This folder contains various scripts to generate an SBML model describing activation (phosphorylation) of ERK-MAPK
components across the depth of human epidermis. The model was initially created in MATLAB by Dr Jerry
Gao (jerry.gao@unimelb.edu.au) and subsequently implemented in SBML for submission at BMC Systems Biology.
The associated manuscript should be referred to for further details:
Cursons, J., Gao, J., Hurley, D.G., Print, C.G., Dunbar, P.R, Jacobs, M.D. & Crampin, E.J. (2015).
Regulation of ERK-MAPK Signalling in Human Epidermis. BMC Systems Biology, under review.
DOI: 10.1186/s12918-015-0187-6
PMID: 26209520
BMC Systems Biology URL @ 11/09/2015: http://www.biomedcentral.com/1752-0509/9/41
The modelling framework used here is a 'normalized Hill differential equation' approach, as described within:
Kraeutler, Matthew J., Soltis, Anthony R., & Saucerman, Jeffrey J. (2010). Modeling cardiac ß-adrenergic
signaling with normalized-Hill differential equations: comparison with a biochemical model. BMC Systems
Biology. Nov, 18;4: pp. 157
DOI: 10.1186/1752-0509-4-157
PMID: 21087478
The resulting SBML model was deposited in BioModels Database [Li C et al. BioModels Database: An enhanced, curated
and annotated resource for published quantitative kinetic models. BMC Systems Biology 2010, 4:92] and
assigned the identifier MODEL1503270000.
SED-ML scripts to execute this model at various spatial positions, are available together with various
MATLAB scripts, from:
http://sourceforge.net/projects/EpidermalERKMAPK/
For further details, please contact:
Joe Cursons: joseph.cursons@unimelb.edu.au
Edmund Crampin: edmund.crampin@unimelb.edu.au
Jerry Gao: jerry.gao@unimelb.edu.au
Last updated 11/09/15
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There are three MATLAB .m files contained within this folder
optimp.m - the MATLAB script file which calls on the MATLAB function 'lsqnonlin' to determine values for
the baseline and amplitude of the input signals (plasma-membrane CaM and tissue Ca^2+) which best
fit the nuclear and cytoplasmic phospho-ERK data (see Table 2 of the associated manuscript).
V2O.m - a collection of MATLAB functions which specify the non-linear equations
fobj.m - a MATLAB function which specifies the non-linear parameter optimisation (specifies parameters
to fit; data to optimise fit etc), evaluates the model at the different spatial positions,
normalizes the simulated values and then evaluates the optimisation function which is minimized
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Each of these MATLAB scripts contains detailed comments which should be examined for further information. Note
that variation in the fitted parameter values has been observed for different versions of MATLAB:
MATLAB 2011a (v. 7.12):
Ca^2+ baseline: 0.754
CaM baseline: 0.363
Ca^2+ amplitude: 0.092
CaM amplitude: 0.485
** Note that these parameter values correspond to Table 2 of the associated manuscript and the
SED-ML script: SEDML_EpidermalMAPK_varySpatPos_execTimeCourse.xml
MATLAB R2012b (v. 8.0.0.783):
Ca^2+ baseline: 0.736
CaM baseline: 0.523
Ca^2+ amplitude: 0.039
CaM amplitude: 0.243
MATLAB 2014a (v. 8.3.0.532):
Ca^2+ baseline: 0.832
CaM baseline: 0.595
Ca^2+ amplitude: 0.046
CaM amplitude: 0.283
** Note that these parameter values correspond to those used in the SED-ML script:
SEDML_EpidermalMAPK_varySpatPos_execTimeCourse_modFitParams.xml
MATLAB 2015a (v. 8.5.0.197613):
Ca^2+ baseline: 0.788
CaM baseline: 0.735
Ca^2+ amplitude: 0.008
CaM amplitude: 0.058
It appears that other MATLAB users have encountered a similar error:
http://www.mathworks.com/matlabcentral/answers/59415-lsqnonlin-different-results-version-r2011b-vs-r2012a
- As noted by Shashank Prasanna the Levenberg-Marquardt algorithm changed between 2011 and 2012;
a number of changes to the Optimization Toolbox have also been made through successive
versions of MATLAB which may contribute to these differences:
http://au.mathworks.com/help/optim/release-notes.html?refresh=true
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In an attempt to improve reproducibility of these computational results, we are implementing these scripts within
a Virtual Reference Environment, which will be available (expected release 18/19/15):
https://github.com/uomsystemsbiology/Cursons2015EpidermalMAPK_reference_environment
SBML and SED-ML scripts will be provided in the future to perform these analyses using parameter values from the
Reference Environment, which can be reproduced in a much more consistent manner.
For more information on Reference Environments, please refer to:
- Our GitHub project page:
http://uomsystemsbiology.github.io/research/reference-environments/
- A description in Briefings in Bioinformatics:
Hurley, DG, Budden, DB, & Crampin, EJ. (2014). Virtual Reference Environments: a simple way to
make research reproducible. Briefings in bioinformatics.
doi:10.1093/bib/bbu043
