For safety issues we want bakillus to vanish after he fulfilled his purpose. For this task we want our BaKillus to stop producing resistancy proteins against his own toxicity. (see: [[https://2014.igem.org/Team:LMU-Munich/Project/Bakillus#suicide|Suicide Switch]]) However, as simple as this idea might sound in theory, the implementation will require very precise fine-tuning.
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For safety issues we want bakillus to vanish after he fulfilled his purpose. For this task we want our BaKillus to stop producing resistancy proteins against his own toxicity. (see: [https://2014.igem.org/Team:LMU-Munich/Project/Bakillus#suicide Suicide-Switch]) However, as simple as this idea might sound in theory, the implementation will require very precise fine-tuning.
To assess the applicability of this approach we first need to understand the underlying dynamics of the system. Consequentely we chose to simulate the process in silico.
To assess the applicability of this approach we first need to understand the underlying dynamics of the system. Consequentely we chose to simulate the process in silico.
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For analysis of the impact of each parameters, we've used a function to determine the fitness '''W''' of the model. The fitness describes the integral of the surplus of toxic cannibalism proteins over the proprietary antidote. Therefore, a successful suicide equals a high fitness of the model. This means plotting W against one parameter, while statistically bruteforcing versatile value combinations of the remaining parameters, one can see how the parameter effects the quality of the model. That way we can figure out what options we have to manipulate our suicide system to adjust it.
For analysis of the impact of each parameters, we've used a function to determine the fitness '''W''' of the model. The fitness describes the integral of the surplus of toxic cannibalism proteins over the proprietary antidote. Therefore, a successful suicide equals a high fitness of the model. This means plotting W against one parameter, while statistically bruteforcing versatile value combinations of the remaining parameters, one can see how the parameter effects the quality of the model. That way we can figure out what options we have to manipulate our suicide system to adjust it.
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
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As we can see.....
As we can see.....
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=== Delayed Model ===
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We also considered an extended signalling pathway by integrating a second Sigma Factor.
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The mathematical model for this system contains two additional equations for the new gene:
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[[File:LMU14_Equations2.png|center]]
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'''The slightly adapted script looks like this:'''
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% par0 = lhsdesign(10000,12);
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par0 = rand(100000,15);
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par = 10.^(4*par0 - 2); % Coveres range from 0.01 to 100
The results were fairly similar. As above, the statistical fitness W of the model plotted against each considered parameter, the black line resembling the average.
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
Suicide Switch
For safety issues we want bakillus to vanish after he fulfilled his purpose. For this task we want our BaKillus to stop producing resistancy proteins against his own toxicity. (see: Suicide-Switch) However, as simple as this idea might sound in theory, the implementation will require very precise fine-tuning.
To assess the applicability of this approach we first need to understand the underlying dynamics of the system. Consequentely we chose to simulate the process in silico.
We deployed following differential equations for our model:
Fixed parameters
In a first evaluation, the variation of following parameters were figured out to cause only insignificant variation of the model's fitness. Therefore they were keept at these specified values for all subsequent simulations:
Degradation Rate (g1) of mRNA:
12 molecules per hour
Degradation Rate (g2) of Proteins:
1 molecule per hour
Transcription Rate (k0) of Sdp:
7.2 per hour
Translation Rate (k1) of SdpI & SdpABC:
10 per hour
Fitness (W)
For analysis of the impact of each parameters, we've used a function to determine the fitness W of the model. The fitness describes the integral of the surplus of toxic cannibalism proteins over the proprietary antidote. Therefore, a successful suicide equals a high fitness of the model. This means plotting W against one parameter, while statistically bruteforcing versatile value combinations of the remaining parameters, one can see how the parameter effects the quality of the model. That way we can figure out what options we have to manipulate our suicide system to adjust it.
% par0 = lhsdesign(10000,12);
par0 = rand(100000,12);
par = 10.^(4*par0 - 2); % Coveres range from 0.01 to 100
par(:,3) =12; % g1
par(:,5) =1; % g2
par(:,2) =0.12*60; % k0_sdp
par(:,4) =10; % k1_sdp
tout = linspace(0,10,100);
tic
for j = 1:size(par,1)
[~,~,x,y] = simulate_RRE_cannibalism(tout,par(j,:));
%figure
%plot(tout,y)
%legend('p_{p_sdpABC}','p_{p_sdpI}')
W(j) = sum(y(:,1)-y(:,2));
end
toc
parname={'C0_Pqs','k0_sdp','g1','k1_sdp','g2','C0_p_sdpI', ...
