http://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&feed=atom&action=historyTeam:NTU-Taida/Modeling/Single-Cell - Revision history2024-03-29T12:32:47ZRevision history for this page on the wikiMediaWiki 1.16.0http://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=293002&oldid=prevShihyi: /* Motivation */2012-10-27T00:16:26Z<p><span class="autocomment">Motivation</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==Motivation==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==Motivation==</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Inferring from our circuit design, we expect to see an anti-noise response of GLP1 in spite of the fluctuating level of fatty acid. However, since the overall response of GLP1 strongly depends on the actual dynamics and equilibrium state of the species involved in the network, we are not able to confirm the anti-noise function without a detailed, quantitative analysis. Furthermore, the overall sensing threshold of fatty acid in our circuit plays a critical role in the function of our system, determining if our system is able to distinguish between the basal fatty acid level (the noise) and the FA level after food intake (the signal) and respond properly. Therefore, we performed single cell model to confirm the function of our circuit and examine the threshold of our high pass filter. </div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Inferring from our circuit design, we expect to see an anti-noise response of GLP1 in spite of the fluctuating level of fatty acid. However, since the overall response of GLP1 strongly depends on the actual dynamics and equilibrium state of the species involved in the network, we are not able to confirm the <ins class="diffchange diffchange-inline">'''</ins>anti-noise function<ins class="diffchange diffchange-inline">''' </ins>without a detailed, quantitative analysis. Furthermore, the overall sensing threshold of fatty acid in our circuit plays a critical role in the function of our system, determining if our system is able to distinguish between the basal fatty acid level (the noise) and the FA level after food intake (the signal) and respond properly. Therefore, we performed single cell model to confirm the function of our circuit and examine the <ins class="diffchange diffchange-inline">'''</ins>threshold of our high pass filter<ins class="diffchange diffchange-inline">'''</ins>.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==Overview==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==Overview==</div></td></tr>
</table>Shihyihttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292956&oldid=prevCharlotte torng: /* Sensing Module */2012-10-27T00:14:37Z<p><span class="autocomment">Sensing Module</span></p>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>There were not much existing literature about the exact parameters of betaFA and betaFadR, only the overall relationship between the input FA and the expression of genes controlled by pFadR. Therefore, we fit our parameters to the experimental data shown in a recent study, which has a sensing threshold of approximately 200 uM to find the value of betaFA and betaFadR.<br /></div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>There were not much existing literature about the exact parameters of betaFA and betaFadR, only the overall relationship between the input FA and the expression of genes controlled by pFadR. Therefore, we fit our parameters to the experimental data shown in a recent study <ins class="diffchange diffchange-inline">[[#Ref|[1]]]</ins>, which has a sensing threshold of approximately 200 uM to find the value of betaFA and betaFadR.<br /></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>The resulting response curves of LuxI and TetR1 are shown below.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>The resulting response curves of LuxI and TetR1 are shown below.</div></td></tr>
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</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292943&oldid=prevCharlotte torng: /* Double Repressor Module */2012-10-27T00:13:58Z<p><span class="autocomment">Double Repressor Module</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>===Double Repressor Module===</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>===Double Repressor Module===</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Recent studies have shown that longer transcriptional cascades make the steady-state switching behavior sharper<del class="diffchange diffchange-inline">.</del>[[#Ref|[2]]] Therefore, we connect our sensing module to the double repressor module, which is essentially a two stage transcriptional cascade, and expect the output of double repressor module, GLP1, to display sharper transition between steady states than output of the sensing module TetR1 or LuxI. To examine this, we use Hill functions to describe each step in the double repressor cascade.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Recent studies have shown that longer transcriptional cascades make the steady-state switching behavior sharper[[#Ref|[2]]]<ins class="diffchange diffchange-inline">. </ins>Therefore, we connect our sensing module to the double repressor module, which is essentially a two stage transcriptional cascade, and expect the output of double repressor module, GLP1, to display sharper transition between steady states than output of the sensing module TetR1 or LuxI. To examine this, we use Hill functions to describe each step in the double repressor cascade.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}LacI}{\text{d}t}=\frac{\alpha _{LacI}}{1+(\frac{TetR_{1}+TetR_{2}}{\beta_{TetR}})^{n5}}-\gamma_{LacI}\times LacI$$</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}LacI}{\text{d}t}=\frac{\alpha _{LacI}}{1+(\frac{TetR_{1}+TetR_{2}}{\beta_{TetR}})^{n5}}-\gamma_{LacI}\times LacI$$</div></td></tr>
