# Difference between revisions of "LuisM SPi"

### Graphical Stochastic π Calculus model for iGEM Mexico 2007 Oscillator

The motivation for use Stochastic π Calculus for modeling our constructions is based in the advantages that present this formal lenguage against the ordinary differential equations (ODE's). There are many papers that describe this advantages, escentially I used it because is a different approach to model biological systems, a very interesting approach!.

I have learned SPi alone without any supervision, and my models has not to be necessary corrects. I hope that you can undestand my situation.

This is the representation with logic gates

Now the model in Graphical Stochastic π Calculus Representation

g(a,b)=  ?a.g'(a,b) + Ʈt'.(P(b)|g(a,b))

g'(a.b)= Ʈu . g(a,b)

P(b)=  !b . P(b) + Ʈd

g(b,c)=  ?e . g'(b,c) + Ʈt . (P(c)|GFP()|g(b,c))

g'(b,c)= Ʈu . g(b,c) + ?b . g(b,c)

g(b,c)= Ʈt' . (P(c)|GFP()|g'(b,c))

P(c)=  !c . P(c) + Ʈd

GFP()= Ʈd

g(c,a)= ?f . g'(c,a) + Ʈt . (P(a)|RFP()|g(c,a))

g'(c,a)= Ʈu . (P(a)) + ?c . g(c,a)

g(c,a)= Ʈt' . (P(a)|RFP()|g'(c,a))

P(a)= !a . P(a) + Ʈd

RFP()= Ʈd

X(e,f)= Ʈx . (P(e)|P(f)|X())

P(e)= !e . P(e) + Ʈd

P(f)= !f . P(f) + Ʈd

Once i had the model, i simulated it in the Stochastic Pi Machine, (Andrew Phillips) and I will present the result soon.