Difference between revisions of "Team:Toronto/Analysis"

 
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<div class="section">
 
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<div class="container header" id="yellow">
 
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<h1>Description</h1>
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<h1>Analysis</h1>
 
</div>
 
</div>
 
</div>
 
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<!-- subsection 1 -->
 
<!-- subsection 1 -->
<div class="subsection">
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<div id="subsection-Introduction" class="subsection">
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<h2 class="text-yellow">MathWorks Simulations</h2>
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<h3>Equations 1, 2, 3</h3>
 +
\begin{eqnarray}
 +
\frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \tag{Fig. 1.A}\\
 +
\frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \tag{Fig. 1.B}\\
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\frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda \tag{Fig. 1.C}
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\end{eqnarray}
  
<h2 class="text-yellow">Summary</h2>
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        <p>Using the previously derived expressions from our ODEs, restated above, we simulated our equations for cI Protein, sgRNA and anti-CRISPR, shown in Figure 1. </p>
  
        <p>Our project this year is to quantitatively model out lacILov system with ODE’s. It follows the methodology applied in Timoth S gardners paper “A genetic toggle switch in ecoli” 2005. We began by abstracting away the details of specific promoters and repressors (figure 1) to get a simplified view of the interactions of our system. Afterwards we modeled the interactions through a set of first order ordinary differential equations. Using various assumptions to reduce the number of equations and parameters, along with the application of nondimensionalization we obtained our final result:</p>
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<figure>
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<div class="figures">
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/7/7b/T--Toronto--2017_CI.png" alt="data" width="300px"></div>
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    <div class="image"><img src="https://static.igem.org/mediawiki/2017/c/c3/T--Toronto--2017_sgRNA.png" alt="data" width="300px"></div>
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/5/54/T--Toronto--2017_anti_crispr.png" alt="data" width="300px"></div>
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    <div class="image"><img src="https://static.igem.org/mediawiki/2017/2/23/T--Toronto--2017_ci_anti.png" alt="data" width="300px"></div>
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<figcaption>Figure 1:<br>
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A) <b>cI Protein Simulation</b> Lower cI protein concentrations in the dark (LacILOV is bound)<br>
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B) <b>sgRNA Simulation</b> Lower sgRNA protein concentrations in the dark (LacILOV is bound)<br>
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C) <b>anti-CRISPR Simulation</b> Anti-CRISPR expression inversely proportional to LacILOV activation<br>
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D) <b>anti-CRISPR vs cI Protein</b> Anti-CRISPR protein concentration increases in lower cI concentration</figcaption>
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</div>
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</figure>
  
<p>iunno.svg</p>
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<p> We then used the Mathworks Simulink package to derive solutions to our system and model our system for a range of parameters.</p>
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<figure>
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<div class="figures">
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/8/8a/T--Toronto--2017_x2_light_on.svg" alt="data" width="200px"></div>
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/7/7a/T--Toronto--2017_x2_light_off.svg" alt="data" width="200px"></div>
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/f/ff/T--Toronto--2017_theta_light_on.svg" alt="data" width="200px"></div>
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/7/7a/T--Toronto--2017_theta_light_off.svg" alt="data" width="200px"></div>
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/d/d3/T--Toronto--2017_lambda_light_on.svg" alt="data" width="200px"></div>
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/3/39/T--Toronto--2017_lambda_light_off.svg" alt="data" width="200px"></div>
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<figcaption>Figure 2:<br>
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<i>x<sub>2</sub></i> = cI Protein,
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<i>&alpha;</i> = maximum transcription rate,
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<i>&gamma;</i> = degradation rate,
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<i>&theta;</i> = sgRNA,
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<i>&lambda;</i> = anti-CRISPR</figcaption>
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</div>
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</figure>
  
