Difference between revisions of "Template:Team:Utrecht/MainBody"

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<b>Endonuclease activity assay</b><br>
 
<b>Endonuclease activity assay</b><br>
The first endonuclease activity assay was used to determine the optimal concentration of gRNA for the assay with the secreted proteins. The concentrations used were all functional for the Cpf1 protein (Figure 5). Cas9 however, showed no cleavage of the targeted DNA at all. This may be due to low quality gRNA. In the second assay, which contained sCas9 and sCpf1 purified from medium, there was no cleavage visible at all, even for the controls with Cas9 and Cpf1, as shown in figure 6.  
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The first endonuclease activity assay was used to determine the optimal concentration of gRNA for the assay with the secreted proteins. The concentrations used were all functional for the Cpf1 protein (Figure 5, left pane). Cas9 however, showed no cleavage of the targeted DNA at all. This may be due to low quality gRNA. In the second assay, which contained sCas9 and sCpf1 purified from medium, there was no cleavage visible at all, even for the controls with Cas9 and Cpf1, as shown in figure 5 (right pane).  
<center><img style="margin-top: 10px;" src="https://static.igem.org/mediawiki/2017/f/fc/UU_secretion_fig5.png"></center>
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<center><img style="margin-top: 25px;" src="https://static.igem.org/mediawiki/2017/f/fc/UU_secretion_fig5.png"></center>
 
<span class="text-figure">
 
<span class="text-figure">
 
<b>Figure 5.</b> Left - DNA gel electrophoresis of a linearized 800 base pair length plasmid. Concentrations of Cas9 and Cpf1 used in the assay were 0,05 uM and 0,15 uM, respectively. Right - DNA gel electrophoresis of the linearized 800bp plasmid. 2,5 nM Cas9 or Cpf1 protein was used and 10 nM gRNA.
 
<b>Figure 5.</b> Left - DNA gel electrophoresis of a linearized 800 base pair length plasmid. Concentrations of Cas9 and Cpf1 used in the assay were 0,05 uM and 0,15 uM, respectively. Right - DNA gel electrophoresis of the linearized 800bp plasmid. 2,5 nM Cas9 or Cpf1 protein was used and 10 nM gRNA.
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S'(t) = p_c (t)⋅C(t)⋅(1-∫_0^t p_c  dt)
 
S'(t) = p_c (t)⋅C(t)⋅(1-∫_0^t p_c  dt)
 
<br><br>
 
<br><br>
Wherein S’ is the increase in signal, given by the probability of cleavage (pc) for the remaining uncleaved complex. The remaining uncleaved complex is given by the remaining complex fraction (C) and how likely it is that it has not already been cleaved (one minus the integral of pc from 0 until that timepoint).
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Wherein S’ is the increase in signal, given by the probability of cleavage (p<sub>c</sub>) for the remaining uncleaved complex. The remaining uncleaved complex is given by the remaining complex fraction (C) and how likely it is that it has not already been cleaved (one minus the integral of p<sub>c</sub> from 0 until that timepoint).
 
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</span>
 
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When we solve this for each Protease Chain version and for both the transient and substrate-mediated complex, we end up with time-plots of the signal contribution of a single binding event. In these plots, we see that the resulting substrate-mediated complex signal for the slow cleaver (top) is about half as strong as the signal for the fast cleaver (bottom).In theory, this is not a problem since the signal can be amplified by the cells.  
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When we solve this for each Protease Chain version and for both the transient and substrate-mediated complex, we end up with time-plots of the signal contribution of a single binding event. In these plots, we see that the resulting substrate-mediated complex signal for the slow cleaver (top) is about half as strong as the signal for the fast cleaver (bottom). In theory, this is not a problem since the signal can be amplified by the cells.  
 
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For the fast cleaver, we see a much bigger issue. The signal contribution of the transient complex, i.e. the false positive, is only about ten-fold smaller than the signal contribution of the substrate-mediated complex. Considering that transient encounters will be a lot more frequent than substrate-binding events, the false positive signal can be multiplied many times, making it  a lot stronger than the substrate-mediated signal can ever be.  
 
For the fast cleaver, we see a much bigger issue. The signal contribution of the transient complex, i.e. the false positive, is only about ten-fold smaller than the signal contribution of the substrate-mediated complex. Considering that transient encounters will be a lot more frequent than substrate-binding events, the false positive signal can be multiplied many times, making it  a lot stronger than the substrate-mediated signal can ever be.  
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This is not the case for the slower cleaver, where the transient complex signal contribution is much smaller.
 
This is not the case for the slower cleaver, where the transient complex signal contribution is much smaller.
 
<br><br>
 
<br><br>
Using the contributions of a single transient binding event and a single substrate-mediated binding event, we can calculate a proxy for precision by dividing the contribution of the true signal (substrate-mediated) by the contribution of the false signal (transient). For the slow rate, the contribution of a single substrate-mediated event is almost 48 000 times that of the transient occurrence. For the fast cleavage rate, this is only 17 times.
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Using the contributions of a single transient binding event and a single substrate-mediated binding event, we can calculate a proxy for precision by dividing the contribution of the true signal (substrate-mediated) by the contribution of the false signal (transient). For the slow rate, the contribution of a single substrate-mediated event is almost 48,000 times that of the transient occurrence. For the fast cleavage rate, this is only 17 times.
 
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If we assume that the transient interaction occurs 100 times more frequently than the substrate-binding event, a modest estimate, ‘true’ signal strength would only be 0.17 times that of the background for the fast cleaver whereas it would still be 480 times stronger than the background for the slow cleaving protease.
 