</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292927&oldid=prevCharlotte torng: /* Double Repressor Module */2012-10-27T00:13:06Z<p><span class="autocomment">Double Repressor Module</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>===Double Repressor Module===</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>===Double Repressor Module===</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Recent studies have shown that longer transcriptional cascades make the steady-state switching behavior sharper.[[#Ref|[<del class="diffchange diffchange-inline">1</del>]]] Therefore, we connect our sensing module to the double repressor module, which is essentially a two stage transcriptional cascade, and expect the output of double repressor module, GLP1, to display sharper transition between steady states than output of the sensing module TetR1 or LuxI. To examine this, we use Hill functions to describe each step in the double repressor cascade.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Recent studies have shown that longer transcriptional cascades make the steady-state switching behavior sharper.[[#Ref|[<ins class="diffchange diffchange-inline">2</ins>]]] Therefore, we connect our sensing module to the double repressor module, which is essentially a two stage transcriptional cascade, and expect the output of double repressor module, GLP1, to display sharper transition between steady states than output of the sensing module TetR1 or LuxI. To examine this, we use Hill functions to describe each step in the double repressor cascade.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}LacI}{\text{d}t}=\frac{\alpha _{LacI}}{1+(\frac{TetR_{1}+TetR_{2}}{\beta_{TetR}})^{n5}}-\gamma_{LacI}\times LacI$$</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}LacI}{\text{d}t}=\frac{\alpha _{LacI}}{1+(\frac{TetR_{1}+TetR_{2}}{\beta_{TetR}})^{n5}}-\gamma_{LacI}\times LacI$$</div></td></tr>
</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292904&oldid=prevCharlotte torng: /* Quorum Sensing Module */2012-10-27T00:12:18Z<p><span class="autocomment">Quorum Sensing Module</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}AHL_{e}}{\text{d}t}=-kse\times AHL_{e}+\eta_{Ext}\times(AHL_{i}-AHL_{e})$$</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}AHL_{e}}{\text{d}t}=-kse\times AHL_{e}+\eta_{Ext}\times(AHL_{i}-AHL_{e})$$</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>The dimerization of AHL in the cells with LuxR result in R complex, with LuxR constitutively expressed at a high level of <del class="diffchange diffchange-inline">10uM</del>. </div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>The dimerization of AHL in the cells with LuxR result in R complex, with LuxR constitutively expressed at a high level of <ins class="diffchange diffchange-inline">10 uM</ins>. </div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}R}{\text{d}t}=\rho\times (LuxR)^{2} \times (AHL_{i})^{2}-\gamma_{R}\times R$$</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}R}{\text{d}t}=\rho\times (LuxR)^{2} \times (AHL_{i})^{2}-\gamma_{R}\times R$$</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}TetR_{2}}{\text{d}t}=\alpha_{TetR_{2}}\times \frac{R^{n3}}{\beta^{n3}_{R}+R^{n3}}-\gamma_{TetR_{2}}\times TetR_{2}$$</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>$$\frac{\text{d}TetR_{2}}{\text{d}t}=\alpha_{TetR_{2}}\times \frac{R^{n3}}{\beta^{n3}_{R}+R^{n3}}-\gamma_{TetR_{2}}\times TetR_{2}$$</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>In figure 6, we can see that the level of AHLi, AHLe and R rise immediately as FA appears at time=0 (FA level is not shown in the figure, FA is added at time=0 at level of <del class="diffchange diffchange-inline">10mM</del>, which is at the order of normal fatty acid after food intake, and persists for the whole time span in the figure).</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>In figure 6, we can see that the level of AHLi, AHLe and R rise immediately as FA appears at time=0 (FA level is not shown in the figure, FA is added at time=0 at level of <ins class="diffchange diffchange-inline">2 mM</ins>, which is at the order of normal fatty acid after food intake, and persists for the whole time span in the figure).</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[[FIle:NTU-Taida-Model-Single-fig6.png|450px|center|thumb|Figure 6]]</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[[FIle:NTU-Taida-Model-Single-fig6.png|450px|center|thumb|Figure 6]]</div></td></tr>
</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292867&oldid=prevCharlotte torng: /* Filter */2012-10-27T00:10:50Z<p><span class="autocomment">Filter</span></p>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>We simulate the response of output GLP to different input fatty acid concentration level to see if our circuit behaves as a high-pass filter. Figure 7 shows the result when we first performed the single cell analysis. We can see that there is indeed a sharp high pass filter, but with a relatively small filter threshold. Since the baseline concentration of fatty acid is shown in literature <del class="diffchange diffchange-inline">[[#Ref|[2]]] </del>to be about <del class="diffchange diffchange-inline">200uM </del>, we want to set the threshold to be <del class="diffchange diffchange-inline">larger than 500uM </del>so as to filter out the baseline noise. With the help of [[Team:NTU-Taida/Modeling/System-Analysis|system analysis]], we adjust the parameters and are able to tune the threshold to our desired level, as shown in figure 8. As a result, single cell modeling confirms that our circuit can serve as a high-pass filter with tunable threshold.