Difference between revisions of "Team:Pasteur Paris/TrainingThomas2"

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<div id="MainContent">
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<div class="block title">
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<h1>General introduction</h1>
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</div>
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<div class="block two-third">
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<p>This year we did a simulation of the growth of neurons towards ou biofilm using a mathematical model. We created a determin-istic model to help the wetlab establish the optimal concen-tration gradients of Nerve Growth Factor (NGF) needed for the regrowth of the nerves. Nerve growth factor (NGF) is one of a group of small protein like molecules called neurotrophins that are responsible for the development of new neurons, and for the health and maintenance of mature ones. The concentra-tion and concentration gradient of NGF are key parameters af-fecting the growth rate and direction of neurites and axons. Neurite growth have shown to be NGF dose-dependent. At a too low concentration the NGF does not have enough effect on the growth of the neurites whereas if the concentration is too high the risks could exceed the benefits, causing diseases. In order to visualize the results of the model on a microfluidic chip we used MATLAB, App Designer, Python, Gmsh and FreeFem. This is an important part of our project because it creates the link between the wetlab and drylab.</p>
 +
<p>In order to achieve our goal, we divided our project in three parts:
 +
<ol style="text-align: left;">
 +
<li>Production of NGF by the E.coli genetically modified</li>
 +
<li>Simulation of the diffusion of NGF in a given environment</li>
 +
<li>Neurons growth in the presence of NGF</li>
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</ol>
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</p>
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</div>
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<div class="block separator-mark"></div>
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<div class="block title">
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<h1>The context of our model</h1>
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</div>
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<div class="block half">
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<p>Our project aims to create a biofilm composed of genetically modified E.coli able to release a neurotrophic factor: NGF. This will help to accelerate the connection between the neurons and the implant of the prothesis; hence aiming to connect directly the prothesis to the neurons of the amputee. This will enable the patient to have more natural control of his prothesis device. The nerves will be guided toward a conductive membrane surrounding our biofilm. The membrane will then pass the electric signal of the regenerated nerves toward the electronic chip of the implant through wires.  It will allow the patient to have a more instinctive and natural control than any other actual prosthesis, and a reduced reeducation time.</p>
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</div>
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<div class="block half">
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 +
</div>
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<div class="block one-third">
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</div>
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<div class="block two-third">
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<p>We will test the biofilm on a microfluidic chip as a proof of concept. The chip will be composed of two compartments: one composed of the E.Coli genetically modified to produce NGF and the other one of neurons. Both compartments will be kept alive via an input and output of nutriments. Micro canals link the two compartments in the middle of the chip, allowing the diffusion of NGF and the growth of the neurites. Our model will hence be established on a micro-fluidic chip in order to share our results with the wetlab and indicate them the optimal concentration of NGF needed according to their model.</p>
 +
</div>
 +
<div class="block two-third">
 +
<p><i>We introduce different parameters in order to create our model :</i></p>
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<table style="background-color: #fffafc; width: 100%;">
 +
<tr>
 +
<td>g</td>
 +
<td>Length of the neurite outgrowth</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
<td>Neurite outgrowth rate</td>
 +
</tr>
 +
<tr>
 +
<td>u(x,t)</td>
 +
<td>Concentration of NGF at the position x and time t</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
<td>NGF concentration gradient at the position x and time t</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
<td>Diffusion coefficient of NGF</td>
 +
</tr>
 +
<tr>
 +
<td>K</td>
 +
<td>Gradient factor (growth rate of the neurite under the stimulation of the NGF concentration gradient)</td>
 +
</tr>
 +
<tr>
 +
<td>Baseline growth rate (neurite growth rate in absence of NGF concentration gradient)</td>
 +
<td></td>
 +
</tr>
 +
<tr>
 +
<td>L</td>
 +
<td>Length of the conduit </td>
 +
</tr>
 +
 +
</table>
 +
</div>
 
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Revision as of 12:35, 23 August 2018

""

General introduction

This year we did a simulation of the growth of neurons towards ou biofilm using a mathematical model. We created a determin-istic model to help the wetlab establish the optimal concen-tration gradients of Nerve Growth Factor (NGF) needed for the regrowth of the nerves. Nerve growth factor (NGF) is one of a group of small protein like molecules called neurotrophins that are responsible for the development of new neurons, and for the health and maintenance of mature ones. The concentra-tion and concentration gradient of NGF are key parameters af-fecting the growth rate and direction of neurites and axons. Neurite growth have shown to be NGF dose-dependent. At a too low concentration the NGF does not have enough effect on the growth of the neurites whereas if the concentration is too high the risks could exceed the benefits, causing diseases. In order to visualize the results of the model on a microfluidic chip we used MATLAB, App Designer, Python, Gmsh and FreeFem. This is an important part of our project because it creates the link between the wetlab and drylab.

In order to achieve our goal, we divided our project in three parts:

  1. Production of NGF by the E.coli genetically modified
  2. Simulation of the diffusion of NGF in a given environment
  3. Neurons growth in the presence of NGF

The context of our model

Our project aims to create a biofilm composed of genetically modified E.coli able to release a neurotrophic factor: NGF. This will help to accelerate the connection between the neurons and the implant of the prothesis; hence aiming to connect directly the prothesis to the neurons of the amputee. This will enable the patient to have more natural control of his prothesis device. The nerves will be guided toward a conductive membrane surrounding our biofilm. The membrane will then pass the electric signal of the regenerated nerves toward the electronic chip of the implant through wires. It will allow the patient to have a more instinctive and natural control than any other actual prosthesis, and a reduced reeducation time.

We will test the biofilm on a microfluidic chip as a proof of concept. The chip will be composed of two compartments: one composed of the E.Coli genetically modified to produce NGF and the other one of neurons. Both compartments will be kept alive via an input and output of nutriments. Micro canals link the two compartments in the middle of the chip, allowing the diffusion of NGF and the growth of the neurites. Our model will hence be established on a micro-fluidic chip in order to share our results with the wetlab and indicate them the optimal concentration of NGF needed according to their model.

We introduce different parameters in order to create our model :

g Length of the neurite outgrowth
Neurite outgrowth rate
u(x,t) Concentration of NGF at the position x and time t
NGF concentration gradient at the position x and time t
Diffusion coefficient of NGF
K Gradient factor (growth rate of the neurite under the stimulation of the NGF concentration gradient)
Baseline growth rate (neurite growth rate in absence of NGF concentration gradient)
L Length of the conduit