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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> | ||
− | |||
<h3>Equations 1, 2, 3</h3> | <h3>Equations 1, 2, 3</h3> | ||
\begin{eqnarray} | \begin{eqnarray} | ||
− | \frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \\ | + | \frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \tag{(1), Fig. 1.A}\\ |
− | \frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \\ | + | \frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \tag{(2), Fig. 1.B}\\ |
− | \frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda | + | \frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda \tag{(3), Fig. 1.C} |
\end{eqnarray} | \end{eqnarray} | ||
+ | |||
+ | <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> | ||
<figure> | <figure> | ||
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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> | <div class="image"><img src="https://static.igem.org/mediawiki/2017/2/23/T--Toronto--2017_ci_anti.png" alt="data" width="300px"></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 | + | A) <b>cI Protein Simulation</b> Lower cI protein concentrations in the dark (LacILOV is bound)<br> |
− | B) <b>sgRNA Simulation</b> Lower sgRNA protein concentrations in the dark (LacILOV is bound | + | B) <b>sgRNA Simulation</b> Lower sgRNA protein concentrations in the dark (LacILOV is bound)<br> |
− | C) <b>anti-CRISPR Simulation</b> Anti-CRISPR expression inversely proportional to LacILOV activation | + | C) <b>anti-CRISPR Simulation</b> Anti-CRISPR expression inversely proportional to LacILOV activation<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> | ||
</figure> | </figure> | ||
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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> | <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> | ||
<figcaption>Figure 2 Legend:<br> | <figcaption>Figure 2 Legend:<br> | ||
− | + | <i>x<sub>2</sub></i> = cI Protein <br> | |
− | + | <i>α</i> = maximum transcription rate <br> | |
− | + | <i>γ</i> = degradation rate <br> | |
− | + | <i>λ</i> = anti-CRISPR <br> | |
− | + | <i>θ</i> = sgRNA</figcaption> | |
</div> | </div> | ||
</figure> | </figure> |
Revision as of 02:55, 16 December 2017
Analysis
MathWorks Simulations
Equations 1, 2, 3
\begin{eqnarray} \frac{dx_2}{d\tau} = \psi_1 - \gamma_2 x_2 \tag{(1), Fig. 1.A}\\ \frac{d\theta}{d\tau} = k\psi_1 - \gamma_\theta \theta \tag{(2), Fig. 1.B}\\ \frac{d\lambda}{d\tau} = \frac{\alpha_\lambda}{1+x_2^n} - \gamma_\lambda \lambda \tag{(3), 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.
We then used the Mathworks Simulink package to derive solutions to our system and model our system for a range of parameters.
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.
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}