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. </p>
+
         <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.
 +
We simulated our equations for cI Protein, sgRNA and anti-CRISPR, shown in Figure 1. </p>
 
<figure>
 
<figure>
 
<div class="figures">
 
<div class="figures">
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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="200px"></div>
 
    <div class="image"><img src="https://static.igem.org/mediawiki/2017/2/23/T--Toronto--2017_ci_anti.png" alt="data" width="200px"></div>
 
<figcaption>Figure 1:<br>
 
<figcaption>Figure 1:<br>
A)<b>cI Protein Simulation</b> Lower cI protein concentrations in the dark (LacILOV is bound, Eq. 1)<br>
+
A) <b>cI Protein Simulation</b> Lower cI protein concentrations in the dark (LacILOV is bound, Eq. 1)<br>
B)<b>sgRNA Simulation</b> Lower sgRNA protein concentrations in the dark (LacILOV is bound, Eq. 2)<br>
+
B) <b>sgRNA Simulation</b> Lower sgRNA protein concentrations in the dark (LacILOV is bound, Eq. 2)<br>
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>
 
</div>
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<div id="subsection-Analysis" class="subsection">
 
<div id="subsection-Analysis" class="subsection">
 
<h2 class="text-yellow">R Analysis</h2>
 
<h2 class="text-yellow">R Analysis</h2>
 +
<p>Analyzed in R for this model, and got the following values with adjusted R-squared and p-value: </p>
 
<blockquote class="code">
 
<blockquote class="code">
  <pre>R Analysis:
 
Call:
 
lm(formula = log(x) ~ c(time, time, time))
 
 
Residuals:
 
    Min      1Q  Median      3Q      Max
 
-0.58853 -0.15536  0.01303  0.19867  0.44055
 
 
 
Coefficients:
 
Coefficients:
 
                     Estimate Std. Error t value Pr(>|t|)
 
                     Estimate Std. Error t value Pr(>|t|)
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</blockquote>
 
</blockquote>
 
<p>Intercept represents the equilibrium value of LacILov, our intercept:</p>
 
<p>Intercept represents the equilibrium value of LacILov, our intercept:</p>
<figure>
+
\begin{eqnarray}
  <div class="figures">
+
2.879199 \pm (0.21773)(2.026) \\
    <div class="image"><img src="https://static.igem.org/mediawiki/2017/f/f4/T--Toronto--2017_intercept.png" alt="data"></div>
+
2.879199 \pm 0.44112098
</div>
+
\end{eqnarray}
</figure>
+
 
</div>
 
</div>
 
</div>
 
</div>

Revision as of 02:11, 16 December 2017

Analysis

MathWorks Simulations

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. 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, 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

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

Visit our GitHub repositoryfor our code.

data
Figure 2.a: Log Linear transformation of RFU/OD600 vs Time, Regression Line (red) fitted to data
data
Figure 2.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, our intercept:

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