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>We simulate the response of output GLP to different input fatty acid concentration level to see if our circuit behaves as a high-pass filter. Figure 7 shows the result when we first performed the single cell analysis. We can see that there is indeed a sharp high pass filter, but with a relatively small filter threshold. Since the baseline concentration of fatty acid is shown in literature to be about <ins class="diffchange diffchange-inline">400uM </ins>, we want to set the threshold to be <ins class="diffchange diffchange-inline">around 800 uM </ins>so as to filter out the baseline noise. With the help of [[Team:NTU-Taida/Modeling/System-Analysis|system analysis]], we adjust the parameters and are able to tune the threshold to our desired level, as shown in figure 8. As a result, single cell modeling confirms that our circuit can serve as a high-pass filter with tunable threshold.</div></td></tr>
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</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292825&oldid=prevCharlotte torng: /* Transition to return to equilibrium */2012-10-27T00:09:05Z<p><span class="autocomment">Transition to return to equilibrium</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>====Transition to return to equilibrium====</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>====Transition to return to equilibrium====</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Figure 9C shows the response of GLP1 when FA input is removed. After the input disappears, our system displays sustained GLP1 response for about <del class="diffchange diffchange-inline">750 </del>minutes due to the effect of the quorum sensing system, as described in the next part.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Figure 9C shows the response of GLP1 when FA input is removed. After the input disappears, our system displays sustained GLP1 response for about <ins class="diffchange diffchange-inline">350 </ins>minutes due to the effect of the quorum sensing system, as described in the next part.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>===Prolonged GLP1 response due to quorum sensing system ===</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>===Prolonged GLP1 response due to quorum sensing system ===</div></td></tr>
</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292803&oldid=prevCharlotte torng: /* Prolonged GLP1 response due to quorum sensing system */2012-10-27T00:07:47Z<p><span class="autocomment">Prolonged GLP1 response due to quorum sensing system</span></p>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Although the effect of quorum sensing is mainly seen in behavior of a cell population, we show that by incorporating the quorum sensing system, the duration of sustained GLP1 level after the disappearance of fatty acid dramatically increases. The comparison of the GLP1 response after FA level falls between systems with and without the quorum sensing module is shown in figure 10. With the quorum sensing module, the duration increases from 50 min to <del class="diffchange diffchange-inline">750 </del>min, providing prolonged GLP1 response, as desired.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Although the effect of quorum sensing is mainly seen in behavior of a cell population, we show that by incorporating the quorum sensing system, the duration of sustained GLP1 level after the disappearance of fatty acid dramatically increases. The comparison of the GLP1 response after FA level falls between systems with and without the quorum sensing module is shown in figure 10. With the quorum sensing module, the duration increases from 50 min to <ins class="diffchange diffchange-inline">350 </ins>min, providing prolonged GLP1 response, as desired.</div></td></tr>
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</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=292779&oldid=prevCharlotte torng: /* Reference */2012-10-27T00:06:35Z<p><span class="autocomment">Reference</span></p>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==Reference==</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>==Reference==</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><li id='Ref1'>Sara Hooshangi , Stephan Thiberge, and Ron Weiss, Ultrasensitive and noise propagation in a synthetic transcriptional cascade , pnas, vol. 102, no.10, 3581-3586, </div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><li id='Ref1<ins class="diffchange diffchange-inline">'>Zhang F, et al. (2012) Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nature Biotechnology 30(4):354-9.</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline"><li id='Ref2</ins>'>Sara Hooshangi , Stephan Thiberge, and Ron Weiss, Ultrasensitive and noise propagation in a synthetic transcriptional cascade , pnas, vol. 102, no.10, 3581-3586, </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>8 2005.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>8 2005.</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><del style="color: red; font-weight: bold; text-decoration: none;"><li id='Ref2'>C K Abrams, M Hamosh, V S Hubbard, S K Dutta, and P Hamosh, Lingual Lipase in Cystic Fibrosis – Quantification of Enzyme activity in the upper small intestine of patients with exocrine pancreatic insufficiency, JCI, 73(2): 374–382, 2 1984.</li></del></div></td><td colspan="2"> </td></tr>
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</table>Charlotte tornghttp://2012.igem.org/wiki/index.php?title=Team:NTU-Taida/Modeling/Single-Cell&diff=290601&oldid=prevShihyi: /* Temporal response to FA input */2012-10-26T22:11:26Z<p><span class="autocomment">Temporal response to FA input</span></p>
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