Team:LCG-UNAM-Mexico/Modelling
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+ | Modelling the Defence System | ||
+ | Bacteriophage infection is a complicated process, once the virus has infected it steals the translation machinery of its host. Bacterium ribosomes synthesize virus proteins and virus assembly takes place. Beyond a phage and time threshold bacterium can’t take it anymore and explodes setting free the newly synthesized bacteriophages. | ||
+ | Let’s take a look at the big picture: biochemical reactions taking place inside infected bacterium, new synthesized phage for each infected bacterium, other bacteria get infected, infection propagation. We need to approach this problem in a multi-scale fashion: molecular scale and population scale. | ||
+ | We designed and implemented a Stochastic Simulation for the essential reactions involved in the infection process: T7’s DNA insertion, transcription, translation, capsid assembly, etc. to create a Wild Type Simulation. Then we added the toxins to the model to simulate the dynamics of the kamikaze system.<br><br> | ||
+ | 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 AHL to communicate the message that phages are near, advised bacteria will produce antisense RNA against T7’s DNA polymerase. | ||
+ | To simulate the population scale dynamics 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) to approach the spatial dynamics. | ||
+ | Using the CA we simulate: | ||
+ | a) Bacteria’s duplication, movement, infection and lysis. | ||
+ | b) Quorum Sensing and T7 Diffusion. | ||
+ | c) The alarm system. | ||
+ | So let’s put all together! | ||
+ | Events occurring in the CA are stochastic processes. The attributes of the bacteria in the CA are random variables with and associated 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 distributions generated by the Molecular Simulations and voila, now we have the big picture. | ||
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Revision as of 21:32, 15 October 2009