If we assume that the transient interaction occurs 100 times more frequently than the substrate-binding event, a modest estimate, ‘true’ signal strength would only be 0.17 times that of the background for the fast cleaver whereas it would still be 480 times stronger than the background for the slow cleaving protease.
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There are eight important parameters in these equations. The k1 and k2 rates are the association and dissociation rates between DNA and the target chain. The k3 and k4 are the association and dissociation rates of the protease chain and DNA. k5 gives the effective cleavage rate. pP and pT are the production rates of the protease and target chain and d gives the decay-rate for proteins in general.
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There are eight important parameters in these equations. The k<sub>1</sub> and k<sub>2</sub> rates are the association and dissociation rates between DNA and the target chain. The k<sub>3</sub> and k<sub>4</sub> are the association and dissociation rates of the protease chain and DNA. k<sub>5</sub> gives the effective cleavage rate. pP and pT are the production rates of the protease and target chain and d gives the decay-rate for proteins in general.
 
<br><br>
 
<br><br>
The concentration of each state is given by the name of the state in square brackets, such that T stands for target chain, S stands for substrate (DNA), P stands for protease chain, F stands for effector molecule and Tc stands for cleaved target chain. Binding is indicated by ‘:’ between two components.  
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The concentration of each state is given by the name of the state in square brackets, such that T stands for target chain, S stands for substrate (DNA), P stands for protease chain, F stands for effector molecule and T<sub>c</sub> stands for cleaved target chain. Binding is indicated by ‘:’ between two components.  
 
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<br><br>
The first thing we did was to check whether this system of equations behaves the way we expect it to, on short timescales. At first, we were only interested in the system equilibria. At the time-scale of state transitions, protein production and decay per time-unit are negligible and so the values for d, pT and pP were initially set to zero.
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The first thing we did was to check whether this system of equations behaves the way we expect it to, on short timescales. At first, we were only interested in the system equilibria. At the time-scale of state transitions, protein production and decay per time-unit are negligible and so the values for d, p<sub>T</sub> and p<sub>P</sub> were initially set to zero.
 
<br><br>
 
<br><br>
Using the association and dissociation constants from Richardson et al. <i class="ref" data-id="3">3</i>, we set the parameters k3 and k4 to 4 * 104 and 5 * 10-5 respectively. Fonfara et al. <i class="ref" data-id="4">4</i> found a range of Cpf1 affinities in the same order of magnitude as Richardson et al. and so we chose to perform several runs with a variety of Cpf1 parameters going from .9 to 1.1 times the rates of Cas9. All runs resulted in similar equilibria where all target chain would be processed and cleaved.  
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Using the association and dissociation constants from Richardson et al. <i class="ref" data-id="3">3</i>, we set the parameters k<sub>3</sub> and k<sub>4</sub> to 4 * 10<sup>4</sup> and 5 * 10<sup>-5</sup> respectively. Fonfara et al. <i class="ref" data-id="4">4</i> found a range of Cpf1 affinities in the same order of magnitude as Richardson et al. and so we chose to perform several runs with a variety of Cpf1 parameters going from .9 to 1.1 times the rates of Cas9. All runs resulted in similar equilibria where all target chain would be processed and cleaved.  
 
<br><br>
 
<br><br>
 
This result is expected from a well-mixed system but, considering the membrane-bound nature of our proteins, the dynamics might not be accurate. Diffusion in the membrane is limited and thus might limit the interactions of the molecules. We made an attempt to illustrate this, too, using a molecular dynamics model but this will be described later on.
 
This result is expected from a well-mixed system but, considering the membrane-bound nature of our proteins, the dynamics might not be accurate. Diffusion in the membrane is limited and thus might limit the interactions of the molecules. We made an attempt to illustrate this, too, using a molecular dynamics model but this will be described later on.
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.url_external { background: url(https://static.igem.org/mediawiki/2017/0/0f/Uu_url_external.png) center right no-repeat !important; height: 13px !important; padding-right: 15px !important; }
 
.url_external { background: url(https://static.igem.org/mediawiki/2017/0/0f/Uu_url_external.png) center right no-repeat !important; height: 13px !important; padding-right: 15px !important; }
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#modal-overlay

Revision as of 15:31, 1 November 2017

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Cas9 & Cpf1 secretion
and activity
Comparison of endonuclease activity for Cas9 and Cpf1 that has been produced in, and excreted by, HEK293 cells.
MESA two-component system replication
Details on the MESA two-component system, explanation of its relation to our design and the results of its reproduction.
OUTCASST system production
Detailed explanation of the OUTCASST mechanism, experimental progress and technical prospects.
Modeling and
mathematics
Ordinary differential equations, cellular automaton and an object based model for optimal linker-length estimation.
InterLab study participation
Results and details of our measurements for the iGEM 2017 InterLab Study.
Stakeholders & opinions
Interviews and dialogues with stakeholders, potential users, third parties and experts relating to pathogen detection or DNA-based diagnostics.
Risks & safety-issues
Implications and design considerations relating to safety in the usage and implementation of OUTCASST as a diagnostics tool.
Design & integration
OUTCASST toolkit and product design with factors such as bio-safety and user-friendliness taken into account.
Outreach
Videos we made for the dutch public, together with 'de Kennis van Nu'.
Meet our team
About us, our interests and roles in the team and our supervisors.
Sponsors
A listing of our sponsors, how they assisted us and our gratitude for their assistance.
Collaborations
Read about our exchanges with other iGEM teams and government agencies.
Achievements
A short description of all that we have achieved during our participation in the iGEM.
Attributions
A thank-you for everyone that assited us, both in and outside the lab.