Difference between revisions of "Team:Munich/Model"

 
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<!-- Head End -->
 
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<!-- Content Begin -->
 
<!-- Content Begin -->
<img id="TopPicture" width="800" src="https://static.igem.org/mediawiki/2017/7/78/T--Munich--FrontPagePictures_Software.svg">
+
<img id="TopPicture" width="800" src="https://static.igem.org/mediawiki/2017/6/62/T--Munich--FrontPagePictures_Modeling.jpg">
 
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</tr>
 
</tr>
 
<tr class="lastRow">
 
<tr class="lastRow">
<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 one to get a deeper understanding
+
Modelling in Biosciences is a powerful tool that allows us to get a deeper understanding
of one's system. We mainly used Modelling to help with the design of our device.
+
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>.
By this, we could avoid spending time on dead-end-designs that otherwise might have
+
cost us a significant amount of time. Rather simple models can already give
+
</p>
fair amount of information about one's system. That is why we decided at an early stage to incorporate
+
</td>
Modelling in our device design.  
+
        </p>
+
</td>
+
 
</tr>
 
</tr>
  
 
+
<tr><td colspan=6 align=center valign=center>
<tr class="lastRow"><td colspan=6 align=left valign=center>
+
<h3>Detection Limit</h3>
<h2>Detection Limit</h2>
+
 
<p>   
 
<p>   
One major concern when dealing with the problem of diagnostics on patients is obtaining 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. 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  
+
as fM. Thus, we characterised the theoretical detection limit of the Cas13a RNAse activity. In order to do this, we first
+
fitted parameters using experimental data to the model shown below and used these in target RNA concentration dependent
+
simulations. The results are shown in Figure 1. It shows that the detection limit in the time range of an hour is
+
approximately one- to two-digit nM region. 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. Instead, we had to explore amplification methods we could
+
perform upstream in the detection process.
+
<br>
+
As a side note, the detection limit could most probably have been pushed a bit to lower concentrations by using higher
+
concentrations in Cas13a and crRNA, but by doing this production cost per paperstrip would have increased a lot. Also,
+
it is known from literature that Cas proteins at high concentrations show activity independent of their activation mechanism
+
which is why the concentration of Cas13a in the detection system could not be increased by higher orders of magnitude.  
+
 
</p>
 
</p>
 +
<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.
 +
Using our kinetic data, we estimated the rate constants for the different reactions to create a simple ODE model.
 +
<br>The chemical and differential equations for the model are shown below:
 +
</p>
 +
 
<div class="captionPicture">
 
<div class="captionPicture">
<img width=600 src="https://static.igem.org/mediawiki/2017/c/c5/T--Munich--ModellingPagePicture_Theoretical_Detection_Limit.png" alt="Diagram for Cas13a's function">
+
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/3/32/T--Munich--ModellingPagePicture_ODE_equations.png" width="900">
<p>Figure 1: Theoretical Detection Limit determined for the Cas13a system using 20 nM concentrations of Cas13a and crRNA.</p>
+
<p>
 +
</p>
 
</div>
 
</div>
</td>
 
  
<tr class="lastRow"><td colspan=6 align=left valign=center>
+
<tr class="lastRow"><td colspan=6 align=center valign=center>
<h2>Lysis on Chip</h2>
+
<table class="myTable" width=100%>
<p>  
+
<th class="leftAligned">rate constant</th>
We modelled the lysis process on chip to get an idea of how long lysis would need to take place
+
<th class=”leftAligned”>value</th>
in order to release enough RNA for downstream amplification. For this, we constructed a very simplistic
+
<th class="leftAligned">reference or rationale</th>
model for bacterial cell lysis. In this, we estimated the rate constants for cell lysis by common colony PCR
+
<tr>
protocols which use a 10 minute lysis step at 95 °C for thermolysis. Thus, we considered a half-time of Bacteria
+
<td class="leftAligned">k<sub>cr</sub></td>
of 2 minutes at 95 °C. This would result in a lysis efficiency of 96.875%. Starting from this estimation,  
+
<td class="leftAligned">1 [1/min]</td>
we considered the rate constant of lysis and thus the half-time using Arrhenius equation.  
+
<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">
 +
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/8/87/T--Munich--ModellingPagePicture_kinetics_Cas13a_only.png" width="700">
 
<p>
 
<p>
Equations 1+2
+
Figure 1: Kinetics of Cas13a using 1 nM Cas13a and 10 nM crRNA at different target concentrations.
 
