Team:LCG-UNAM-Mexico:ABmodel

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=AGENT-BASED MODEL=

HOW TO USE IT?
Click the SETUP button to setup the infections in the virtual culture. Then click on the experiment that you desire to run (NEGATIVE CONTOL or FIGHT FIRE WITH FIRE). You can control the number of bacteria and the number of initial infections with the slide bars. Also, with the switch display-AHL, you can choose to observe or hide the AHL diffusion. Click again in the current experiment button to stop the simulation.

Proliferation of bacteriophage infection in an Escherichia coli culture is a complicated phenomenon. Even more complex is the behavior of our synthetic Escherichia coli population which fights against the infection. In order to test if the intracellular defense system was effectively contending versus phages at population level, we implement an agent-based simulation using the program NetLogo. Agent-based models have the advantage of being easily constructed. This approach allows us to recreate complex processes and the interactions of thousands of objects (for instance, bacteria and phages) in parallel, thus exploring their effects in the community (the culture) as a whole.

In an agent-based model each object has its own variables and states.

Contents

 * Run the model
 * Variables
 * Rules
 * Assumptions

RUN THE MODEL
This model consists of two types of experiments: Simulate a T7 infection of wild-type Escherichia coli. When a phage encounters a bacterium, it attaches itself to the cell wall of the bacterium and start an infection. The phage takes over the E.coli’s metabolic machinery in order to ensemble multiple copies of itself. Within a time tau (latency period) the host bacteria die, releasing phage particles ready to infect nearby cells. Phages win the game. When an E. coli is infected by a phage, the warning device and the suicide system are activated. The burst size of the phage is severely diminished (according to BSD prediction). At the same time, the infected bacteria produces GFP (that’s why they are green :p) and simultaneously release AHL to the medium. AHL act as a signal to warn the neighbor bacteria of the presence of phages in the vicinity. Advised bacteria begins to synthesize antisense RNA molecules that protect it against phages. Warned bacteria additionally produces RFP indicating that they are protected. Bacteria win the game!
 * NEGATIVE CONTROL.
 * FIGHT FIRE WITH FIRE.

AGENTS

 * Bacteria
 * Phages

VARIABLES:
Bacteria's variables   Period   Latency period   Time to release AHL   Time to die by system   Phages' variables   Burst size   Bacteriophage decay rate   Effective infection probability (Bacteriophage Adsorption Rate) </li> </ul>

RULES
The objects interact with each other through a series of defined rules:  <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Bacteria' movement </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Phages' movement </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Infection process </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Suicide system activation </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Diffusion of AHL </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Antisense production when AHL reach to the activation threshold </li> </ul>

ASSUMPTIONS
For simplicity the model make certain assumptions:  <li class=rvps3 style="margin-left: 0; text-indent: 0px"> The time is discrete </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Only one phage infect one bacteria </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Phages moves at random </li> <li class=rvps3 style="margin-left: 0; text-indent: 0px"> Antisense avoid totally the phage production </li> </ul>

Important to note: In this model, for clarity, we have neglected bacterial duplication since we are only interested in the local dynamic of the population.


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