Difference between revisions of "Team:Munich/Model"

 
(88 intermediate revisions by 5 users not shown)
Line 1: Line 1:
{{Munich}}
+
<!-- #919191 Grau1 -->
 +
<!-- #787878 Grau2 -->
 +
<!-- #51A7f9 Blau1 -->
 +
<!-- #3c7cb9 Blau2 -->
 +
<!-- #ffffff weiss -->
 
<html>
 
<html>
 +
<link rel="stylesheet" type="text/css"
 +
href="https://2017.igem.org/Template:Munich/CSS?action=raw&ctype=text/css" />
 +
<link rel="stylesheet" type="text/css"
 +
href="https://2017.igem.org/Template:Munich/Header?action=raw&ctype=text/css" />
 +
<link rel="stylesheet" type="text/css"
 +
href="https://2017.igem.org/Template:Munich/Filter?action=raw&ctype=text/css" />
 +
<head>
 +
<style>
 +
#HQ_page h3{
 +
text-align: left;
 +
margin-bottom: 10px;
 +
}
  
 +
#HQ_page h2{
 +
text-align: left;
 +
}
  
<div class="column full_size">
+
#HQ_page h3{
<h3>★ ALERT! </h3>
+
  color: #51a7f9;
<p>This page is used by the judges to evaluate your team for the <a href="https://2017.igem.org/Judging/Medals">medal criterion</a> or <a href="https://2017.igem.org/Judging/Awards"> award listed above</a>. </p>
+
}
<p> Delete this box in order to be evaluated for this medal criterion and/or award. See more information at <a href="https://2017.igem.org/Judging/Pages_for_Awards"> Instructions for Pages for awards</a>.</p>
+
</div>
+
<div class="clear"></div>
+
  
<div class="column full_size">
 
<h1> Modeling</h1>
 
  
<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
+
#myContent tr p{
 +
margin-bottom: 10px;
 +
}
  
 +
#myContent tr.lastRow p{
 +
margin-bottom: 40px;
 +
}
 +
 +
#HQ_page .listInParagraph{
 +
margin-bottom: 10px;
 +
margin-left: 10px;
 +
text-align: left;
 +
}
 +
 +
#HQ_page .interLabResults{
 +
}
 +
 +
#HQ_page .interLabResults img{
 +
margin-top: 75px;
 +
margin-bottom: 75px;
 +
}
 +
 +
#HQ_page .equationDiv img {
 +
height: 100px;
 +
}
 +
 +
</style>
 +
</head></html>
 +
{{Munich/Menu}}
 +
<html>
 +
<body>
 +
<table width=100% height=100% cellpadding=0 cellspacing=0 border=0>
 +
<!-- Content -->
 +
<tr><td width="100%" colspan=4>
 +
<table width=100% height=100% cellpadding=0 cellspacing=0 border=0>
 +
<tr>
 +
<td width="40%">
 +
</td>
 +
<td id="myContent" width="20%" valign=top align=center>
 +
<br>
 +
<!-- Head End -->
 +
<!-- Content Begin -->
 +
<img id="TopPicture" width="800" src="https://static.igem.org/mediawiki/2017/6/62/T--Munich--FrontPagePictures_Modeling.jpg">
 +
<table width="960" border=0 cellspacing=0 cellpadding=10>
 +
<tr>
 +
<td width=160></td>
 +
<td width=160></td>
 +
<td width=160></td>
 +
<td width=160></td>
 +
<td width=160></td>
 +
<td width=160></td>
 +
</tr>
 +
<tr><td colspan=6 align=left valign=center>
 +
<font size=7 color=#51a7f9><b style="color: #51a7f9">Modelling</b></font>
 +
</td>
 +
</tr>
 +
<tr class="lastRow">
 +
<td  colspan=6 align="left">
 +
<p class="introduction">
 +
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 <a class='myLink' href='https://github.com/igemsoftware2017/igem_munich_2017/tree/master/Modelling'>GitHub repository</a>.
 +
 +
</p>
 +
</td>
 +
</tr>
 +
 +
<tr><td colspan=6 align=center valign=center>
 +
<h3>Detection Limit</h3>
 +
<p> 
 +
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.
 +
</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">
 +
<img alt="LightbringerReal" src="https://static.igem.org/mediawiki/2017/3/32/T--Munich--ModellingPagePicture_ODE_equations.png" width="900">
 +
<p>
 +
</p>
 
