The Project

Bacteriophage infection represents an interesting process in science and industry. The idea of being able to contend at a population level with such infections is the main motivation for the development of our project.

We propose a population level approach relaying on a defense system delivered by an engineered version of the enterobacteria phage P4. The purpose of the defense construction is to make a bacteria to hold back the process of infection by triggering a cellular death response when a cell encounters a specific component of the infective phage. Such response will be fast enough to stop the formation process of viral particles, thus preventing the phage proliferation and population decline.

The defense system delivery takes advantage of the satellite properties of P4 phage. This means that a P4 phage engineered with the defense construction will be able to infect an E.coli strain which harbors some genes from helper phage P2 that are used for complementing and completing P4 life cycle, hence creating a production line of our version of P4.
Besides, the defense system will consist of DNA and RNA degradation by toxins which will be transcribed by T3 or T7 RNA-Polymerases fast enough to stop phage assembly and scattering in the environment. Simultaneously, a quorum sensing signal will be diffusing to the non-infected bacteria acting as a transcriptional activator of an antisense RNA against bacteriophage's transcriptional machinery , hence "warning" the population to prepare against further T3 or T7 infection.

Furthermore, we will implement a stochastic multi-scale model. The model will simulate the behaviour at the intracellular scale using stochastic molecular simulations and at the populations scale using a Cellular Automata and a system of ODE's.
Simulations results are in good agreement with existing experimental data. Thanks to the structure and design of the model this can be easily modified in order to simulate infection dynamics for different bacteria and phages. Furthermore, our Molecular model can be used as a reliable tool for sampling biomolecules distributions involved in phage infection processes.