Team:LCG-UNAM-Mexico/Conclusions

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(Conclusions and perspectives)
(Conclusions and perspectives)
 
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Most of the models for biological systems rely on deterministic
Most of the models for biological systems rely on deterministic
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approaches, moreover they  fail to assemble both molecular and population scales.  The problem with deterministic approaches  is that they do not reproduce the phenotype distributions observed in nature.
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approaches; moreover they  fail to assemble both molecular and population scales.  The problem with deterministic approaches  is that they do not reproduce the phenotype distributions observed in nature.
Our stochastic simulations for the molecular dynamics generate distributions for the number of biomolecules during the infection process. We integrate molecular and population dynamics by direct sampling of these distributions. This is an innovative way to model biological systems. We have seen molecular and population models working separately, but we have never seen a multi-scale model that integrates molecular and population dynamics in such a delightful and realistic way as we did.
Our stochastic simulations for the molecular dynamics generate distributions for the number of biomolecules during the infection process. We integrate molecular and population dynamics by direct sampling of these distributions. This is an innovative way to model biological systems. We have seen molecular and population models working separately, but we have never seen a multi-scale model that integrates molecular and population dynamics in such a delightful and realistic way as we did.

Latest revision as of 03:29, 22 October 2009

Conclusions and perspectives

Most of the models for biological systems rely on deterministic approaches; moreover they fail to assemble both molecular and population scales. The problem with deterministic approaches is that they do not reproduce the phenotype distributions observed in nature.

Our stochastic simulations for the molecular dynamics generate distributions for the number of biomolecules during the infection process. We integrate molecular and population dynamics by direct sampling of these distributions. This is an innovative way to model biological systems. We have seen molecular and population models working separately, but we have never seen a multi-scale model that integrates molecular and population dynamics in such a delightful and realistic way as we did. One important achievement of our work is the Burst Size Distribution. Using stochastic kinetic simulations, we generated a burst size distribution that is in excellent agreement with existing experimental data. Our model is a powerful tool to analyze how the molecular distributions are sensitive to the parameters. Furthermore, our model is also a reliable tool for sampling molecular distributions for basic research or assembly of complex models.

Although the experimental constructions are still in development, both delivery and defense systems present as a potent introduction of bacteriophages as rich elements in synthetic biology whose properties may serve for applications that could go beyond the reaches of current devices and parts available. As a highlight, we designed a transduction system based on the natural properties of phages such as P4 and P2, which could deliver synthetic biobricks into a range of novel hosts. A direct clinical application is the sabotage of pathogenicity regulators in bacteria for manipulating disease development, as planned with Enterohemorragic Escherichia coli (EHEC) LER binding site submitted to the registry by our team.

The construction of the population-based defense system for bacteriophage infection is a new approach to coupling the elements already present in the part registry along with the sensing of an external element: T7 and T3 polymerases. The system could be applied as part of a phage external switch or as a fine-regulated external stimulus control in biological machine populations.

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