</div>
 
</div>
<div class="clear"></div>
 
  
<div class="column half_size">
+
<tr class="lastRow"><td colspan=6 align=center valign=center>
<h3> Gold Medal Criterion #3</h3>
+
<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>
 
<p>
To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.  
+
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>
Please see the <a href="https://2017.igem.org/Judging/Medals"> 2017 Medals Page</a> for more information.  
+
Figure 1: Kinetics of Cas13a using 1 nM Cas13a and 10 nM crRNA at different target concentrations.  
 
</p>
 
</p>
 
</div>
 
</div>
  
<div class="column half_size">
+
 
<h3>Best Model Special Prize</h3>
+
  
 
<p>
 
<p>
To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.  
+
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.
<br><br>
+
You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
+
 
</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>
 +
Figure 2: Estimated detection limit determined for the Cas13a system using 1 nM Cas13a and 10 nM crRNA.
 +
</p>
 
</div>
 
</div>
<div class="clear"></div>
 
  
<div class="column full_size">
+
 
<h5> Inspiration </h5>
+
<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>
 
<p>
Here are a few examples from previous teams:
+
Figure 3: Scheme for the RT-RPA-TX amplification system.
 +
</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>
 
</p>
<ul>
 
<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
 
<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
 
<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
 
<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
 
</ul>
 
  
 +
<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>
 +
Figure 4: Schematic representation of the target RNA amplification during the estimation of the detection limit using the reaction cascade.
 +
</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>
 +
 +
 +
 +
 +
<tr><td colspan=6 align=center valign=center>
 +
<h3>Lysis on Chip</h3>
 +
<p> 
 +
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:
 +
</p>
 +
<p>
 +
<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>
 +
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>
 +
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>
 +
The full model can then be described by the coupled ordinary differential equations:<br>
 +
</p>
 +
<p>
 +
<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>
 +
<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>
 +
Plugging equation 5 into equation 4 gives
 +
</p>
 +
<p>
 +
<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>
 +
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:
 +
</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>
 
</div>
 +
</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>
 +
<br><br><br><center><hr></center>
 +
</td></tr>
 +
</table>
 +
<!-- Content End -->
 
</html>
 
</html>
 +
{{Munich/Footer}}

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.

References

  1. Moody, C., et al. (2016). “A mathematical model of recombinase polymerase amplification under continuously stirred conditions.” Biochemical Engineering Journal 112: 193-201.
  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.
  3. Li, L., et al. (2011). “Kinetics of hydrothermal inactivation of endotoxins.” Appl Environ Microbiol 77(8): 2640-2647.
  4. Valente, W., et al. (2009). “A Kinetic Study of In Vitro Lysis of Mycobacterium smegmatis.” Chem Eng Sci 64(9): 1944-1952.
  5. Fabritz, H. (2007). “Autoclaves Qualification & Validation.” Experts Meeting in Baden
  6. Marras, S. A., et al. (2004). “Real-time measurement of in vitro transcription.” Nucleic Acids Res 32(9): e72.
  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.
  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.
  9. Rauhut, R. and G. Klug (1999). “mRNA degradation in bacteria.” FEMS Microbiol Rev 23(3): 353-370.
  10. Licciardello, J. J., & Nickerson, J. T. R. (1963). “Some Observations on Bacterial Thermal Death Time Curves.” Applied Microbiology, 11(6), 476–480.
  11. Deindoerfer, F. H. (1957). “Calculation of Heat Sterilization Times for Fermentation Media.” Applied Microbiology, 5(4), 221–228.
  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) Nature Chemistry 6(4): 295–302.
  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" Nucleic Acids Research, Vol. 44(6): 2837–2845