Difference between revisions of "Team:UNOTT/Model"

 
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<center> <h1 class="box_header1"> Modelling
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<h6>When developing Key.Coli, we found it was important to mathematically model possible situations so we could investigate the effects of different situations we might encounter as well as representing every single chemical reaction which could expect.</h6>
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<p>This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of Python and Tellurium to create easy to read graphs. <p>
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<p>The models weren't perfect as first: refinement from lab results helped to optimize and correct the models</p>
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<p>Our aims when creating the models were the following:</p>
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<li> To assist the processes within the Wet Lab by informing them and allowing for simulations. This would be especially useful when predicting the required fluorescence </li>
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<li> Test our biological systems with conditions that might not be possible to replicate in a lab environment. This allows us to future proof our methods as well as identify any vulnerabilities </li> 
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<h3> Single gene expressing protein and protein undergoing degradation </h3>
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<h5> Using the Law of Mass Action and assuming there is no radioactive decay </h5>
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<h3 class="box_header">Overview</h3>
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<p>When developing <i> Key. coli</i>, we found it was important to mathematically model possible situations in order to investigate the effects of different situations that we might encounter throughout different stages of development as well as during implementation. </p>
 +
 +
<p>A major problem the project faced is that the comparison process of the fluorescence proteins wouldn't be possible to be investigated with all combinations as it would take too long. </p>
 +
<p> To answer this problem, the team attempted to model the fluorescence spectra over time expressed by each different protein. First, the type of gene expression was identified and then, the model was refined to take into account gene inhibition (whether the gene is expressed or not) and finally, applied over time to see how much expression would occur at a certain time period. The team used mathematical modelling such as Ordinary Differential Equations because they were easy to convert into programming in order to build components for the simulation.</p>
 +
 +
<p> This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of programming to create visual graphs and simulations, as well as development of tools to allow for comparison between fluorescence levels without needing to actually create more synthetic organisms. One advantage of this was it allowed for data to be easily read and understood by the team, rather than reading a wall of numbers. Another advantage is that this is far quicker than creating these results in the lab. </p>
 +
 +
<p> One limitation of models the team found out that they were too high level to accurately predict and represent all the processes that would be undertaken during the random constructions of the fluorescent proteins. This is an issue because this means the models weren't perfect to describe the real life, which however, suggests, they could undergo more refining and improving. </p>
 +
 +
<p> Software was developed to compare the fluorescence levels of the key colony with the mother colony to check if there was a high enough degree of similarity. The mother colony is defined as the colony of bacteria that is securely kept within the facility and whose fluorescence acts as a verification for the key colony, which is defined as the bacteria which is taken from the mother colony and given to a person who own's a Key.Coli container. </p>
 +
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<p> As a side project, the team investigated into whether our method is random and unique by investigating how many combinations we could make and whether we could accurately predict which combination will occur. </p>
  
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<h3> Signal Dependent Expression </h3>
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<h5> Where S is Signal Dependent and Ko is a low basal expression </h5>
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  <h3 class="box_header">Modelling Aims </h3>
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<h3> Relationship between Fluorescent Intensity and GFP Intensity </h3>
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<h5> CHANGE IT TRANSFORMED INSTEAD OF INFECTED. Reference:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430915/ </h5>
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<p> The first aim is to assist the processes within the wet lab by informing them of what spectra they can expect through the use of simulations. This would be especially useful when predicting the required fluorescence </p> 
−
<img src="https://static.igem.org/mediawiki/2017/b/b2/T--UNOTT--Formula1.png" style= position: fixed; align=center;>
+
<p> The second is to test our biological systems with conditions that might not be possible to replicate in a lab environment. This allows us to future proof our methods as well as identify any vulnerabilities further down the line as well as save time and money on testing these conditions, if we were to. </p>  
−
<img src="https://static.igem.org/mediawiki/2017/c/c7/T--UNOTT--Graph2.png" style= position: fixed; align=center;>
+
<p>In order to achieve these aims, we decided on an end goal of writing a Simulation for measuring Fluorescence Intensity when given parameters such as protein concentrations and wavelengths of lasers. </p>
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</center>
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<br></br>
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<p style="text-align: center;" > <a href="https://2017.igem.org/Team:UNOTT/Modelling">Find out more about our modeling</a> </p>
 +
<p style="text-align: center;" > <a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/tree/master/Models">The source code for these models can be accessed from our GitHub page</a> </p>
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</div>
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<div class="box">
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  <h3 class="box_header">Software Aims</h3>
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  <div class="box_content">
 +
<p> The main objective of software was to check between fluorescence levels during implementation of Key.Coli between the mother colony and the Key.Coli capsules. </p>
 +
<p> In order to achieve this objective, the team decided to investigate into image comparison and raw data comparison with data from the fluorescence reader. These were programmed using data from the models and a threshold value was set using data from the wet lab to ensure not just any file being compared would get verified. </p>
 +
 
