Taverna services for Systems Biology



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Probabilistic model checking (PMC) of SBML stochastic model

Depiction of the PMC of SBML stochastic model: compute" workflow.

"PMC of SBML stochastic model: compute" workflow

This is a probabilistic model checking experiment for the stochastic version of the enzymatic reaction model (SBML). The The reward-based CSL formula, which is being checked, asks the model about how many times, on average, the enzyme-substrate complex association reaction have to occur before amount of the product reaches 50% of its maximum? This is motivated by the half maximal effective concentration (EC50 coefficient). Question is asked for different enzyme initial amount to find its optimal efficiency. As the PMC is time consuming, the experiment is divided into "compute" and "plot" workflows, mainly because plotting usually requires many runs to fine-tune the plot parameters.

Depiction of the PMC of SBML stochastic model: plot" workflow.

"PMC of SBML stochastic model: plot" workflow

To run this experiment choose File > Open workflow location... and copy-paste the link to the "compute" workflow file. Then File > Run workflow..., wait for results and from the Results perspective choose Save all values > Save in single XML document. This workflow has no inputs and all parameters to play around with are embedded in string constants (modelUrl, constName, varName, rewardsUrl, propertiesUrl, initValues). Next, open and run the "plot" workflow file. This time Taverna will ask for filling in the input values, so in the input values dialog choose Load previous values and point to previously saved "compute" values. The p_MAX PRISM model parameter wont be interesting to plot, it is constant in our experiment. Delete it from constantNameList input values. Fill in the plot options list; Taverna requires at least one input value, so if you don't know what to put in, put at least PlotLabel -> "". Now you're all set to run the plotting workflow.

As a result you have the <results> XML element from the computational part and an image of calculated formula values from the plotting part. To obtain the following plot:
Probabilistic model checking of the enzymatic reaction model for different enzyme initial amounts.
we used the following plot options:

AxesLabel -> { "E(0)", "Formula value" }
PlotLegend -> None
ColorFunction -> "TemperatureMap"
PlotRange -> {{0., 20.}, {6., 13.}}
MarkerColor -> Black

Note: for description of plotting options check mathPlot WS operation wrapper docs on optionList input at myExperiment or input docs at BioCatalogue.

The plot can be read as: if E(0) is equal to 1, then, on average, before product reaches half of its maximum, each enzyme has to convert slightly more than 6 substrates. To no surprise, when E(0) is equal to 12 (the initial amount of substrate), each enzyme has to convert at most one substrate. The total, parallel enzymatic reaction system's efficiency doesn't improve significantly from that point as not much more than 12 complex formation reactions r1 are needed to achieve half of the maximum product amount.


Taverna 2 workflow files: