Científicos del Instituto de Investigación Scripps (TSRI) han informado sobre su síntesis de una forma de la bacteria Escherichia coli con un código genético que usa 21 elementos aminoácidos para sintetizar proteínas - en lugar de los 20 encontrados en la naturaleza.

Ésta es la primera vez que alguien ha creado un organismo totalmente autónomo que use 21 aminoácidos y tenga la maquinaria metabólica para construir esos aminoácidos.

La vida tal como la conocemos se compone en el nivel molecular de los mismos elementos básicos, por ejemplo, toda forma de vida existente en la Tierra usa los mismos cuatro nucleotides para producir ADN, y, casi sin excepción, todas las formas conocidas de vida utilizan los mismos 20 aminoácidos - y solamente esos 20 - para producir proteínas. La pregunta es ¿por qué la vida se detuvo en 20 y por qué esos 20?

Algunos científicos han agregado previamente aminoácidos artificiales a los organismos, pero la mayoría de esos experimentos implicaban eliminar la fuente de aminoácido natural del organismo y sustituirla por un pariente cercano. "Así pues, al final, todavía se obtiene una bacteria de 20 aminoácidos, pero utilizando un aminoácido artificial en lugar del natural", dijo Christopher Anderson, uno de los autores del documento sobre esta investigación conducida por Peter Schultz. "Lo qué nuestro grupo realmente quiso hacer es ampliar el código genético, no sólo recodificarlo. Hacer eso exige mucho más esfuerzo. Tienes que conseguir de algún modo denotar específicamente cómo la proteína va a codificar este 21º aminoácido, porque todo en el código genético tiene ya un significado asociado a él."
> de *Expanding the genetic code --TSRI scientists synthesize 21-amino-acid bacterium*, 14 de enero, 2002

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imago
> ampliación genética de una bacteria

reciban primeramente un saludo a todos los colegas.
primeramente es asombroso que una bacteria simple y común como e. coli tenga 21 ya que solo contienen dos cromosomas, es algo realmente interesante y lo cual debe ser tratado mas afondo ya que podría ser una planta bioquímica para producir y sintetizar moléculas complejas para beneficio popular.

Hi... :-)

First, there is an issue of sizes and length scales, e.g., a E.Coli bacteria is 10.000 times larger than aminoacids. Thus, in volume, one could put more than 10.000.000.000.000 aminoacids (and also you can pack 10.000.000.000 neurons in the brain). Second, there may be a reason why
all life forms (bacteria, plants, animals...) are made up from the same 20 aminoacids, maybe because there was a specially active pre-bio-chemical period on the planet which finishesd with a list of 20 aminoacids (probably the most common and stable under determined circumpstances). Thus, adding an extra aminoacids (which is very similar to the others but having local modifications, like the position of an atom in the molecule), is like having more chances to built new functional structures. However, in
evolutive biological systems, the building units are not as important as their interactions. Complex systems as the biological ones, are extremely tolerant to local perturbations, and then, at the end, it could be that having the code to synthesize an extre aminoacid has the same impact as eating a (fully artificial) chewing gum...

Besides, the idea to use bacteria as highly selective, low energy, low cost, large production, waste free biochemical reactors is an old dream, and a young reality, currently a common technique in biochemistry labs. Related to this, bacteria are also used, not to produce chemical molecules, but to eat them, and, inexpensively and consistently, clean the environment.

And about the modifications of nature, for those who does not have enough with this one, apart from modifying its building blocks, one can force
evolution under lab conditions:

(extracted from http://www.its.caltech.edu/~els/0001.html )

Christopher Voigt (Chemical Engineering)

Title: Outrunning Nature: Optimizing In Vitro Evolution

Evolution has led to enormous diversity in the plant and animal kingdoms. The simple process of cycles of mutation and selection has fueled the
intricate and complex hierarchies of chemistry in biology. Richard Dawkins referred to this process as being "blind," due to the lack of rational
input from a designer. In this lecture, I will demonstrate that, while evolution is random, there is order in the types of solutions that will emerge as well as an intrinsic self-organization of the evolutionary algorithm itself. From the prospective of biotechnology, the elucidation of these dynamics will allow the design of a new generation of evolutionary
methods that maximize our ability to discover novel biological molecules for pharmaceutical and industrial applications.

The in vitro evolution of enzymes presents an ideal system to study the dynamics of molecular evolution. Like natural evolution, diversity is
created through mutation and recombination and the mutant enzymes are screened for improvement in desired catalytic properties (such as stability, activity, or selectivity). This simple search technique strongly resembles the genetic algorithm, a common search strategy in computer
science. Theory developed to understand genetic algorithms has focused on using information about the structure of the search space to optimize
evolutionary parameters (e.g., the mutation rate). Following this archetype, we first develop simple models, based on statistical mechanics,
to study the gross dynamics of the evolution algorithm. Next, we apply the principles from the simple model to specific enzyme systems, through a detailed model that captures the effects of amino acid substitutions on the stability of the protein structure.

As an example of this strategy, I will describe our theoretical treatment of in vitro sexual recombination. Theory developed to understand genetic algorithms hypothesizes that the optimal crossovers are those that least disrupt structural building blocks. Based on this assumption, we have developed a computational method to predict the locations of crossovers for
recombination experiments. Our predictions correlate well with crossovers that lead to functional enzymes in independent experiments with beta-lactamase, transformylase, and cytochrome.