<p>Equations1,2,3</p>
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<p>In the first two plots, cI Protein is represented by the parameter <i>x<sub>2</sub></i>. When light is on, we see that CI protein is at maximum when degradation rate is at 0 and maximum transcription rate is at the highest. There is no transcription when degradation rate is highest and maximum transcription rate is at the lowest. </p>
 +
<p>In the second row of plots, sgRNA is represented by the parameter <i>&theta;</i>. When light is on, we get maximum concentration of sgRNA when degradation is at 0 and notably, when CI protein is high, sgRNA is also high as they are both not repressed.</p>
 +
<p>For the last row of plots, anti-CRISPR is represented by the parameter <i>&lambda;</i>. Anti-CRISPR expression is high when CI concentration is low, as CI represses anti-crispr.</p>
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</div>
  
         <p>These 3 equations encapsulate the core nature of our system. Note that all the parameters and variables have no dimensions, so our results may be generalized to other light activated systems of the same structure. Mapping our abstracted variables back to our system we see that:</p>
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<div id="subsection-solution" class="subsection">
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<h2 class="text-yellow">ODE Solution</h2>
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         <p>Solving: </p>
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\begin{eqnarray}
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\frac{x_2}{dt} = \alpha - \gamma x_2 \\
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\frac{x_2}{dt} + \gamma x_2 = \alpha
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\end{eqnarray}
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<p>Integrating Factor: </p>
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\begin{eqnarray}
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e^{\int \gamma dt} = e^{\gamma t}
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\end{eqnarray}
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<p>Multiplying both sides by our integrating factor: </p>
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\begin{eqnarray}
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(\frac{x_2}{dt} + \gamma x_2)e^{\gamma t} =  \alpha e^{\gamma t}\\
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\int (\frac{x_2}{dt} + \gamma x_2)e^{\gamma t} =  \int \alpha e^{\gamma t} \\
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x_2 = \frac{\alpha}{\gamma} + ce^{-\gamma t}
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\end{eqnarray}
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</div>
  
<h3>Equation 1</h3>
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<div id="subsection-Plots" class="subsection">
<p>Represents the rate of change of the CI repressor, whose activation depends on whether or not light is on and exhibits linear scaling with respect to its promoter strength.</p>
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<h2 class="text-yellow">R plots</h2>
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<p>Our <a href="https://github.com/igemuoftATG/drylab-matlab">GitHub repository</a> contains all our code for the following R plots and R analysis, as well as for generating the above simulations. </p>
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<figure>
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<div class="figures">
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/6/66/T--Toronto--2017_mcherr_reg_log.png" alt="data"></div>
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</div>
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<figcaption>Figure 3.a: Log Linear transformation of RFU/OD600 vs Time, Regression Line (red) fitted to data</figcaption>
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</figure>
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<figure>
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<div class="figures">
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<div class="image"><img src="https://static.igem.org/mediawiki/2017/4/42/T--Toronto--2017_mcherry-reg-norm.png" alt="data"></div>
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</div>
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<figcaption>Figure 3.b: RFU/OD600 vs Time with Transformed Regression Line (red)</figcaption>
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</figure>
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</div>
  
<h3>Equation 2</h3>
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<div id="subsection-Analysis" class="subsection">
<p>Is the rate of change of sgrna and it is important to note that from the equations, its expression is indirectly linked to the CI repressor via the psi term.</p>
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<h2 class="text-yellow">R Analysis</h2>
 
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<p>Analyzed in R for this model, and got the following values with adjusted R-squared and p-value: </p>
<h3>Equation 3</h3>
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<blockquote class="code">
<p>Is the key result of our system, it represents the rate of change of Anti-CRISPR. Our model confirms that the nature of Anti-CRISPR activation is inversely proportional to LacILov activation.</p>
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<pre>Coefficients:
 
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                    Estimate Std. Error t value Pr(>|t|)
      </div>
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(Intercept)          2.87199    0.21773  13.19 1.47e-15 ***
 +
c(time, time, time)  0.15267    0.01142  13.37 9.74e-16 ***
 +
---
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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
 +
Residual standard error: 0.2935 on 37 degrees of freedom
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Multiple R-squared:  0.8285, Adjusted R-squared:  0.8238
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F-statistic: 178.7 on 1 and 37 DF,  p-value: 9.741e-16
 +
</pre>
 +
</blockquote>
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<p>Intercept represents the equilibrium value of LacILov, and thus our intercept:</p>
 +
\begin{eqnarray}
 +
2.879199 \pm (0.21773)(2.026) \\
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2.879199 \pm 0.44112098
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\end{eqnarray}
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<li> <a href="#">Content 1</a></li>
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<li> <a href="https://2017.igem.org/Team:Toronto/ODE">ODE</a></li>
<li> <a href="#">Content 2</a></li>
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<li> <a href="https://2017.igem.org/Team:Toronto/Model">Model</a></li>
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<li> <a href="https://2017.igem.org/Team:Toronto/Design">Design</a></li>
 