 +
<p> A strong emphasis was placed on effectiveness of the software and efficiency; we wanted the software to run on low end hardware but still be useful without compromises. </p>
 +
<br> </br>
 +
<p style="text-align: center;" > <a href="https://2017.igem.org/Team:UNOTT/Software">Find out more about our software </a> </p>
 +
<p style="text-align: center;" > <a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/tree/master/Software">Our software can be downloaded from our GitHub page</a> </p>
 +
  </div>
 
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</div>
  
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<div class="box">
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  <h3 class="box_header">Parameters</h3>
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  <div class="box_content">
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<p> Inputs and Outputs </p>
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<table style="width:100%">
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  <tr>
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    <th>Parameter</th>
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    <th>Unit</th>
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  </tr>
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  <tr>
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    <td>Protein Concentration</td>
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    <td>ug / mol </td>
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  </tr>
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  <tr>
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    <td>mRNA Concentration</td>
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    <td>ug / mol </td>
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  </tr>
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  <tr>
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    <td>Fluorescence</td>
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    <td>RFU (Relative Fluorescence Unit) </td>
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  </tr>
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  <tr>
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    <td>Time</td>
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    <td>Hours / Hrs</td>
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  </tr>
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</table>
  
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<p> Rates of change </p>
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<table style="width:100%">
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  <tr>
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    <th>Parameter</th>
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    <th>Unit</th>
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  </tr>
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  <tr>
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    <td>Protein Degradation</td>
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    <td>Nanograms per microlitre per minute</td>
 +
  </tr>
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<tr>
 +
    <td>mRNA Degradation</td>
 +
    <td>Nanograms per microlitre per minute</td>
 +
  </tr>
 +
<tr>
 +
    <td>Translation </td>
 +
    <td>Number of protein molecules produced per mRNA molecule, per unit of time</td>
 +
  </tr>
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<tr>
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    <td>Transcription </td>
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    <td>Number of mRNA molecules produced per gene, per unit of time.</td>
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  </tr>
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</table>
  
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Latest revision as of 02:29, 2 November 2017

</DIV>



Modelling    

 

 

Overview

When developing Key. coli, we found it was important to mathematically model possible situations in order to investigate the effects of different situations that we might encounter throughout different stages of development as well as during implementation.

A major problem the project faced is that the comparison process of the fluorescence proteins wouldn't be possible to be investigated with all combinations as it would take too long.

To answer this problem, the team attempted to model the fluorescence spectra over time expressed by each different protein. First, the type of gene expression was identified and then, the model was refined to take into account gene inhibition (whether the gene is expressed or not) and finally, applied over time to see how much expression would occur at a certain time period. The team used mathematical modelling such as Ordinary Differential Equations because they were easy to convert into programming in order to build components for the simulation.

This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of programming to create visual graphs and simulations, as well as development of tools to allow for comparison between fluorescence levels without needing to actually create more synthetic organisms. One advantage of this was it allowed for data to be easily read and understood by the team, rather than reading a wall of numbers. Another advantage is that this is far quicker than creating these results in the lab.

One limitation of models the team found out that they were too high level to accurately predict and represent all the processes that would be undertaken during the random constructions of the fluorescent proteins. This is an issue because this means the models weren't perfect to describe the real life, which however, suggests, they could undergo more refining and improving.

Software was developed to compare the fluorescence levels of the key colony with the mother colony to check if there was a high enough degree of similarity. The mother colony is defined as the colony of bacteria that is securely kept within the facility and whose fluorescence acts as a verification for the key colony, which is defined as the bacteria which is taken from the mother colony and given to a person who own's a Key.Coli container.

As a side project, the team investigated into whether our method is random and unique by investigating how many combinations we could make and whether we could accurately predict which combination will occur.

Modelling Aims

The first aim is to assist the processes within the wet lab by informing them of what spectra they can expect through the use of simulations. This would be especially useful when predicting the required fluorescence

The second is to test our biological systems with conditions that might not be possible to replicate in a lab environment. This allows us to future proof our methods as well as identify any vulnerabilities further down the line as well as save time and money on testing these conditions, if we were to.

In order to achieve these aims, we decided on an end goal of writing a Simulation for measuring Fluorescence Intensity when given parameters such as protein concentrations and wavelengths of lasers.



Find out more about our modeling

The source code for these models can be accessed from our GitHub page

Software Aims

The main objective of software was to check between fluorescence levels during implementation of Key.Coli between the mother colony and the Key.Coli capsules.

In order to achieve this objective, the team decided to investigate into image comparison and raw data comparison with data from the fluorescence reader. These were programmed using data from the models and a threshold value was set using data from the wet lab to ensure not just any file being compared would get verified.

A strong emphasis was placed on effectiveness of the software and efficiency; we wanted the software to run on low end hardware but still be useful without compromises.



Find out more about our software

Our software can be downloaded from our GitHub page

Parameters

Inputs and Outputs

Parameter Unit
Protein Concentration ug / mol
mRNA Concentration ug / mol
Fluorescence RFU (Relative Fluorescence Unit)
Time Hours / Hrs

Rates of change

Parameter Unit
Protein Degradation Nanograms per microlitre per minute
mRNA Degradation Nanograms per microlitre per minute
Translation Number of protein molecules produced per mRNA molecule, per unit of time
Transcription Number of mRNA molecules produced per gene, per unit of time.