Difference between revisions of "Team:Munich/Model"

 
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<td  colspan=6 align="left">
 
<td  colspan=6 align="left">
 
<p class="introduction">
 
<p class="introduction">
Modelling in Biosciences is a powerful tool that allows us to get a deeper understanding
+
Modelling in Biosciences is a powerful tool that allows us to get a deeper understanding
of our system. It guided and assisted the design of our detection system which helped us saving a lot of time by avoiding dead-end designs.
+
of our system. It guided and assisted the design of our detection system which helped us saving a lot of time by avoiding dead-end designs. We used simple models to simulate the kinetics of our enzyme cascade to develop intuition for the design of experiments. We then used our models to optimize the design of our reaction cascades for an improved detection limit and optimal lysis times. Our scripts can be found on our <a class='myLink' href='https://github.com/igemsoftware2017/igem_munich_2017/tree/master/Modelling'>GitHub repository</a>.
Rather simple models can already give fair amount of information about a system. From the very beginning we used simple models to simulate the kinetics of
+
our enzyme cascade to develop intuition for the design of experiments. We further used our models to estimate detection limits using different cascades and reaction times.
+
</p>
 
+
</td>
        </p>
+
</td>
+
 
</tr>
 
</tr>
 
  
 
<tr><td colspan=6 align=center valign=center>
 
<tr><td colspan=6 align=center valign=center>
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One major concern when dealing with the problem of diagnostics on patients is extracting the sample with which
 
One major concern when dealing with the problem of diagnostics on patients is extracting the sample with which
 
detection can actually be performed. Since we wanted our method to be non-invasive, one concern that we needed to  
 
detection can actually be performed. Since we wanted our method to be non-invasive, one concern that we needed to  
deal with is the concentration of pathogens and thus detectable RNA in the patients mucus or non-invasive sample. First approximations from  
+
deal with is the concentration of pathogens and thus detectable RNA in the patients mucus or other non-invasive sample. First approximations from different papers already showed that virological samples show concentrations no higher than low pM and can even go as low as aM.  
different papers already showed that virological samples show concentrations no higher than low pM and can even go as low  
+
</p>
as aM. Thus, we characterised the theoretical detection limit of the Cas13a RNase activity.  
+
<p>
 
+
Our <a class='myLink' href=https://2017.igem.org/Team:Munich/Cas13a>wetlab experiments</a> indicated that the detection limit of the Cas13a RNase activity is in the range of 10 nM.  
In order to do this, we first estimated the rate constants for the different reactions to create a behavioural model.  
+
Using our kinetic data, we estimated the rate constants for the different reactions to create a simple ODE model.
The results are shown in Figure 1. It shows that the detection limit in the time range of an hour is
+
<br>The chemical and differential equations for the model are shown below:
approximately one- to two-digit nM region. Due to this result, our initial design of applying the lysed and purified RNA sample
+
</p>
directly on the detection paper strip had to be discarded. Instead, we had to explore amplification methods we could
+
perform upstream in the detection process.
+
The concentrations that can be used in Cas13a nucleotide reaction have an upper limit since it is known from literature that Cas proteins
+
show activity independent of their activation mechanism at high concentrations. This is why the concentration of Cas13a in the detection system
+
could not be increased to higher orders of magnitude. The chemical and differential equations for the model are shown below:
+
  
 
<div class="captionPicture">
 
<div class="captionPicture">
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</div>
 
