Difference between revisions of "Team:NTNU Trondheim/Model"

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<h1>Modeling</h1>
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<h2><span class="project-color">Introduction</span></h2>
  
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Bacteria with Quorom Sensing has been modeled using Fozard et al. 2012 article, "Inhibition of quorom sensing in a computational biofilm simulation". The method of quorom sensing inhibition has been changed to more closely represent the CRISPRi system used in experiments. The code is available on <a href"https://github.com/torhaugl/NTNU_Trondheim">github</a>.
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Bacteria produce insignificant amount of biofilm when they are alone. When bacterias are grouped up however, they produce a biofilm at a much higher rate per bacteria than they did alone. This is attributed to the quorom sensing molecule(QSM). When the bacteria senses a high concentration of QSM it starts producing biofilm at a much higher rate than when it is low.
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We have produced a computational model inspired by Fozard et al. 2012 article, "Inhibition of quorom sensing in a computational biofilm simulation". We use a 3 dimensional grid of boxes, which contain information about bacteria and concentrations of molecules. By setting up a number of differential equations, one can from an initial state of bacteria and concentrations learn how they evolve over time. In our project we are interested in finding how many bacteria is activated by the quorom sensing molecules and how the biofilm is produced. The code is available on <a href"https://github.com/torhaugl/NTNU_Trondheim">github</a>.
 
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<h2><span class="project-color">Theory</span></h2>
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<h2><span class="project-color">Results and Discussion</span></h2>
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Revision as of 17:02, 26 August 2018

Introduction

Bacteria produce insignificant amount of biofilm when they are alone. When bacterias are grouped up however, they produce a biofilm at a much higher rate per bacteria than they did alone. This is attributed to the quorom sensing molecule(QSM). When the bacteria senses a high concentration of QSM it starts producing biofilm at a much higher rate than when it is low. We have produced a computational model inspired by Fozard et al. 2012 article, "Inhibition of quorom sensing in a computational biofilm simulation". We use a 3 dimensional grid of boxes, which contain information about bacteria and concentrations of molecules. By setting up a number of differential equations, one can from an initial state of bacteria and concentrations learn how they evolve over time. In our project we are interested in finding how many bacteria is activated by the quorom sensing molecules and how the biofilm is produced. The code is available on github.

Theory

Results and Discussion