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

[Nicolas Le N. <le...@eb...>]

On 02/02/12 09:57, Richard Adams wrote:

>>> 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.
>>
>
> Also we would need classes for the optimization procedure ( e.g.,
> simulated annealing, GA, etc., which each have specific arguments to
> configure them (mu, population size, mutation frequency etc., ) .
> These could be generic param name/value pairs, perhaps using Kisao
> terms? Would optimisation algorithms fit in Kisao ?

Yes, the specific algorithms are food for KiSAO, because this is an 
unbounded problem. Classes of simulation should be types of simulation, 
what we want to do, not how to do it.

>    And also time-series to reference, together with which parts of a
> dataset to use in fitting, time offsets wrt to simulation time etc.
> This would be needed when
> referencing public data sets of which we're only interested in a part.
>
> Cheers
>    Richard
>
>>> 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
>>
>>
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>
> Dr Richard Adams
> Centre For Systems Biology Edinburgh
> University of Edinburgh
> Tel: 0131 651 9019
> email : ric...@ed...
> Web: http://csbe.bio.ed.ac.uk/adams.php
>
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-- 
Nicolas LE NOVERE, Computational Systems Neurobiology, EMBL-EBI, WTGC,
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