</div>
  
 +
<tr class="lastRow"><td colspan=6 align=center valign=center>
 +
<table class="myTable" width=100%>
 +
<th class="leftAligned">rate constant</th>
 +
<th class=”leftAligned”>value</th>
 +
<th class="leftAligned">reference or rationale</th>
 +
<tr>
 +
<td class="leftAligned">k<sub>cr</sub></td>
 +
<td class="leftAligned">1 [1/min]</td>
 +
<td class="leftAligned">Mekler et al. (2016) Nucleic Acids Resarch</td>
 +
</tr>
 +
<tr>
 +
<td class="leftAligned">k<sub>t</sub></td>
 +
<td class="leftAligned">0.001 [1/min]</td>
 +
<td class="leftAligned">Estimated to be slow in comparison to k<sub>cr</sub></td>
 +
</tr>
 +
<tr>
 +
<td class="leftAligned">k<sub>col</sub></td>
 +
<td class="leftAligned">10 [1/min]</td>
 +
<td class="leftAligned">Estimated to be fast in comparison to k<sub>cr</sub> </td>
 +
</tr>
 +
<tr>
 +
<td class="leftAligned">k<sub>RT-RPA-Tx</sub></td>
 +
<td class="leftAligned">0.4 [1/min]</td>
 +
<td class="leftAligned">Estimated from RPA-Tx amplification experiments</td>
 +
</tr>
 +
<tr>
 +
<td class="leftAligned">K<sub>M</sub></td>
 +
<td class="leftAligned">500 [nM]</td>
 +
<td class="leftAligned">Weitz et al. (2014) Nature Chemistry</td>
 +
</tr>
 +
</table>
 +
 +
<br>
 +
<br>
 +
<p>
 +
As shown in <b>Figure 1</b>, our simulations are able to reproduce the behavior observed experimentally.
 
</p>
 
</p>
 +
 
<div class="captionPicture">
 
<div class="captionPicture">
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/1/14/T--Munich--ModellingPagePicture_kinetics_Cas13a.png" width="600">
+
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/8/87/T--Munich--ModellingPagePicture_kinetics_Cas13a_only.png" width="700">
 
<p>
 
<p>
Figure 1: Kinetics of the  Cas13a system using 1 nM concentrations of Cas13a and 10 nM crRNA at different target concentrations.  
+
Figure 1: Kinetics of Cas13a using 1 nM Cas13a and 10 nM crRNA at different target concentrations.  
 
</p>
 
</p>
 
</div>
 
</div>
 +
 +
 +
 +
<p>
 +
Next, we analysed the amount of readout RNA that was cleaved after 30 minutes for varying target concentrations. As shown in <b>Figure 2</b>, the curve follows a sigmoidal behavior and suggests a detection limit in the range of 10 nM. Due to this result, our initial design of applying the lysed and purified RNA sample directly on the detection paper strip had to be discarded. Since it is known from literature that Cas proteins show activity independent of their activation mechanism at high concentrations, we could not increase the concentration of Cas13a to improve the sensitivity. Instead, we explored amplification methods upstream in the detection process.
 +
</p>
 +
 
<div class="captionPicture">
 
<div class="captionPicture">
 
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/b/b8/T--Munich--ModellingPagePicture_CascAID.png" width="600">
 
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/b/b8/T--Munich--ModellingPagePicture_CascAID.png" width="600">
 
<p>
 
<p>
Figure 1: Theoretical Detection Limit determined for the Cas13a system using 20 nM concentrations of Cas13a and crRNA.  
+
Figure 2: Estimated detection limit determined for the Cas13a system using 1 nM Cas13a and 10 nM crRNA.  
 
</p>
 
</p>
 
</div>
 
</div>
<p>
 
Since our detector has shown to be sensitive enough to detect one- to two-digit nM concentrations of RNase alert, we employed a
 
</p>
 
  
</td>
 
</tr>
 
  
<tr><td colspan=6 align=center valign=center>
+
<h3>Improved Reaction Cascade</h3>
<h3>Estimating the Detection Limit of the Reaction Cascade</h3>
+
 
<p>   
 
<p>   
In order to compare the detection limit of the Cas13a protein kinetics by itself with the detection limit implementing the reaction cascade of reverse transcription, recombinase polymerase amplification and <i>in-vitro</i> transcription, we created a model that takes exponential amplification of the target RNA into account. This was constructed in a way
+
 
that amplification of the RNA signal takes in an exponential manner, illustrated in Figure. In order to assure that the amplification of the target RNA reaches an upper limit, we capped the amplification
+
 
at 1000 nM. As visible in Figure XXX, the detection limit of the reaction cascade decreases the detection limit by approximately three orders of magnitude.  
+
In collaboration with our wetlab team we developed a reaction cascade for sample pre-amplification by coupling reverse transcription to isothermal recombinase polymerase amplification and transcription (RT-RPA-TX), resulting in auto-catalysis of target RNA <b>(Figure 3)</b>.</p>
 +
 
 +
<div class="captionPicture">
 +
<img width=600 src="https://static.igem.org/mediawiki/2017/d/dc/T--Munich--ModellingPagePicture_RT-RPA-TX_scheme.svg" alt="RT-RPA-TX_scheme">
 +
<p>
 +
Figure 3: Scheme for the RT-RPA-TX amplification system.
 
