Team:LCG-UNAM-Mexico/Description

The Defence System

A virus infection is a process that takes place inside and individual but the real consequences of the infection become important at the population scale. In order to efficiently and accurately simulate the behaviour of our system we need to implement two different kinds of approaches: an individual-based simulation and a population simulation.
We designed a kamikaze system that will prevent the spreading of phage infection. We fused T7’s promoter with toxin E3 and GFP genes. Naive T7 will infect protected E. Coli which will start producing toxins that deactivate ribosomes. The result: No translation Machinery, no phages production and a heroic bacterium’s death. We expect the burst size to be significantly reduced when our system is working.

We designed and implemented a stochastic simulation for the essential reactions involved in the infection process: T7’s DNA insertion, transcription, translation, capsid assembly, toxins production, DNA degradation etc. With a fairly big number of simulations we are going to generate probability distributions for the number of molecules for each metabolite as a function of time. We are particularly interested in the Burst-Size Distribution (BSD); the burst-size is the number of phages an infected cell will produce.

Once we have the BSD we are ready for the Spatial Population Model. The kamikaze system we designed is meant to increase the probability that the population as a whole survive an infection process. We make infected-E. Coli commit suicide for the benefit of the population. In case suicide wasn’t altruistic enough we thought an ‘’alarm system’’ might be useful. Once a bacterium is infected it will use Quorum Sensing to communicate the message that phages are near, advised bacteria will produce antisense RNA against phage DNA Polymerase.

To simulate this system we used two different approaches:

We solved the system of Ordinary Differential Equations (ODE’s) described in REFERENCE and We designed and implemented a Cellular Automaton (CA).

Using the CA we simulate:

1. Bacteria’s duplication, movement, infection and lysis.
2. AHL and T7 Diffusion.
3. The alarm system.

So let’s put all together:

Parameters of the events occurring in the CA are random variables that take values according to a corresponding Probability Distribution. We have distributions from literature and distributions generated by our simulations. So, for instance, when a bacterium gets infected we sample the Burst-Size Distribution, when a bacterium duplicate we sample the Duplication Time Distribution to assign lifetime to the newborn bacteria and so on. Sampling the distributions is the link between kinetic and population simulations: Random Variables in the population simulations take values from the kinetic simulations and voila we have our multi-scale stochastic model.

System Specifications

Construction:
E Coli Strain:
Toxins:
Bacteriophages:

Model Validation

We expect the Burst-Size to be significantly reduced. An optimal result would be a Burst-Size of 0; we see in our results that this is not the case. The BSD has mean ___ and variance___. We can calculate the likelihood of the model (BSD) given the observed burst size for both the wild type and modified E.Coli. The CA and the ODE’s generate growth curves that can be compared with those obtained experimentally.