To summerize

REACTIONS

Based on the law of mass action that states that the rate of a chemical reaction is proportional to the product of the concentrations of the reactants, the whole modified ecosystem is represented in the next chemical reaction network

EQUATIONS SYSTEM

PARAMETERS

MATLAB

To be able to visualize the numbers obtained from modeling, we simulated the behavior described before, on the MathWorks® platform MATLAB. Thanks to these simulations we were able to compare the graphs between the variables concentrations inside a Wild Erwinia amylovora and a Modified one. The results were the following

Wild Cell

Graph 1.

Graph 2.

Graph 3.

Graph 4.

Modified Cell

Graph 6.

Graph 7.

Graph 8.

Graph 9.

CONCLUSIONS

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OPEN PROBLEMS

Through the model we described the E. amylovra's behavior changes caused by the aiiA enzyme insertion. Nevertheless, the project is based on the combination of three enzymes to reach our goal and inhibit the E. amylovra's virulence factors. We are aware that there is more to do, so with a little help from our friends from IONIS Paris, the yhjH enzyme model is now a future project. They helped us to 3D model the yhjH enzyme (shown below) and to take certain conditions under consideration to its proper activity. Thanks to this edition model, the unregulated gene inhibition over a regulated one can be taken as a basis, and together with the 3D simulation, a second model could be achieved.

References

Ingalls, B. (2013) Modeling of Chemical Reaction Networks & Gene Regulatory Networks. From Mathematical Modeling in Systems Biology(pp. 21-314 ). England: MIT press.

Frederick K. Balagaddé et al. (2008) A synthetic Escherichia coli predator–prey ecosystem, EMBO, 187, pp. 1-26.

James, S. et al. (2000) Luminescence Control in the Marine Bacterium Vibrio fischeri: An Analysis of the Dynamics of lux Regulation., JMB, 296, pp. 1127-1137.

Koczan JM, McGrath MJ, Zhao Y, Sundin GW (2009) Contribution of Erwinia amylovora exopolysaccharides amylovoran and levan to biofilm formation: implications in pathogenicity. Phytopathology 99:1237-1244