</p>
 
</p>
 +
</div>
 +
<p>
 +
In order to compare the detection limit of the Cas13a system alone with the detection limit of the amplified the reaction cascade, we expanded our model, assuming exponential amplification of the target RNA. As the amplification reaction saturates due to a depletion of resources, the amplification stops as soon as the target RNA level reaches an upper limit of 1000 nM <b>(Figure 4)</b>.
 +
</p>
 +
 
<div class="captionPicture">
 
<div class="captionPicture">
 
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/1/16/T--Munich--ModellingPagePicture_scheme2.png" alt="RT-RPA-TX">
 
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/1/16/T--Munich--ModellingPagePicture_scheme2.png" alt="RT-RPA-TX">
 
<p>
 
<p>
Figure 3: Schematic representation of the target RNA amplification during the estimation of the detection limit using the reaction cascade.
+
Figure 4: Schematic representation of the target RNA amplification during the estimation of the detection limit using the reaction cascade.
 
</p>
 
</p>
 +
</div>
 +
<p>
 +
The kinetics for the amplfication cascade coupled to Cas13a based detection are shown in <b>Figure 5</b>. Strikingly, the start of the reaction seems to be determined by the amplificaiton reaciton, while the consecutive phase is limited by the rate of Cas13a mediated cleavage.
 +
As shown in <b>Figure 2</b>, the detection limit of the reaction cascade decreases by approximately three orders of magnitude. These simulations led us to implement our pre-amplification cascade into our CascAID system.
 +
</p>
 +
<div class="captionPicture">
 +
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/1/14/T--Munich--ModellingPagePicture_kinetics_Cas13a.png" width="600">
 +
<p>
 +
Figure 5: Kinetics of the  Cas13a systemusing 1 nM Cas13a and 10 nM crRNA at different target concentrations using the reaction cascade.
 +
</p>
 +
</div>
 +
 +
 +
</td>
 +
</tr>
  
  
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in order to release enough RNA for downstream amplification. For this, we constructed a very simplistic  
 
in order to release enough RNA for downstream amplification. For this, we constructed a very simplistic  
 
model for bacterial cell lysis. In this, we estimated the rate constants for cell lysis by common colony PCR
 
model for bacterial cell lysis. In this, we estimated the rate constants for cell lysis by common colony PCR
protocols which use a 10 minute lysis step at 95 °C for thermolysis. Thus, we considered a half-time of Bacteria
+
protocols which use a 10 minute lysis step at 95 °C for thermolysis. Thus, we considered a half-life of bacteria
 
of 2 minutes at 95 °C. This would result in a lysis efficiency of 96.875%. Starting from this estimation,  
 
of 2 minutes at 95 °C. This would result in a lysis efficiency of 96.875%. Starting from this estimation,  
we considered the rate constant of lysis and thus the half-time using Arrhenius equation as commonly done in the literature:  
+
we considered the rate constant of lysis and thus the half-life using Arrhenius equation as commonly done in the literature:  
 
</p>
 
</p>
 
<p>
 
<p>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/b/b0/T--Munich--Model_Equation_1.png"><span>(1)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/b/b0/T--Munich--Model_Equation_1.png"><span>(7)</span></div>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/3/30/T--Munich--Model_Equation_2.png"><span>(2)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/3/30/T--Munich--Model_Equation_2.png"><span>(8)</span></div>
 