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Latest revision as of 03:07, 16 December 2017

Analysis

MathWorks Simulations

Equations 1, 2, 3

\begin{eqnarray} \frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \tag{Fig. 1.A}\\ \frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \tag{Fig. 1.B}\\ \frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda \tag{Fig. 1.C} \end{eqnarray}

Using the previously derived expressions from our ODEs, restated above, we simulated our equations for cI Protein, sgRNA and anti-CRISPR, shown in Figure 1.

data
data
data
data
Figure 1:
A) cI Protein Simulation Lower cI protein concentrations in the dark (LacILOV is bound)
B) sgRNA Simulation Lower sgRNA protein concentrations in the dark (LacILOV is bound)
C) anti-CRISPR Simulation Anti-CRISPR expression inversely proportional to LacILOV activation
D) anti-CRISPR vs cI Protein Anti-CRISPR protein concentration increases in lower cI concentration

We then used the Mathworks Simulink package to derive solutions to our system and model our system for a range of parameters.

data
data
data
data
data
data
Figure 2:
x2 = cI Protein, α = maximum transcription rate, γ = degradation rate, θ = sgRNA, λ = anti-CRISPR

In the first two plots, cI Protein is represented by the parameter x2. When light is on, we see that CI protein is at maximum when degradation rate is at 0 and maximum transcription rate is at the highest. There is no transcription when degradation rate is highest and maximum transcription rate is at the lowest.

In the second row of plots, sgRNA is represented by the parameter θ. When light is on, we get maximum concentration of sgRNA when degradation is at 0 and notably, when CI protein is high, sgRNA is also high as they are both not repressed.

For the last row of plots, anti-CRISPR is represented by the parameter λ. Anti-CRISPR expression is high when CI concentration is low, as CI represses anti-crispr.

ODE Solution

Solving:

\begin{eqnarray} \frac{x_2}{dt} = \alpha - \gamma x_2 \\ \frac{x_2}{dt} + \gamma x_2 = \alpha \end{eqnarray}

Integrating Factor:

\begin{eqnarray} e^{\int \gamma dt} = e^{\gamma t} \end{eqnarray}

Multiplying both sides by our integrating factor:

\begin{eqnarray} (\frac{x_2}{dt} + \gamma x_2)e^{\gamma t} = \alpha e^{\gamma t}\\ \int (\frac{x_2}{dt} + \gamma x_2)e^{\gamma t} = \int \alpha e^{\gamma t} \\ x_2 = \frac{\alpha}{\gamma} + ce^{-\gamma t} \end{eqnarray}

R plots

Our GitHub repository contains all our code for the following R plots and R analysis, as well as for generating the above simulations.

data
Figure 3.a: Log Linear transformation of RFU/OD600 vs Time, Regression Line (red) fitted to data
data
Figure 3.b: RFU/OD600 vs Time with Transformed Regression Line (red)

R Analysis

Analyzed in R for this model, and got the following values with adjusted R-squared and p-value:

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)          2.87199    0.21773   13.19 1.47e-15 ***
c(time, time, time)  0.15267    0.01142   13.37 9.74e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2935 on 37 degrees of freedom
Multiple R-squared:  0.8285,	Adjusted R-squared:  0.8238
F-statistic: 178.7 on 1 and 37 DF,  p-value: 9.741e-16

Intercept represents the equilibrium value of LacILov, and thus our intercept:

\begin{eqnarray} 2.879199 \pm (0.21773)(2.026) \\ 2.879199 \pm 0.44112098 \end{eqnarray}