Difference between revisions of "Team:UNOTT/Modelling"

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       <h4 style="color: #ffffff; font-weight: bold; font-size: 30px;">STEP 3: Promoter Library</h4><center><img class="icons2" src="https://static.igem.org/mediawiki/2017/f/f1/T--UNOTT--Promoterpool.png" style="width:300px;height:auto;"></center>
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       <h4 style="color: #ffffff; font-weight: bold; font-size: 30px;">Absorption and Emission Wavelengths From Given Concentrations of sfGFP, mRFP & ECFP </h4>
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<p> After concluding the general scheme we would be using, the team evaluated the selection of proteins. The proteins selected for the system use fluorescence, indicating they take in a light at a certain wavelength, and re-emit it at a different wavelength. This has to be considered because it informs the wet-lab in knowing which wavelengths are required to produce a spectra as well as highlighting the importance of considering any side effects from producing the spectra such as light being reabsorbed and re-emitted at a different wavelength / color, which would result in the spectra being similar to each other rather than unique. </p>
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<p> In order to save time and program a model, the team used Semrock's Online Fluorescence graph maker <sup> 1 </sup> which operated by taking in the expected Absorption wavelengths and emitting the Emission wavelengths expected by sfGFP (green), mRFP (red) and ECFP (blue) proteins. This was done through the Web App on the website. Furthermore, they provided the raw data in a text file format which was useful as it allows the team to read the data into a stand alone program. </p>
  
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<sup> Figure 4 </sup>
  
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<img src="https://static.igem.org/mediawiki/2017/8/8f/T--UNOTT--SpectrumAbsoprtionEM.png" class="border" width="550" height="300" style= position: fixed; align=center;>
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            <p>BACK</p>
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<p>This graph tells us the emitted light is expected to be at a higher wavelength than the absorbed wavelength. This must be considered in the model as there is overlap between emitted and absorbed wavelengths implying emitted light may be absorbed and re-emitted at a higher wavelength.</p>
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<p> Fortunately, the data points used to graph the spectra is available on the website as a raw data text file which was very useful as it meant we could read the data directly into our simulator when it was being implemented. </p>
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<p> This model is important  as it guides us when using wavelengths as parameters so we know which wavelengths to use, especially when trying to create a specific color as well as what wavelengths to look out for as they might cause overlap. This was very useful to the wet-lab as it informed them of what wavelengths to use as well as what wavelength range they should use to produce different fluorescence spectra.</p>
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<p> <sup> 1 </sup> <a href=" https://www.semrock.com/searchlight-welcome.aspx ">Semrock Fluorescence Spectra Chart</a></p>
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            <h3><span>RESULTS</span></h3>
 
 
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       <h4 style="color: #ffffff; font-weight: bold; font-size: 30px;">STEP 4: Random Ligations</h4><center><img class="icons3" src="https://static.igem.org/mediawiki/2017/d/d6/T--UNOTT--Randomligations.png" style="width:300px;height:auto;"></center>
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       <h4 style="color: #ffffff; font-weight: bold; font-size: 30px;">Gene Transcription Regulation by Repressors (CRISPRi) - Concentration over Time</h4>
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<p> The next step in developing our simulation was to calculate our protein concentration at any given time when using CRISPRi. Discussion with wet-lab revealed our method would be using CRISPRi as a repressor, which works by inhibiting the expression of one or more genes by binding to the promoter region <sup> 1 </sup>. The expanded mRNA and Protein concentration models from the Constitutive Gene Expression Model <sup> 2 </sup> were modified to include the element of repression from the CRISPRi inhibition. </p>
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$$ Gene \overset{Repressor}{\rightarrow} mRNA \rightarrow Protein  $$
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$$  mRNA \underset{Degradation}{\rightarrow} \oslash  $$
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$$  sfGFP \underset{Degradation}{\rightarrow} \oslash  $$
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<h5> This change can be applied to the Law of Mass Action <sup> 3 </sup> : </h5>  
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$$ m = k_{1} \cdot \frac{k^{n}}{k^{n} + R^{n}}- d_{1}m $$
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$$ p = k_{2} m - d_{2}p $$
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<p>Where...</p>
  
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<p>m is mRNA concentration, p is Protein concentration, R is Repressor, k1 is Max Transcription Rate, k is the Repression Coefficient, n is number of repressors that need to cooperatively bind the promoter to trigger the inhibition of gene expression (Hill Coefficient), R is Repressor, d1 is mRNA degradation rate, d2 is Protein degradation rate </p>
  
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<p> The value for these constants and variables were taken from literature and calculating them <sup> 4 </sup> but later, adjusted to the lab results.</p>
  
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<sup> Figure 6 </sup>
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<p> Figure 6 shows the structure which underwent CRISPRi inhibition are expected to produce lower concentration of the protein whose expression were are inhibiting. This is important as it means the team can calculate concentration of proteins which are inhibited and compare them to the control conditions as well as giving the correct concentration for the simulation. </p>
  
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<p> Furthermore, by having a model which can calculate the protein concentration at any given time, we can deduce how much fluorescence is being emitted at that time period by the bacteria </p>
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<p> <sup> 4 </sup> See Relationship between Max Fluorescence and Protein Concentration </p>
  
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            <h3><span>RESULTS</span></h3>
 
 
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Revision as of 19:18, 31 October 2017





MODELLING

Overview

Constitutive Gene Expression For Protein and mRNA Expression over Time

Absorption and Emission Wavelengths From Given Concentrations of sfGFP, mRFP & ECFP

Gene Transcription Regulation by Repressors (CRISPRi) - Concentration over Time

The next step in developing our simulation was to calculate our protein concentration at any given time when using CRISPRi. Discussion with wet-lab revealed our method would be using CRISPRi as a repressor, which works by inhibiting the expression of one or more genes by binding to the promoter region 1 . The expanded mRNA and Protein concentration models from the Constitutive Gene Expression Model 2 were modified to include the element of repression from the CRISPRi inhibition.

$$ Gene \overset{Repressor}{\rightarrow} mRNA \rightarrow Protein $$ $$ mRNA \underset{Degradation}{\rightarrow} \oslash $$ $$ sfGFP \underset{Degradation}{\rightarrow} \oslash $$
This change can be applied to the Law of Mass Action 3 :
$$ m = k_{1} \cdot \frac{k^{n}}{k^{n} + R^{n}}- d_{1}m $$ $$ p = k_{2} m - d_{2}p $$

Where...

m is mRNA concentration, p is Protein concentration, R is Repressor, k1 is Max Transcription Rate, k is the Repression Coefficient, n is number of repressors that need to cooperatively bind the promoter to trigger the inhibition of gene expression (Hill Coefficient), R is Repressor, d1 is mRNA degradation rate, d2 is Protein degradation rate

The value for these constants and variables were taken from literature and calculating them 4 but later, adjusted to the lab results.

Figure 6



Figure 6 shows the structure which underwent CRISPRi inhibition are expected to produce lower concentration of the protein whose expression were are inhibiting. This is important as it means the team can calculate concentration of proteins which are inhibited and compare them to the control conditions as well as giving the correct concentration for the simulation.

Furthermore, by having a model which can calculate the protein concentration at any given time, we can deduce how much fluorescence is being emitted at that time period by the bacteria

4 See Relationship between Max Fluorescence and Protein Concentration