Hi Perhaps Skype would be better, since many locations would allow Skype participation, but there are only a few locations that allow participation via BestGRID.
Regards, Randall > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:cellml-discussion- > [EMAIL PROTECTED] On Behalf Of Andrew Miller > Sent: Wednesday, 14 May 2008 11:58 a.m. > To: Kevin Burrage > Cc: For those interested in contributing to the development of CellML. > Subject: Re: [cellml-discussion] Representing stochastic models in > CellML > > Kevin Burrage wrote: > > Andrew > > > > I have lots of ideas on this - but email not a good way of doing it. > > Am in oxford at the moment but could skype you if you were interested. > > Hi Kevin, > > Sorry for the delay in getting back to you about this. > > There seems to be a lot of interest amongst members of the CellML > community in Auckland about hearing your views on this; one suggestion > I > heard was to use the BestGRID rather than Skype so more people could be > involved at this end (although I am not sure how convenient it would be > for you to access the BestGRID facilities in Oxford so the timezones > would work out). > > When would be the most convenient for you? Given that there are a > number > of people who have indicated a desire to be involved in these > discussion, and also a few that are interested in sitting in to listen, > I would suggest making it at earliest next week, to ensure everyone can > get their schedules to fit, and to ensure that software is working > ahead > of time. Since you are, I presume, in a GMT+1 timezone, and we are in > GMT+12, the best time would probably be early in the morning for you / > night time for us, e.g. 7-8 AM Oxford / 6-7 PM NZ, 8-9 AM Oxford / 7-8 > PM NZ , or 9-10 AM Oxford / 8-9 PM NZ. > > Best regards, > Andrew > > > > > BW > > > > Kevin > > ------------------------------------------------- > > Kevin Burrage > > Professor of Computational Systems Biology, COMLAB, University of > Oxford > > and Professor of Computational Mathematics, IMB, University of > Queensland > > ------------------------------------------------- > > > > > > > > On Wed, 23 Apr 2008 17:31:26 +1200 > > Andrew Miller <[EMAIL PROTECTED]> wrote: > > Hi all, > > > > I have spent some time looking into how we might be able > > to represent stochastic models in CellML. This is > > something that would be useful to ensure we have properly > > contemplated for CellML 1.2. I have pasted in the notes I > > wrote on this below. > > > > Please let me know if you have any suggestions, comments, > > or criticisms of the below document. At some point, this > > will obviously need to be transformed into a more robust > > proposal, but for now, I just want to make sure we keep > > the option open to use the CellML 1.2 core to represent > > stochastic models. > > > > Best regards, > > Andrew > > > > ----- > > > > Overall goal: > > Describe a framework which can be used in CellML 1.2 to > > represent a > > range of > > different systems which require stochastic differential > > equations to > > describe. > > > > Constraints: > > Do not want to describe the procedure for solving the > > model in core > > CellML, > > only the underlying mathematical / statistical model. > > Want to express the model in such a way that the > > procedure is computable > > from the model. > > Want there to be only one interpretation of the model. > > Want the representation to be abstract enough that it is > > meaningful for a > > number of different fields, and not just chemical > > equations in a well > > stirred vessel. > > Want the representation to work naturally when mixed > > with systems of > > ordinary > > differential equations. > > > > Use cases: > > Chemical reactions under the Chemical Master Equation > > model of Gillespie: > > We need to split these into separate species. This is > > a Poisson process, > > so there are simple ways to represent it. > > > > It is more efficient to represent models using Weiner > > processes when > > this > > there are large enough numbers of molecules to justify > > this but ODEs are > > not being used. > > > > However, Poisson and Weiner processes are both Levy > > Processes, that is, > > they have stationary independent increments, are zero > > at time zero, and > > are cadlag. This is not necessarily a good thing > > because some things we > > want to model might have memory of past events or a > > time dependence. > > > > How we can represent this in CellML: > > For the continuous case, integral equations for the > > increment in terms of > > built in processes like Weiner and Poisson processes (I > > don't believe > > there > > is a clean way to represent increments in MathML). > > > > Implementations will need to identify these and work out > > the > > distribution of > > the time until the next event (good implementations > > might be able to > > perform > > symbolic algebra to work this out, but most > > implementations would probably > > just recognise common cases like Poisson distributions > > with arbitrary > > parameters, and deal with expressions involving a Weiner > > process by > > sampling > > from the increment distribution in each time step), > > which could be put > > into a > > slightly generalised Gibson-Bruck type of framework > > where we store the > > time of > > the next event. > > > > None of this helps for non stationary independent > > processes, however. > > > > How could this be related to the standards: > > It has been proposed that CellML 1.2 have a core > > specification which > > describes > > the basic way of representing the mathematical structure > > in very general > > terms, and secondary specifications be used to narrow > > CellML down to > > specific > > subsets which can be implemented in their entirety by a > > actual software > > packages. > > > > Core CellML 1.2 should be general enough to represent > > concepts like > > probability distributions (MathML allows new operators > > to be defined, > > so we > > could create ones for our base types of stochastic > > process). The ODE IV > > secondary specification would not allow stochastic > > models, while we would > > have another alternative secondary specification which > > extended the ODE IV > > one to allow certain limited types of stochastic model > > (limited to > > types we > > know how to solve). > > > > In terms of interaction with the typing system, in a > > stochastic system we > > have both reals and real-valued random variables. Once > > we have one random > > variable in our system, this will propagate to > > everything else which > > is affected by it, so most of the model will technically > > be a random > > variable. > > However, we want to be able to easily mix stochastic > > models with > > existing ODE > > IV models to create hybrid models, so we don't really > > want the required > > datatype to change just to connect up a random variable > > to a non-random > > variable. For this reason, I don't think it is > > worthwhile to consider a > > random variable of a certain type a different datatype, > > and instead we > > would > > require tools to deduce this information if they require > > it. > > > > _______________________________________________ > cellml-discussion mailing list > cellml-discussion@cellml.org > http://www.cellml.org/mailman/listinfo/cellml-discussion _______________________________________________ cellml-discussion mailing list cellml-discussion@cellml.org http://www.cellml.org/mailman/listinfo/cellml-discussion