</p>
 
</p>
 
<p>
 
<p>
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</p>
 
</p>
 
<p>
 
<p>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/7/7d/T--Munich--Model_Equation_3.png"><span>(3)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/7/7d/T--Munich--Model_Equation_3.png"><span>(9)</span></div>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/3/35/T--Munich--Model_Equation_4.png"><span>(4)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/3/35/T--Munich--Model_Equation_4.png"><span>(10)</span></div>
 
</p>  
 
</p>  
 
<p>
 
<p>
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</p>
 
</p>
 
<p>
 
<p>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/8/81/T--Munich--Model_Equation_5.png"><span>(5)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/8/81/T--Munich--Model_Equation_5.png"><span>(11)</span></div>
 
</p>
 
</p>
 
<p>
 
<p>
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</p>
 
</p>
 
<p>
 
<p>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/6/6d/T--Munich--Model_Equation_6.png"><span>(6)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/6/6d/T--Munich--Model_Equation_6.png"><span>(12)</span></div>
 
</p>
 
</p>
 
<p>
 
<p>
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<p>
 
<p>
 
Equation 7 + 8
 
Equation 7 + 8
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/b/b7/T--Munich--Model_Equation_7.png"><span>(7)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/b/b7/T--Munich--Model_Equation_7.png"><span>(13)</span></div>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/e/e5/T--Munich--Model_Equation_8.png"><span>(8)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/e/e5/T--Munich--Model_Equation_8.png"><span>(14)</span></div>
 
</p>
 
</p>
 
<p>
 
<p>
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</p>
 
</p>
 
<p>
 
<p>
<div class="equationDiv"><img style="height: 40px;" src="https://static.igem.org/mediawiki/2017/4/4e/T--Munich--Model_Equation_10.png"><span>(10)</span></div>
+
<div class="equationDiv"><img style="height: 40px;" src="https://static.igem.org/mediawiki/2017/4/4e/T--Munich--Model_Equation_10.png"><span>(15)</span></div>
 
</p>
 
</p>
 
<p>
 
<p>
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</p>
 
</p>
 
<p>
 
<p>
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/5/5d/T--Munich--Model_Equation_11.png"><span>(11)</span></div>
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/5/5d/T--Munich--Model_Equation_11.png"><span>(16)</span></div>
 
</p>
 
</p>
 
<p>
 
<p>
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</tr>
 
</tr>
  
<tr><td colspan=6 align=center valign=center>
 
<h3>Signal Amplification</h3>
 
<p> 
 
We next developd a model for an RNA amplification circuit. Therefore,
 
we chose a combination of reverse trancription to recombinase polymerase amplification (RPA) and <i>in-vitro</i> transcription. A scheme for the model is shown in Figure 4.
 
Following Occami's razor, we tried to build the model with as few parameters as possible, breaking down the model into a reaction scheme that uses only 4 rate constants. In addition, we made the following assumptions to this model:<br>
 
First, the RPA reaction is thought to be in the exponential region, independent of Primer concentration since we
 
work in an environment of high primer and dNTP concentrations (up to 1000 nM) and only want to reach RNA concentration within the
 
range of the detection limit of our Cas13a protein, which is in the lower nM region. The same argument goes for the In-Vitro Transcription; since we
 
are in an environment of excessive rNTP concentrations, thus first order approximation is valid. <br>
 
Rate constants were approximated by experiments or taken from literature. The only rate constant that was not available was
 
the rate of reverse transcription. We, thus, took producer's information about commercial RT kits and estimated from these very
 
conservatively.  <br>
 
The rate constants are the following:
 
  
<tr class="lastRow"><td colspan=6 align=center valign=center>
 
<table class="myTable" width=100%>
 
<th class="leftAligned">rate constant</th>
 
<th class=”leftAligned”>value</th>
 
<th class="leftAligned">reference or rationale</th>
 
<tr>
 
<td class="leftAligned">k_lysis</td>
 
<td class="leftAligned">0.000152 [1/s]</td>
 
<td class="leftAligned">Liccardello & Nickerson, 1963; temperature dependency via Arrhenius</td>
 
