Difference between revisions of "Team:Toronto/Analysis"

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<div id="subsection-Introduction" class="subsection">
 
<div id="subsection-Introduction" class="subsection">
 
<h2 class="text-yellow">MathWorks Simulations</h2>
 
<h2 class="text-yellow">MathWorks Simulations</h2>
         <p>Using the previously derived expressions from our ODEs, we use the Mathworks Simulink package to derive solutions to our system and model our system for a range of parameters.
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         <p>Using the previously derived expressions from our ODEs, we simulated our equations for cI Protein, sgRNA and anti-CRISPR, shown in Figure 1. </p>
We simulated our equations for cI Protein, sgRNA and anti-CRISPR, shown in Figure 1. </p>
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<h3>Equations 1, 2, 3</h3>
 +
\begin{eqnarray}
 +
\frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \\
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\frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \\
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\frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda
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\end{eqnarray}
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<figure>
 
<figure>
 
<div class="figures">
 
<div class="figures">
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C) <b>anti-CRISPR Simulation</b> Anti-CRISPR expression inversely proportional to LacILOV activation, Eq. 3)<br>
 
C) <b>anti-CRISPR Simulation</b> Anti-CRISPR expression inversely proportional to LacILOV activation, Eq. 3)<br>
 
D) <b>anti-CRISPR vs cI Protein</b>Anti-CRISPR protein concentration increases in lower cI concentration</figcaption>
 
D) <b>anti-CRISPR vs cI Protein</b>Anti-CRISPR protein concentration increases in lower cI concentration</figcaption>
 +
</div>
 +
</figure>
 +
 +
<p> We then used the Mathworks Simulink package to derive solutions to our system and model our system for a range of parameters.</p>
 +
<figure>
 +
<div class="figures">
 +
<div class="image"><img src="https://static.igem.org/mediawiki/2017/8/8a/T--Toronto--2017_x2_light_on.svg" alt="data" width="300px"></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="300px"></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="300px"></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="300px"></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="300px"></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="300px"></div>
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<figcaption>Figure 2 Legend:<br>
 +
x_2 = cI Protein <br>
 +
\alpha = maximum transcription rate <br>
 +
\gamma = degradation rate <br>
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\lambda = anti-CRISPR <br>
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\theta = sgRNA</figcaption>
 
</div>
 
</div>
 
</figure>
 
</figure>
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<div id="subsection-Plots" class="subsection">
 
<div id="subsection-Plots" class="subsection">
 
<h2 class="text-yellow">R plots</h2>
 
<h2 class="text-yellow">R plots</h2>
<p><a href="https://github.com/igemuoftATG/drylab-matlab">GitHub repository</a> contains all our code for the following R analysis, as well as for generating the above simulations. </p>
+
<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>
 
<figure>
 
<figure>
 
<div class="figures">
 
<div class="figures">
 
<div class="image"><img src="https://static.igem.org/mediawiki/2017/6/66/T--Toronto--2017_mcherr_reg_log.png" alt="data"></div>
 
<div class="image"><img src="https://static.igem.org/mediawiki/2017/6/66/T--Toronto--2017_mcherr_reg_log.png" alt="data"></div>
 
</div>
 
</div>
<figcaption>Figure 2.a: Log Linear transformation of RFU/OD600 vs Time, Regression Line (red) fitted to data</figcaption>
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<figcaption>Figure 3.a: Log Linear transformation of RFU/OD600 vs Time, Regression Line (red) fitted to data</figcaption>
 
</figure>
 
</figure>
 
<figure>
 
<figure>
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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>
 
<div class="image"><img src="https://static.igem.org/mediawiki/2017/4/42/T--Toronto--2017_mcherry-reg-norm.png" alt="data"></div>
 
</div>
 
</div>
<figcaption>Figure 2.b: RFU/OD600 vs Time with Transformed Regression Line (red)</figcaption>
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<figcaption>Figure 3.b: RFU/OD600 vs Time with Transformed Regression Line (red)</figcaption>
 
</figure>
 
</figure>
 
</div>
 
</div>

Revision as of 02:48, 16 December 2017

Analysis

MathWorks Simulations

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

Equations 1, 2, 3

\begin{eqnarray} \frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \\ \frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \\ \frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda \end{eqnarray}
data
data
data
data
Figure 1:
A) cI Protein Simulation Lower cI protein concentrations in the dark (LacILOV is bound, Eq. 1)
B) sgRNA Simulation Lower sgRNA protein concentrations in the dark (LacILOV is bound, Eq. 2)
C) anti-CRISPR Simulation Anti-CRISPR expression inversely proportional to LacILOV activation, Eq. 3)
D) anti-CRISPR vs cI ProteinAnti-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 Legend:
x_2 = cI Protein
\alpha = maximum transcription rate
\gamma = degradation rate
\lambda = anti-CRISPR
\theta = sgRNA

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}