# Team/CINVESTAV-IPN-UNAM MX/Cellular.htm

### From 2012.igem.org

*Strange variant of cellular automata for *

the simulation of a bio-reactor!

the simulation of a bio-reactor!

Introduction

First of all: why use cellular automata if any linear models are easier to analyze? First, it looks great.

Second, depending on the configuration of the Cellular Automaton (CA) "how does it look", the CA is more realistic, and it can provide enough data for other models. In fact, is just what took place in this model.

Imagine that we are scientists with more resources and that we can generate hundreds of experiments in the conditions we want to feed our prediction models. The CA allows us to do this with the only drawback that we hate our CPU.

## Brief History

Cellular automata are a mathematical model developed by Konrad Zuse and Stanislaw Ulam, but was better known and developed by John Vonn Neumann who loved parallel computing, history remembers him as the father of sequential computing.

Later during the 60's, a crazy mathematical said "oh, it would be great to do a math game where no players" (you know, the time before tetris and farmville), and created the world famous "Game of Life". The name of this cool mathematician was John Horton Conway. The really curious thing about this game was that, unlike tetris and Farmville, is a universal machine,is that you can solve almost any problem with it.

For this reason, a lot of scientists began to study de about the CA. First a physicist named Steven Wolfram, he emphasize the importance of simulations and modeling computer as unquestionable substitute of the traditional experimentation.

## Our Approach!

In the near future, our project can be used to produce biofuels, so we decided to create a software that allows us to generate Pilot-Plant simulations. To perform this software, we are taking the two-dimensional Cellular Automata developed by John Conway called "GAME OF LIFE", and extending it over a living cell type, in where there are several stages of the cell and different scope ranges. Our goal is to generate enough simulations at different concentrations of reactants or products under distinct environmental conditions; until the program shows what inputs (concentrations) are optimal to maximize the production. At this point, the program runs simulations based on precompiled transition rules, but due to the inherent complexity of cellular automata, a single simulation isn´t enough. So the next step is to vary the concentrations and apply an evolutionary algorithm to modify the starting conditions of the "game" in each iteration cycle.

**Rhodofactory 2012**