Difference between revisions of "Team:Bordeaux/Model"
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<li class="menu_item" style="color:#000000">The definition</li> | <li class="menu_item" style="color:#000000">The definition</li> | ||
| − | <li class="menu_item" style="color:#000000">Why | + | <li class="menu_item" style="color:#000000">Why have we chosen this model?</li> |
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Our model aims to provide friendly users with information about bacterial growth. If you wish to evaluate a risk of contamination, or to experiment with parameters to characterise bacterial plasmid transfer or plasmid maintenance, you are welcome to test it out! <br></p> | Our model aims to provide friendly users with information about bacterial growth. If you wish to evaluate a risk of contamination, or to experiment with parameters to characterise bacterial plasmid transfer or plasmid maintenance, you are welcome to test it out! <br></p> | ||
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| − | <p align="justify"> As stated before, our modelling choice was to implement a multi-agent systems. Multi-agent modelling are frequently used to model biological | + | <h3> Why have we chosen this model?</h3> |
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| + | <p align="justify"> As stated before, our modelling choice was to implement a multi-agent systems. Multi-agent modelling are frequently used to model biological phenomena. It stands out against continuous mathematical modelling, which is often used for predictions at a population level. | ||
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| + | Multi-agent modelling offers different advantages : | ||
| + | <ul> | ||
| + | <li><b>Stochasticity</b> : contrary to mathematical deterministic equations, it is much easier to induce randomness in multi-agents models.</li> | ||
| + | <li><b>Heterogeneity</b> within a population. </li> | ||
| + | <li>BLA BLA BLA</li> | ||
| + | </ul> | ||
</p> | </p> | ||
Revision as of 14:59, 7 October 2016
All iGEM projects involve modified organisms. When we work with those organisms, the question of confinement is essential to prevent their spreading out of the lab. Even if each team thinks about the best tool to answer this question, our team has decided to think about the worst situations. To answer this question, we decided to create a computational simulation model in order to see :
The presentation of our work will be done in different sections. First, we are going to explain our approach in choosing a simulation model and our reasons for our choices. In the next section, we will describe how we have created our model and explain its initialization. After this, we will explain our mathematical choices to modelize bacterial growth and plasmid loss. Finally, we will make an assessment of our model, explain how we have validated it and give our perspectives for this project. In informatics, a multi-agent system aims to represent intelligent agents which interact with one another and with a specific environment. In our case, our agents are bacteria, dispersed randomly on a grid (our environment).
The bacteria are submitted to successive actions such as growth or division, and have specific attributes such as a an individual cell mass, or more importantly a plasmid.
Our model aims to provide friendly users with information about bacterial growth. If you wish to evaluate a risk of contamination, or to experiment with parameters to characterise bacterial plasmid transfer or plasmid maintenance, you are welcome to test it out! As stated before, our modelling choice was to implement a multi-agent systems. Multi-agent modelling are frequently used to model biological phenomena. It stands out against continuous mathematical modelling, which is often used for predictions at a population level.
Multi-agent modelling offers different advantages :
What happens when a bacterial population escapes from our test tubes ?
1. Multi-agent modelling
The definition
Why have we chosen this model?
