; ppase nuclear*kh*ppase*IE_phosphorylated/(KKh+IE_phosphorylated) kh = 0.15; KKh = 0.01
=> IE_phosphorylated; Cyclin_Cdk1_MPF, IE_total nuclear*kg*Cyclin_Cdk1_MPF*(IE_total-IE_phosphorylated)/((KKg+IE_total)-IE_phosphorylated) KKg = 0.01; kg = 0.02
Cyclin_Cdk1_MPF => Cyclin_Cdk1_preMPF; Wee1 nuclear*kwee*Cyclin_Cdk1_MPF kwee = 1.0
Cyclin_Cdk1_MPF => nuclear*k2*Cyclin_Cdk1_MPF k2 = 0.25
Cyclin_Cdk1_preMPF => nuclear*k2*Cyclin_Cdk1_preMPF k2 = 0.25
Curator's comment:
(added: 06 Sep 2018, 14:20:33, updated: 01 May 2020, 11:37:41)
Figure 4A of the reference publication has been reproduced using Copasi 4.27 (Build 217). Use attached SEDML file to reproduce figure 4A.