IPN UNAM 2006

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First bulletin of iGEM MEXICO November 2006 (bulletin in Spanish). Media:Bulletin-1.pdf

IPN logo.jpg

[http://www.ipn.mx/ Instituto Politécnico Nacional-Universidad Nacional Autónoma de México, México]


Mexican Staff - iGEM MEXICO


STUDENTS INSTRUCTORS
María E. González Jiménez
maruhtfl#hotmail.com
Genaro Juárez Martínez (COM)
genarojm#correo.unam.mx
Raúl I. López Gómez
raul.ivan.lopez.gomez@hotmail.com
Pablo Padilla (COM)
pablo#mym.iimas.unam.mx
Tania G. Bermúdez Cisneros
tgbermudez#hotmail.com
Edgar Salgado Majarrez (BIO)
esalgado@ipn.mx
Paulina A. Leon Hernández
pauanana#gmail.com
Juan S. Aranda Barradas (BIO)
jaranda@acei.upibi.ipn.mx
Pablo Gerardo Padilla
tyomero#gmail.com
Ma Carmen Oliver Salvador(BIO)
oliveripn@hotmail.com
Izchel N. Ríos Gaspar
nayb_juice#yahoo.com.mx
Paola B. Zarate Segura (BIO)
Pbzars@yahoo.com
Arturo Rodríguez Martínez
arturo_rguez_mtz#yahoo.com.mx
Claudia I. Franco Arteaga (BIO)
claudia_imelda@yahoo.com
Julio N. Argota Quiróz
julioargota#hotmail.com
Rosaura Palma Orozco (COM)
rpalma#math.cinvestav.mx
Alejandra Sánchez Arzate
alesa#ciencias.unam.mx
Arturo Becerra Bracho (BIO)
abb#fciencias.unam.mx
Iván Y. Fernández Rosales
najtaaj#gmail.com
Elias Samra Hassan (COM)
elias#uxmcc2.iimas.unam.mx
José C. Rodríguez Chico
rodriguez_chico#hotmail.com
Carlos Silva Sánchez (COM)
sscarlos#gmail.com
Rogelio Basurto Flores
larckov#hotmail.com
Jaime López Rabadan (COM)
rabadanlorj#gmail.com
Rosario E. Gordillo Padilla
extraterrestre710#hotmail.com
Claudia Benítez (BIO)
beni1972uk#yahoo.com.mx
Juan C. Gómez Sánchez
thunder2099#hotmail.com
Armando Galicia Naranjo
armandushko#hotmail.com
Abraham J. Leal Baena
fenixaj#hotmail.com

BIO: Biology, COM: Computation, PHY: Physics


Line of Investigation


Our main line of investigation uses unconventional computing as a means of building novel computing paradigms, such as in biology, physics and chemistry.

We are attempting to construct discrete dynamical systems capable of simulating cellular computation. Therefore, our work focuses on molecular computation. We thus have three models using cellular automata theory (you can see our original contributions [http://uncomp.uwe.ac.uk/genaro/ click here]).

Cellular automata (CA) operate over a scales ranging from the molecular level to n-dimensions and are massively parallel. Also, they are capable of supporting universal computation, self-repair and self-reproduction.

We wish contribute to the iGEM project development various protein based bio-components. We will work along three main lines: complex and reversible dynamical systems and formal languages, that support particles and multiple reactions, related to the molecular transformations.

We aim to develop CA software for a specific bio-simulation and gradually implement broader real biological scenarios.


iGEM México Project


EXPERIMENT PROPOSAL: Engineering a genetic signaling cascade to produce a green flourescence protein expression/repression system in Escherichia coli.


Upibifig1.JPG
Figure 1. Green flourescense protein.


IN THIS PART OF THE PROJECT: We intend to emulate some genetic networks already identified in Arabidopsis responsible for the formation of hair in root and leaves. These networks lead to simple genetic circuits of the repression/expression type. We would like to show that these systems support Turing patterns.


Unam1.JPG
Figure 2. Turing patterns.


MODELLING PROPOSAL: Developing models to describe inside-the-cell metabolic events through a cellular automata approach.


We study two-dimensional cellular automaton, where every cell takes states 0 and 1 and updates its state depending on sum of states of its 8 closest neighbors as follows. Cell in state 0 takes state 1 if there are exactly two neighbors in state 1, otherwise the cell remains in state 0. Cell in state 1 remains in state 1 if there are exactly seven neighbors in state 1, otherwise the cell switches to state 0. CA governed by such cell-state transition rule exhibits reaction-diffusion like pattern dynamics, so we call this Diffusion Rule.


Diffusionrule.JPG
Figure 3. Diffusion rule.


Using the diffusion rule we can generate a dynamical pattern over a system, like turn on/off ligth with alive o dead cells that shows a luminescence, examples include fluorescence, bioluminescence and phosphorescence.

Starting with any configuration, the cells alive are represented in yellow (the activator) and dead in black (the inhibitor), see figure 4. The system is created defining an inicial state over the base configuration (see figure 3). The luminescence is obtained by the evolution of this initial pattern.


Lumi.JPG
Figure 4. Luminescence by diffusion rule.

Collaborators


Paula Figueroa Arredondo (BIO), Absalom Zamorano (BIO), Jovita Martínez (BIO), Juan C. Seck Tuoh Mora (COM), Sergio V. Chapa Vergara (COM), Francisco Hernández Quiroz (COM), Carlos A. Espinosa Soto (BIO), Mark Olson (BIO), Rafael Baquero (PHY), José U. Cruz Cedillo (COM), Rafael Peña Miller (COM), Rocío Reséndiz Muñoz (COM) and Ulises Vélez Saldaña (COM)



Institutions


IPN logo.jpg Instituto Politécnico Nacional (National Polytechnic Institute)

Unam.gif Universidad Nacional Autónoma de México (National Autonomous University of Mexico)

Hidaldo.jpg Universidad Autónoma del Estado de Hidalgo (National Autonomous University of Hidalgo State)

CINVESTAV1.JPG Centro de Investigación y de Estudios Avanzados (Centre of Advanced Studies and Researche)

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