</tr>
 
<tr>
 
<td class="leftAligned">k_RPA</td>
 
<td class="leftAligned">0.0008 [1/s]</td>
 
<td class="leftAligned">experimental data fitted with exponential function</td>
 
</tr>
 
<tr>
 
<td class="leftAligned">k_Tx</td>
 
<td class="leftAligned">0.0000333 [1/s]</td>
 
<td class="leftAligned">Berta Tinao, Master's Thesis, Technical University of Munich</td>
 
</tr>
 
<tr>
 
<td class="leftAligned">K_RT</td>
 
<td class="leftAligned">0.15 [1/s]</td>
 
<td class="leftAligned">http://www.sygnis.com/hiv-reverse-transcriptase/ (11/2/2017)</td>
 
</tr>
 
</table>
 
  
 +
<tr><td colspan=6 align=center valign=center>
  
</p>
 
 
 
<p>
 
The coupled ODEs for the signal amplification circuit can be described simply by:
 
</p>
 
<p>
 
<div class="equationDiv"><img width=150 src="https://static.igem.org/mediawiki/2017/2/2c/T--Munich--ModellingPagePicture_equation13.png"><span>(13)</span></div>
 
Equations 12 + 13
 
<div class="equationDiv"><img width=150 src="https://static.igem.org/mediawiki/2017/5/5d/T--Munich--ModellingPagePicture_Theoretical_equations14.png"><span>(13)</span></div>
 
 
</p>
 
<br>
 
<div class="captionPicture">
 
<img width=300 align=center valign=center src="https://static.igem.org/mediawiki/2017/d/dc/T--Munich--ModellingPagePicture_RT-RPA-TX_scheme.svg" alt="RT-RPA-TX_scheme">
 
<p>
 
Figure 4: Scheme for the RT-RPA-Tx Amplification system.
 
</p>
 
</div>
 
<div class="captionPicture">
 
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/8/8c/T--Munich--ModellingPagePicture_RT-RPA-TX.png" alt="RT-RPA-TX">
 
<p>
 
Figure 5: Target RNA concentration dependent on initial concentrations to determine the cycle time in RT-RPA-Tx needed for reaching
 
the Cas13a detection limit of 10 nM (red line).
 
</p>
 
</div>
 
<p>
 
The overall dynamics of the RT-RPA-Tx system are shown below for several starting concentrations of RNA.
 
</p>
 
</td>
 
</tr>
 
 
<tr><td colspan=6 align=center valign=center>
 
<h3>Theoretical Detection Limit using the Amplification Circuit and Cas13a Detection</h3>
 
<p> 
 
Since the reasoning behind using an amplification method was to bring down the detection limit, a new theoretical
 
detection limit of the device may be determined combining model of lysis and isothermal amplification. For this,
 
a reasonable cycle time for point-of-care application of one hour was chosen.
 
</p>
 
<div class="captionPicture">
 
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/4/40/T--Munich--ModellingPagePicture_Cycle_Times2.png" alt="RT-RPA-TX">
 
<p>
 
Determining Cycle times to reach 10 nM Detection Limit using Amplification Circuit. Red dashed line marks the end of the thermolysis
 
</p>
 
</div>
 
<p>
 
When comparing this to cycle times needed for reaching the detection limit at 95 °C, one sees that lysis temperatures is not very important
 
to the amplification and only results in a slight shift to longer time scales. This is reasonable, since RPA, and PCR in general,
 
are enormously sensitive methods, and thus only need few templates to show a signal. Also, when comparing the concentrations
 
in the temperature screen above, one can observe that the concentrations of RNA within the sample only change insignificantly, all showing concentrations that range
 
within three-digit attomolar region or higher. Also, this model works with the statement in the literature that as little as 10 templates are enough to trigger amplification through RPA.
 