</p>
 
</p>
 +
</div>
 +
 +
 +
 
<p>
 
<p>
where R is the gas constant and k describe the rate constant at Temperature T_1 and Temperature T_2. The Arrhenius
+
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.
energy E_A was fitted to a barrier that follows the common rule of thumb that lysis should increase twice in
+
efficiency every temperature increase of 10 °C. The model for lysis is shown below:
+
 
</p>
 
</p>
 +
 +
<div class="captionPicture">
 +
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/b/b8/T--Munich--ModellingPagePicture_CascAID.png" width="600">
 
<p>
 
<p>
 +
Figure 2: Estimated detection limit determined for the Cas13a system using 1 nM Cas13a and 10 nM crRNA.
 +
</p>
 +
</div>
  
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/f/f1/T--Munich--ModellingPagePicture_Lysis_Temperature.png" alt="Lysis_Temperature">
+
 
 +
<h3>Improved Reaction Cascade</h3>
 +
<p> 
 +
 
 +
 
 +
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>
 
<p>
<i> Effect of lysis temperature on the lysis efficiency of bacterial cells
+
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>.
and Determination of the released concentration of target RNA from lysis assuming a ratio of 30
+
RNA molecules per cell. </i>
+
 
</p>
 
</p>
 +
 +
<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">
 
<p>
 
<p>
The full model can then be described by the coupled ordinary differential equations:<br>
+
Figure 4: Schematic representation of the target RNA amplification during the estimation of the detection limit using the reaction cascade.
Equations 3+4
+
</p>
</p>  
+
</div>
 
<p>
 
<p>
The full model at 95 °C looks as follows:
+
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>
 
</p>
</td>
+
<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>
 
</tr>
  
  
<tr class="lastRow"><td colspan=6 align=left valign=center>
+
 
<h2>Signal Amplification</h2>
+
 
 +
<tr><td colspan=6 align=center valign=center>
 +
<h3>Lysis on Chip</h3>
 
<p>   
 
<p>   
For the simulation of an amplification system, we circuit amplifying an RNA system. Therefore,  
+
We modelled the lysis process on chip to get an idea of how long lysis would need to take place
we couple a Reverse Trancription to an isothermal PCR-like amplification called Recombinase Polymerase Amplification (RPA)
+
in order to release enough RNA for downstream amplification. For this, we constructed a very simplistic
and do In-Vitro Transcription from the build template. A scheme for the model is shown in Figure 2.  
+
model for bacterial cell lysis. In this, we estimated the rate constants for cell lysis by common colony PCR
For simplicity, we made assumptions to this model:<br>
+
protocols which use a 10 minute lysis step at 95 °C for thermolysis. Thus, we considered a half-life of bacteria
First, the RPA reaction is thought to be in the linear region, independent of Primer concentration since we
+
of 2 minutes at 95 °C. This would result in a lysis efficiency of 96.875%. Starting from this estimation,
work in an environment of very high primer and dNTP concentrations (up to 1000 nM) and only want to reach RNA concentration within the  
+
we considered the rate constant of lysis and thus the half-life using Arrhenius equation as commonly done in the literature:  
range of the detection limit of our Cas13a protein, which is in the nM region. Therefore, since we are amplifying the RNA by
+
Transcription from the cDNA, this assumption is reasonable. 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 (two orders of magnitude less in reaction speed) to not be biased in the simulation by overfitting parameters. <br>
+
The rate constants are the following:  
+
COUNT ALL 4 RATE CONSTANTS
+
 
</p>
 
</p>
 
 
<p>
 
<p>
<img width=600 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">
+
<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>(8)</span></div>
 
</p>
 
</p>
 
 
<p>
 
<p>
<i>Figure 2: Scheme for the RT-RPA-Tx Amplification system </i>
+
with rate constants k<sub>1</sub> and k<sub>2</sub> at temperature T<sub>1</sub> and T<sub>2</sub>
 +
and Boltzmann constant R.
 
