I think the example of stochastic solvers exposes two conflicting
objectives a solver in general might have:
(1) Solve the given mathematical model. There is one single
mathematical solution, and all solvers strive to reproduce that one.
Annotations have no relevance to the solution.
(2) Solve the underlying reality. Possibly find that you can
substitute the given model by one that describes reality better.
Therefore, find a solution that diverges from the solution of the
given model, but describes reality much better. Annotations are
essential to finding the best solution.
In essence, this boils down to whether we solely want to describe a
model (the domain of SBML), or also some of the underlying reality
(the domain of BioPAX/SBPAX).
On Tue, Nov 29, 2011 at 7:47 PM, Stefan Hoops <firstname.lastname@example.org> wrote:
> Hello All,
> On Tue, 29 Nov 2011 20:45:46 +0000 (GMT)
> Darren Wilkinson <email@example.com> wrote:
>> I also don't think that a new conversion factor _completely_ solves
>> the problem, as we still have the issue of rate laws of the form
>> k*P*(P-1)/2, where P is in units of substance, 1 is in units of item
>> and 2 is dimensionless... Unless you require people to explicitly
>> embed the new conversion factor within the mathematical formulae of
>> rate laws of this form (so that the 1 is multiplied by our new
>> conversion factor), you are still going to struggle with the
>> automatic interpretation of models which do not use item units. And
>> even if you think that is a great idea, that still only really solves
>> the problem for models written with discrete stochastic simulation in
>> mind. It still doesn't help for model containing rate laws containing
>> k*P*P. They are still wrong however you interpret them! ;-)
> Darren hits the nail on the head. I have advocated years ago that the
> kinetic laws as they exist in SBML need to be augmented. Either, we
> allow the ability to specify alternatives for stochastic and
> deterministic use or we require that software implements an
> intelligent correction.
> In the absence of a solution the COPASI team implemented some heuristics
> to determine whether an SBML model was meant to be simulated
> stochastically and the rate law was properly corrected by the user or
> whether it was written with deterministic integration in mind. COPASI
> uses a model attribute to achieve store that information and correct
> intelligently if required. In the interest of being explicit I suggest
> the creation of a propensity in addition to the kinetic law.
> While we are talking about stochastic models expressed in SBML I have
> one issue with the way currently the propensities/kinetic laws are
> written. They are volume independent which does make any sense at all
> especially if we think about multi-compartment stochastic models. Even
> in stochastic models the propensity depends on the concentration and
> not on the number of items.
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Oliver Ruebenacker, Computational Cell Biologist
Virtual Cell (http://vcell.org)
SBPAX: Turning Bio Knowledge into Math Models (http://www.sbpax.org)
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