</p>
 
</td>
 
</tr>
 
<tr><td colspan=6 align=center valign=center>
 
<h3>Signal Amplification Measurement in RPATx</h3>
 
<p> 
 
When we performed time-dependent measurements of crRNA in a RPATx Ansatz, we measured saturation of T7 RNA Polymerase already at 0.2 nM template DNA. The reaction
 
kinetics and thus the formation of RNA showed pseudo-first order dynamics with a rate constant of 97 ng/min transcribed RNA. Compared to the literature (https://www.biosciencetechnology.com/article/2003/09/maximizing-yield-full-length-rna-vitro-transcription-reaction) this is not even the bottleneck since <i>In-Vitro Transcription</i> reactions can yield up to 400 μg in 4 hours. This suggests that the reaction is limited by the T7 RNA Polymerase and might be increased in yield by adding higher concentrations of T7 RNA Polymerase
 
</p>
 
  
  
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<li id=“ref_10”>Licciardello, J. J., & Nickerson, J. T. R. (1963). “Some Observations on Bacterial Thermal Death Time Curves.” Applied Microbiology, 11(6), 476–480.
 
<li id=“ref_10”>Licciardello, J. J., & Nickerson, J. T. R. (1963). “Some Observations on Bacterial Thermal Death Time Curves.” Applied Microbiology, 11(6), 476–480.
 
<li id=“ref_11”>Deindoerfer, F. H. (1957). “Calculation of Heat Sterilization Times for Fermentation Media.” Applied Microbiology, 5(4), 221–228.
 
<li id=“ref_11”>Deindoerfer, F. H. (1957). “Calculation of Heat Sterilization Times for Fermentation Media.” Applied Microbiology, 5(4), 221–228.
 +
 +
<li id="ref_12">M. Weitz, K. Jongmin, K. Kapsner, E. Winfree, E. Franco, and F.C. Simmel.
 +
“Diversity in the Dynamical Behaviour of a Compartmentalized Programmable Biochemical Oscillator.”
 +
(2014) <i>Nature Chemistry</i> 6(4): 295–302.
 +
 +
<li id="ref_13">V. Mekler1, L. Minakhin, E. Semenova, K. Kuznedelov and K. Severinov
 +
"Kinetics of the CRISPR-Cas9 effector complex assembly and the role of 3′-terminal segment of guide RNA"
 +
<i>Nucleic Acids Research</i>, Vol. 44(6): 2837–2845
  
 
</ol>  
 
</ol>  
 
</p>
 
</p>
 
<div class="captionPicture">
 
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/4/46/T--Munich--ModellingPagePicture_Theoretical_Detection_Limit_lowCas.png" width="600">
 
<p>
 
Figure 2: Theoretical Detection Limit determined for the Cas13a system using concentrations of 1 nM Cas13a and 10 nM crRNA.
 
</p>
 
</div>
 
 
</td>
 
</td>
 
</tr>
 
</tr>

Latest revision as of 03:53, 2 November 2017


Modelling

Modelling in Biosciences is a powerful tool that allows us to get a deeper understanding of our system. It guided and assisted the design of our detection system which helped us saving a lot of time by avoiding dead-end designs. We used simple models to simulate the kinetics of our enzyme cascade to develop intuition for the design of experiments. We then used our models to optimize the design of our reaction cascades for an improved detection limit and optimal lysis times. Our scripts can be found on our GitHub repository.

Detection Limit

One major concern when dealing with the problem of diagnostics on patients is extracting the sample with which detection can actually be performed. Since we wanted our method to be non-invasive, one concern that we needed to deal with is the concentration of pathogens and thus detectable RNA in the patients mucus or other non-invasive sample. First approximations from different papers already showed that virological samples show concentrations no higher than low pM and can even go as low as aM.

Our wetlab experiments indicated that the detection limit of the Cas13a RNase activity is in the range of 10 nM. Using our kinetic data, we estimated the rate constants for the different reactions to create a simple ODE model.
The chemical and differential equations for the model are shown below:

LightbringerReal

rate constant value reference or rationale
kcr 1 [1/min] Mekler et al. (2016) Nucleic Acids Resarch
kt 0.001 [1/min] Estimated to be slow in comparison to kcr
kcol 10 [1/min] Estimated to be fast in comparison to kcr
kRT-RPA-Tx 0.4 [1/min] Estimated from RPA-Tx amplification experiments
KM 500 [nM] Weitz et al. (2014) Nature Chemistry


As shown in Figure 1, our simulations are able to reproduce the behavior observed experimentally.

