Modelling Cell Signalling Networks: A Case Study Of Myocyte Adrenergic Regulation
Catherine
Lloyd
Bioengineering Institute, University of Auckland
Model Status
This is the original unchecked version of the model imported from the previous
CellML model repository, 24-Jan-2006.
Model Structure
Signal transduction pathways form communication networks in the cell. By transducing a range of environmental cues such as hormones and neurotransmitters, they coordinate many of the physiologicval reposnses to changes in the cell environment. In comparision with metabolism, signalling networks are poorly characterised. They account for a large portion of biological complexity, and understanding the function of these complex signalling networks presents a challenge to modern biology, requiring a combined approach of experimental analysis and modelling.
Systems models of cell signalling can be considered at two levels of detail: abstract top-down models and mechansistic systems analyses. Top-down modelling, often based on protein interaction data, is useful for elucidating qualitative, topological features of signalling networks. Mechanistic systems models usually describe the biochemical reactions with kinetic rate laws and differential equations. There are several different approaches for devloping mechanistic systems models of signal transduction. These different methods are reviewed by Saucerman and McCulloch in the 2004 publication described here. As a case study of model devlopment and analysis, they examine the funcyional roles of phospholamban, the L-type calcium channel, the ryanodine receptor, and troponin I phosphorylation upon beta-adrenergic stimulation in the rat ventricular myocyte (see the figure below).
Model analysis revealed that although protein kinase A-mediated phosphorylation of the ryanodine receptor greatly increases its calcium sensitivity, calcium autoregulation may adapt quickly be negating potential increases in contractility. The authors concluded that phospholamban phosphorylation is the main mechanism for increased sarcoplasmic reticulum load and calcium relaxation rate during beta-adrenergic stimulation, whereas both phospholamban and the L-type calcium channle contribute to increased systolic calcium.
The complete original paper reference is cited below:
Mechanistic systems models of cell signalling networks: a case study of nyocyte adrenergic regulation, Jeffrey J. Saucerman and Andrew D. McCulloch, 2000,
Progress in Biophysics and Molecular Biology
, 11, 369-391. (Full text (HTML) and PDF versions of the article are available on the Progress in Biophysics and Molecular Biology website.) PubMed ID: 15142747
cell diagram
Schematic diagram of the beta1-adrenergic signalling ntework and its regulation of rat ventricular myocyte excitation-contraction coupling.
$\mathrm{PKACII\_RyR}=\mathrm{PKACII\_RyRtot}+\frac{\mathrm{epsilon}\mathrm{RyR}\mathrm{PKACII\_RyR}}{\mathrm{Ks\_RyR\_PKAC}}$
$\mathrm{PP1\_RyR}=\mathrm{PP1\_RyRtot}+\frac{\mathrm{epsilon}\mathrm{RyRp}\mathrm{PP1\_RyR}}{\mathrm{Ks\_RyR\_PP1}}$
$\mathrm{PP2A\_RyR}=\mathrm{PP2A\_RyRtot}+\frac{\mathrm{epsilon}\mathrm{RyRp}\mathrm{PP2A\_RyR}}{\mathrm{Ks\_RyR\_PP2A}}$
$\mathrm{RyR}=\mathrm{RyRtot}+\frac{\mathrm{epsilon}\mathrm{RyR}\mathrm{PKACII\_RyR}}{\mathrm{Ks\_RyR\_PKAC}}$
$\mathrm{RyRp}=\mathrm{RyRptot}+\frac{\mathrm{epsilon}\mathrm{RyRp}\mathrm{PP1\_RyR}}{\mathrm{Ks\_RyR\_PP1}}+\frac{\mathrm{epsilon}\mathrm{RyRp}\mathrm{PP2A\_RyR}}{\mathrm{Ks\_RyR\_PP2A}}$
$\mathrm{RyRtot}=\mathrm{RyRsum}-\mathrm{RyRptot}$
$\frac{d \mathrm{RyRptot}}{d \mathrm{time}}=\mathrm{kPKA\_RyR}\frac{\mathrm{epsilon}\mathrm{RyR}\mathrm{PKACII\_RyR}}{\mathrm{Ks\_RyR\_PKAC}}-\mathrm{kPP1\_RyR}\frac{\mathrm{epsilon}\mathrm{RyRp}\mathrm{PP1\_RyR}}{\mathrm{Ks\_RyR\_PP1}}+\mathrm{kPP2A\_RyR}\frac{\mathrm{epsilon}\mathrm{RyRp}\mathrm{PP2A\_RyR}}{\mathrm{Ks\_RyR\_PP2A}}$
$\mathrm{KMrel}=\frac{1.64}{1.0+\frac{\mathrm{RyRptot}}{\mathrm{RyRsum}}}\mathrm{KMrel0}$
$\mathrm{TnI}=\mathrm{TnItot}-\mathrm{TnIp}$
$\frac{d \mathrm{TnIp}}{d \mathrm{time}}=\frac{\mathrm{kPKA\_TnI}\mathrm{PKAC}\mathrm{TnI}}{\mathrm{KM\_PKA\_TnI}+\mathrm{TnIp}}-\frac{\mathrm{kPP2A\_TnI}\mathrm{PP2A}\mathrm{TnIp}}{\mathrm{KM\_PP2A\_TnI}+\mathrm{TnIp}}$
$\mathrm{KMTnC}=(1.0+0.45+\frac{\mathrm{TnIp}}{\mathrm{TnItot}})\mathrm{KMTnC0}$
mechanics
Cardiac Myocyte
pharmacology
signal transduction
gpcr
metabolism
cardiac myocyte
electrophysiology
cardiac
beta-adrenoreceptor
Progress in Biopysics and Molecular Biology
Mechanistic systems models of cell signaling networks: a case study
of myocyte adrenergic regulation
The University of Auckland
The Bioengineering Institute
Catherine
Lloyd
May
The University of Auckland, Bioengineering Institute
Catherine Lloyd
2003-12-05
Andrew
McCulloch
D
2004
c.lloyd@auckland.ac.nz
Jeffrey
Saucerman
J
This is the CellML description of Saucerman and McCulloch's 2004
mathematical model of myocyte adrenergic regulation.
keyword
Saucerman and McCulloch's 2004 mathematical model of myocyte adrenergic
regulation.
Cardiac Myocyte