</p>
 
</p>
 
 
<p>
 
<p>
<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">
+
where R is the gas constant and k<sub>1</sub> and k<sub>2</sub> are the rate constant  at temperature T<sub>1</sub> and T<sub>2</sub>
 +
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:
 
</p>
 
</p>
 
<p>
 
<p>
<i>Figure 3: Target RNA concentration dependent on initial concentrations to determine the cycle time in RT-RPA-Tx needed for reaching
+
The full model can then be described by the coupled ordinary differential equations:<br>
the Cas13a detection limit of 10 nM (red line). </i>
+
 
</p>
 
</p>
 
<p>
 
<p>
The overall dynamics of the RT-RPA-Tx system are shown below for several starting concentrations of RNA.  
+
<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>(10)</span></div>
 +
</p>
 +
<p>
 +
with k<sub>lysis</sub> being the rate constant of bacterial lysis, k<sub>RNase</sub> 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:
 +
<br>
 
</p>
 
</p>
</td>
+
<p>
 
+
<div class="equationDiv"><img src="https://static.igem.org/mediawiki/2017/8/81/T--Munich--Model_Equation_5.png"><span>(11)</span></div>
<tr class="lastRow"><td colspan=6 align=left valign=center>
+
<h2>Theoretical Detection Limit using the Amplification Circuit and Cas13a Detection</h2>
+
<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>
 
</p>
 
<p>
 
<p>
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/1/13/T--Munich--ModellingPagePicture_Cycle_Times.png" alt="RT-RPA-TX">
+
Plugging equation 5 into equation 4 gives
 
</p>
 
</p>
 
<p>
 
<p>
<i>Determining Cycle times to reach 10 nM Detection Limit using Amplification Circuit. Red dashed line marks the end of the thermolysis</i>
+
<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>
When comparing this to cycle times needed for reaching the detection limit at 65 °C, one sees that lysis temperatures is not very important
+
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:
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.
+
 
</p>
 
</p>
 +
<p>
 +
Equation 7 + 8
 +
<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>(14)</span></div>
 +
</p>
 +
<p>
 +
with initial condition of
 +
</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>(15)</span></div>
 +
</p>
 +
<p>
 +
we get the final solution to the lysis equation:
 +
</p>
 +
<p>
 +
<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>
 +
The full model at different temperatures looks as follows:
 +
</p>
 +
<div class="captionPicture">
 +
<img width=800 align=center valign=center src="https://static.igem.org/mediawiki/2017/f/f1/T--Munich--ModellingPagePicture_Lysis_Temperature.png" alt="Lysis_Temperature">
 +
<p>
 +
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.
 +
</p>
 +
</div>
 
</td>
 
</td>
 +
</tr>
  
  
  
 +
<tr><td colspan=6 align=center valign=center>
  
  
  
  
 +
<tr><td colspan=6 align=center valign=center>
 +
<h3>References</h3>
 +
<p>
 +
    <ol style="text-align: left">
 +
      <li id=“ref_1”>Moody, C., et al. (2016). “A mathematical model of recombinase polymerase amplification under continuously stirred conditions.” Biochemical Engineering Journal 112: 193-201.
 +
<li id=“ref_2”>Moody, C., et al. (2016). “A mathematical model of recombinase polymerase amplification under continuously stirred conditions.” Biochemical Engineering Journal 112(Supplement C): 193-201.
 +
<li id=“ref_3”>Li, L., et al. (2011). “Kinetics of hydrothermal inactivation of endotoxins.” Appl Environ Microbiol 77(8): 2640-2647.
 +
<li id=“ref_4”>Valente, W., et al. (2009). “A Kinetic Study of In Vitro Lysis of Mycobacterium smegmatis.” Chem Eng Sci 64(9): 1944-1952.
 +
<li id=“ref_5”>Fabritz, H. (2007). “Autoclaves Qualification & Validation.” Experts Meeting in Baden
 +
<li id=“ref_6”>Marras, S. A., et al. (2004). “Real-time measurement of in vitro transcription.” Nucleic Acids Res 32(9): e72.
 +
<li id=“ref_7”>Mafart, P., et al. (2002). “On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model.” Int J Food Microbiol 72(1-2): 107-113.
 +
<li id=“ref_8”>Chiruta, J. (2000). “Thermat Sterilisation Kinetics of Bacteria as Influenced by Combined Temperature and pH in Continuous Processing of Liquid.” Thesis, The Universit of Adelaide Department of Chemical Engineering Faculty of Engineering.
 +
<li id=“ref_9”>Rauhut, R. and G. Klug (1999). “mRNA degradation in bacteria.” FEMS Microbiol Rev 23(3): 353-370.
 +
<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_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>
 
+
</p>
 
+
</td>
 
+
</tr>
 
+
 
+
 
+
 
+
  
 
<tr><td class="no-padding" colspan=6 align=right valign=center height=10>
 
<tr><td class="no-padding" colspan=6 align=right valign=center height=10>

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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