Re: [SED-ML-discuss] Future of SED-ML

[Frank T. B. <fbe...@ca...>]

> What we should do so that everyone is happy, is to make sure we say
> 
> "task 1 is made of 100 task 2 with parameter X taking 100 values,
> logarithmically distributed between value a and b."
> 
> rather than
> 
> "perform task 1 by iteratively performing task 2 with parameter X taking
> successively 100 values logarithmically distributed from a to b."
> 
> I realise it is merely a semantic trick, but would-that satisfy everyone?
> 

I look forward to see you proposal on how you would like this realized in
SED-ML. It looks a bit like we are talking about whether it is tomAtos or
tomatOs, but maybe I'm missing a bit. 

> We can still have classes of simulation that would be used in those tasks.

In fact we will need to! Especially given your list below.

> Then we could move on the proposal itself, trying to see it it fits the
bill of the
> current needs:
> 
> Parameter scan (dose response)

This can be done 

> Steady-state analysis

This can be done to an extent. Where it currently falls short is the
description of model intrinsic /derived variables (control coefficients,
elasticities, the Jacobian). This again is independent of the nested
proposal. Any other kind of newly defined simulation will have the same
issue. 

> Optimisation (parameter sampling)

This could be done with the nested approach, provided we had a simulation
class for optimization, where we would define the objective as well as
parameters and constraints. 

> Population sampling (for PK models)

This will be tough, especially without referencing experimental data, so I
would want to hold off for this just a bit longer. 

Cheers
Frank