User:Luis De Jesus Martinez
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*Turing Patterns  *Turing Patterns  
  +  This kind of Patterns are based in a "simple mechanism" which has internaly an autocatalizer and an inhibitor interacting with each other. We can thing that this system is inside a cell but the signals of the system will afect neighbor cells. An by this patterns are produced.  
  +  The use of the Stochastic Pi Calculus modeling a single system is relatively easy, since it behaves like an oscillator; but the use of a spatial component increases the difficult of the model  
  +  In this link, I explained all my work addresing the goal of modeling [[LuisM_Turing_Patterns_with_Stochastic_π_CalculusTuring Patterns with Stochastic Pi Calculus]].  
  [[LuisM_Turing_Patterns_with_Stochastic_π_CalculusTuring Patterns with Stochastic Pi Calculus]]  +  
Revision as of 16:57, 12 April 2008
Contents 
Welcome to Luis' page
Visitor, feel free to comment, ask or discuss anything about my work here
Luis de Jesús Martínez Lomeli
 Undergraduate Mathematics Student at the University of Mexico (UNAM)
 Member of the UNAMIPN iGEM team
Emails
 ljml27@gmail.com
 luis_de_jesus_27@hotmail.com
Motivation
Biomathematics
My main interest in the field of the Synthetic Biology is based on the mathematical modeling of new biological systems that the synthetic biology can proporcionate. I am specially interested in the Stochastic π Calculus (SPi), a new tool which in first instance (the πCalculus) was thought to resolve concurrent problems in computer science, but in this case the Stochastic π Calculus is implemented modeling biological systems. This formal language has some advantages against the deterministic models based on ODE's or ODP's; the stochastic component give a better grade of accuracy for create different types of models. Andrew Phillips and Luca Cardelli designed a simulation algorithm (Stochastic πMachine, SPiM) http://research.microsoft.com/~aphillip/spim/ for the stochastic πcalculus based on standard theory of chemical kinetics [Gillespie 1977] where the probability of a reaction is proportional to the rate of the reaction times the number of reactants. In this simulator I have implemented my models and some of my results have give us an importat approach about how we will able to see the results at the lab(if the model was right).
The use of the SPi as a main tool to create my models has motivated myself because this formal language is not so known as the ODE's and I hope that some of my results could lead, at least in the iGEM, a different and very interesting new point of view since the most common ways of modeling.
Projects
 iGEM México
A very very short history about my job in the team.
Since 2006 our team was focused in the formation of patterns, we are specially interested in the formation of Turing patterns, but the work was very slowly... fortunately the situation changed when new integrants to the team arrived in the middle of 2007, Federico my teammate and me between them. Since there we started to investigated what is the basic way to form patterns with the help of the synthetic biology; and finally after some time in which many many problems emeged each time we was close to solve the problem, Fede showed me a beta version of an oscillator. Then my objective was model it theroretically some teachers recommended me the use of Stochstic Pi Calculus and I started learning it by myself. Just before the Jamboree i got the results from our oscillator... Actually and whith the results from the oscillator model I have started now addressing the problem of the Turing patterns formation.
A link to the theoretically description of the iGEM MEXICO 2007 oscillator.
My work in the team is focus in the modeling of biological processes, for the last jamboree I fulfil my task and i got a model that was done in Stochastic Pi Calculus(SPi)and implemented in the Stochastic Pi Machine(SPiM)
The results of the simulation gave us an idea about how we should expect to see the GFP and RFP be expressed in the lab.
 Turing Patterns
This kind of Patterns are based in a "simple mechanism" which has internaly an autocatalizer and an inhibitor interacting with each other. We can thing that this system is inside a cell but the signals of the system will afect neighbor cells. An by this patterns are produced.
The use of the Stochastic Pi Calculus modeling a single system is relatively easy, since it behaves like an oscillator; but the use of a spatial component increases the difficult of the model
In this link, I explained all my work addresing the goal of modeling Turing Patterns with Stochastic Pi Calculus.



Last updated January/14/2008