The SBML2Julia GitHub repository contains a version of the Vinod et Novak model of the G2/M cell cycle transition. The model contains 13 species, 13 simulated observables, 24 parameters and 4 experimental conditions.
The SBML2Julia problem can be created using the Python API (and assuming that the current working directory is the SBML2Julia root directory) via:
>>> import sbml2julia >>> problem = sbml2julia.SBML2JuliaProblem('examples/Vinod_FEBS2015/Vinod_FEBS2015.yaml')
and solved by:
>>> problem.optimize()
The results are then available under problem.results
, which returns a dictionary containing 'par_best'
(the best found parameter set), 'species'
, 'observables'
, 'fval'
(the negative log-likelihood) and 'chi2'
(chi2 values of residuals). For example, to access the best parameter set, type:
>>> problem.results['x_best'] Name par_0 par_best par_best_to_par_0 0 kDpEnsa 0.0500 0.048840 0.976807 1 kPhGw 1.0000 0.955727 0.955727 2 kDpGw1 0.2500 0.236274 0.945095 3 kDpGw2 10.0000 9.727009 0.972701 4 kWee1 0.0100 0.009697 0.969738 5 kWee2 0.9900 1.308203 1.321418 6 kPhWee 1.0000 1.064760 1.064760 7 kDpWee 10.0000 10.181804 1.018180 8 kCdc25_1 0.1000 0.135812 1.358117 9 kCdc25_2 0.9000 1.099638 1.221820 10 kPhCdc25 1.0000 0.961436 0.961436 11 kDpCdc25 10.0000 8.990541 0.899054 12 kDipEB55 0.0068 0.003400 0.500002 13 kAspEB55 57.0000 46.386552 0.813799 14 fCb 2.0000 1.999269 0.999634 15 jiWee 0.1000 0.199999 1.999988 16 fB55_wt 1.0000 0.967920 0.967920 17 fB55_iWee 0.9000 0.886548 0.985053 18 fB55_Cb_low 1.1000 1.066191 0.969265 19 fB55_pGw_weak 1.0000 0.971742 0.971742 20 kPhEnsa_wt 0.1000 0.100393 1.003933 21 kPhEnsa_iWee 0.1000 0.097819 0.978187 22 kPhEnsa_Cb_low 0.1000 0.101325 1.013248 23 kPhEnsa_pGw_weak 0.0900 0.090801 1.008902
Selected observables of the optimized simulation of a given simulation condition can be plotted via:
>>> problem.plot_results(condition='wt', observables=['obs_Cb', 'obs_pCb', 'obs_B55'])
Similarly, the same example problem can be solved from the command line interface:
user@bash:/sbml2julia$ sbml2julia optimize 'examples/Vinod_FEBS2015/Vinod_FEBS2015.yaml' -d 'examples/Vinod_FEBS2015/results/'
The results can be found in the output directory given to the -d
argument.
The SBML2Julia GitHub repository contains a generalised Lotka-Volterra model of 12 gut bacteria. The model conceived by Shin et al. contains 2 species, 2 observables, 156 parameters and 210 experimental conditions in its PEtab formulation.
The SBML2Julia problem can be created using the Python API (and assuming that the current working directory is the sbml2julia root directory) via:
>>> import sbml2julia >>> problem = sbml2julia.SBML2JuliaProblem('examples/Shin_PLOS2019/Shin_PLOS2019.yaml'})
and solved by:
>>> problem.optimize()
Again, the results are available under problem.results
. Plots of the observables can be generated with the problem.plot_results()
method and results can be written to TSV files with problem.write_results()
.
Similarly, the same example problem can be solved from the command line interface:
user@bash:/sbml2julia$ sbml2julia optimize 'examples/Shin_PLOS2019/Shin_PLOS2019.yaml' -d './examples/Shin_PLOS2019/results/'
The results can be found in the output directory given to the -d
argument.