# Difference between revisions of "LuisM SPi"

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

## Revision as of 22:26, 30 January 2008

### 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.