Dilan Pathirana

re: `t > 0`, yes, but it seems this also won't work, because `t > 0` will get triggered during preequilibration too it seems, sorry. Furthermore, to avoid possible numerical...

The error is that the current model definition has entities defined twice, as both an assignment and a parameter. Given your current assignments, which are of the form ```yaml assignments:...

This looks like a shortcoming of yaml2sbml, feel free to open an issue there. Until it's fixed, I would suggest manually deleting the row for `preequilibration_indicator` from your PEtab parameters...

- you could simulate the model at additional time points between the initial timepoint and the steady-state "timepoint" and plot them. It looks like there is an in-built pyPESTO function...

I guess it's just that the parameters from pyPESTO are on parameter scale. You can unscale them with e.g. ```python parameter_dict = dict(zip( pypesto_problem.x_names, pypesto_result.optimize_result.list[0].x, )) unscaled_dict = petab_problem.unscale_parameters(parameter_dict) #...

Thanks, overall I agree, probably numerical noise. You might be able to avoid this by making the simulation or steady-state tolerances more strict. There is also an option to ensure...

> * I want to try instead of calc. stead-state, take three time points [500, 1500, 3000] and make three measurement replicates for every observable. And fit to them. >...

> * Yes, that one > Could you please have a look at the modified measurements table Overall, looks like it will work for `H`. The `observableId` column looks unusual...

> Initially, `initializationPriorType` and `initializationPriorParameters` were introduced to provide prior distributions for sampling initial points in a multi-start optimization setting. For other global optimization schemes it is less clear how...

> Since what is specified in the parameter table is generally considered an integral part of the optimization problem definition that can't be ignored This means we would need multiple...