Merging a deterministic and a stochastic model in COPASI

[Hikmet Emre Kaya]

Hello everyone,

I have one deterministic and one stochastic model which I want to merge.

Here is the workflow I had in mind.

There is a global value "a" in the deterministic network that is dependent on the transient concentrations of some of the metabolites.

I want this global value to effect the initial particle count of one of the metabs in the stochastic network, before I run a stochastic simulation on this second model. so the initial particle count would be an assignment = a * [initial particle count], where a is 1 in the beginning.

So, the deterministic one should be simulated and finalized before the stochastic one starts.

I attached two dummy COPASI files if my question was not clear.

I want to know if there is any way to merge these two models and streamline this process. When I merge the two networks, the units of the deterministic model change and convert to the units of the second model, which I obviously do not want.

Let me know if you think there is a solution around this.

Many thanks in advance,

Emre

[Hikmet Emre Kaya]

And I forgot to mention, the reason I want to merge them is I want to run a sensitivity analysis for the particle numbers in the second model with respect to the kinetic parameters in the first model.

[juergen]

Dear Emre,

There is no automatic way of merging the models, particularly if they are written with different units and you want to first run one of the models and the result of this should then affect the second (sub-)model. You could, however, script a workflow that connects  the two simulations using one of our high-level APIs:

(1) Python: https://basico.readthedocs.io

(2) R: https://jpahle.github.io/CoRC

Best wishes,

Juergen