LightbringerReal

Figure 1: Kinetics of Cas13a using 1 nM Cas13a and 10 nM crRNA at different target concentrations.

Next, we analysed the amount of readout RNA that was cleaved after 30 minutes for varying target concentrations. As shown in Figure 2, the curve follows a sigmoidal behavior and suggests a detection limit in the range of 10 nM. Due to this result, our initial design of applying the lysed and purified RNA sample directly on the detection paper strip had to be discarded. Since it is known from literature that Cas proteins show activity independent of their activation mechanism at high concentrations, we could not increase the concentration of Cas13a to improve the sensitivity. Instead, we explored amplification methods upstream in the detection process.

LightbringerReal

Figure 2: Estimated detection limit determined for the Cas13a system using 1 nM Cas13a and 10 nM crRNA.

Improved Reaction Cascade

In collaboration with our wetlab team we developed a reaction cascade for sample pre-amplification by coupling reverse transcription to isothermal recombinase polymerase amplification and transcription (RT-RPA-TX), resulting in auto-catalysis of target RNA (Figure 3).

RT-RPA-TX_scheme

Figure 3: Scheme for the RT-RPA-TX amplification system.

In order to compare the detection limit of the Cas13a system alone with the detection limit of the amplified the reaction cascade, we expanded our model, assuming exponential amplification of the target RNA. As the amplification reaction saturates due to a depletion of resources, the amplification stops as soon as the target RNA level reaches an upper limit of 1000 nM (Figure 4).

RT-RPA-TX

Figure 4: Schematic representation of the target RNA amplification during the estimation of the detection limit using the reaction cascade.

The kinetics for the amplfication cascade coupled to Cas13a based detection are shown in Figure 5. Strikingly, the start of the reaction seems to be determined by the amplificaiton reaciton, while the consecutive phase is limited by the rate of Cas13a mediated cleavage. As shown in Figure 2, the detection limit of the reaction cascade decreases by approximately three orders of magnitude. These simulations led us to implement our pre-amplification cascade into our CascAID system.

LightbringerReal

Figure 5: Kinetics of the Cas13a systemusing 1 nM Cas13a and 10 nM crRNA at different target concentrations using the reaction cascade.

Lysis on Chip

We modelled the lysis process on chip to get an idea of how long lysis would need to take place in order to release enough RNA for downstream amplification. For this, we constructed a very simplistic model for bacterial cell lysis. In this, we estimated the rate constants for cell lysis by common colony PCR protocols which use a 10 minute lysis step at 95 °C for thermolysis. Thus, we considered a half-life of bacteria of 2 minutes at 95 °C. This would result in a lysis efficiency of 96.875%. Starting from this estimation, we considered the rate constant of lysis and thus the half-life using Arrhenius equation as commonly done in the literature:

(7)
(8)

with rate constants k1 and k2 at temperature T1 and T2 and Boltzmann constant R.

where R is the gas constant and k1 and k2 are the rate constant at temperature T1 and T2 The activation energy difference E_A was fitted to a barrier that follows the common rule of thumb that lysis should increase twice in efficiency for every temperature increase of 10 °C. The model for lysis is shown derived in the following:

The full model can then be described by the coupled ordinary differential equations:

(9)
(10)

with klysis being the rate constant of bacterial lysis, kRNase the rate constant of RNA degradation, count of target RNA [targetRNA] and count of bacteria Baks(t). The solution to equation 3 is of course simply:

(11)

Plugging equation 5 into equation 4 gives

(12)

where ratio determines the copy number of a target RNA in a single cell. This differential equation has the form and thus the analytical solution:

Equation 7 + 8

(13)
(14)

with initial condition of

(15)

we get the final solution to the lysis equation:

(16)

The full model at different temperatures looks as follows:

Lysis_Temperature

Figure 3: Effect of lysis temperature on the lysis efficiency of bacterial cells and determination of the released concentration of target RNA from lysis assuming a ratio of 30 RNA molecules per cell.

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