'C0_fsrA','k0_SiAlt1','k1_SiAlt1','C0_SiAlt1','k0_fsrA','k1_fsrA'};
for k = [1,6,7,8,9,10,11,12]
figure
scatter(par(:,k),W',10,'filled')
hold on
[xx,ind]=sort(par(:,k));
r = ksr(log10(xx),W(ind));
plot(10.^r.x,smooth(r.f,10),'k-','LineWidth',5)
set(gca,'Yscale','lin')
set(gca,'Xscale','log')
xlabel(parname{k})
ylabel('W')
saveas(gcf,['parameter_' parname{k}]);
print('-depsc2','-r300',['parameter_' parname{k}])
end
The graphs show the statistical fitness W of the model plotted against each considered parameter. The black line resembles the average.
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
This is the script we used to analyse the model on MATLAB:
% par0 = lhsdesign(10000,12);
par0 = rand(100000,12);
par = 10.^(4*par0 - 2); % Coveres range from 0.01 to 100
par(:,3) =12; % g1
par(:,5) =1; % g2
par(:,2) =0.12*60; % k0_sdp
par(:,4) =10; % k1_sdp
tout = linspace(0,10,100);
tic
for j = 1:size(par,1)
[~,~,x,y] = simulate_RRE_cannibalism(tout,par(j,:));
%figure
%plot(tout,y)
%legend('p_{p_sdpABC}','p_{p_sdpI}')
W(j) = sum(y(:,1)-y(:,2));
end
toc
parname={'C0_Pqs','k0_sdp','g1','k1_sdp','g2','C0_p_sdpI', ...
'C0_fsrA','k0_SiAlt1','k1_SiAlt1','C0_SiAlt1','k0_fsrA','k1_fsrA'};
for k = [1,6,7,8,9,10,11,12]
figure
scatter(par(:,k),W',10,'filled')
hold on
[xx,ind]=sort(par(:,k));
r = ksr(log10(xx),W(ind));
plot(10.^r.x,smooth(r.f,10),'k-','LineWidth',5)
set(gca,'Yscale','lin')
set(gca,'Xscale','log')
xlabel(parname{k})
ylabel('W')
saveas(gcf,['parameter_' parname{k}]);
print('-depsc2','-r300',['parameter_' parname{k}])
end
Results
The graphs show the statistical fitness W of the model plotted against each considered parameter. The black line resembles the average.
As we can see.....
Delayed Model
We also considered an extended signalling pathway by integrating a second Sigma Factor.
The mathematical model for this system contains two additional equations for the new gene:
The slightly adapted script looks like this:
% par0 = lhsdesign(10000,12);
par0 = rand(100000,15);
par = 10.^(4*par0 - 2); % Coveres range from 0.01 to 100
par(:,3) =12; % g1
par(:,5) =1; % g2
par(:,2) =0.12*60; % k0_sdp
par(:,4) =10; % k1_sdp
tout = linspace(0,10,1000);
tic
for j = 1:size(par,1)
[~,~,x,y] = simulate_RRE_cannibalism_twosigma(tout,par(j,:));
% figure
% plot(tout,y)
% legend('p_{p_sdpABC}','p_{p_sdpI}')
%
W(j) = sum(y(:,1)-y(:,2));
end
toc
parname={'C0_Pqs','k0_sdp','g1','k1_sdp','g2','C0_p_sdpI','C0_fsrA', 'k0_SiAlt1', ...
'k1_SiAlt1','k0_SiAlt2','k1_SiAlt2','C0_SiAlt2','C0_SiAlt1','k0_fsrA','k1_fsrA'};
for k = [1,6,7,8,9,10,11,12,13,14,15]
figure
scatter(par(:,k),W',10,'filled')
hold on
[xx,ind]=sort(par(:,k));
r = ksr(log10(xx),W(ind));
plot(10.^r.x,smooth(r.f,10),'k-','LineWidth',5)
set(gca,'Yscale','lin')
set(gca,'Xscale','log')
xlabel(parname{k})
ylabel('W')
saveas(gcf,['red_parameter_' parname{k}]);
print('-depsc2','-r300',['red_parameter_' parname{k}])
end
Results
The results were fairly similar. As above, the statistical fitness W of the model plotted against each considered parameter, the black line resembling the average.
As we can see.....
Conclusion
Hi there!
Welcome to our Wiki! I'm BaKillus, the pathogen-hunting microbe, and I'll guide you on this tour through our project. If you want to learn more about a specific step, you can simply close the tour and come back to it anytime you like. So let's start!
What's the problem?
First of all, what am I doing here? The problem is, pathogenic bacteria all around the world are becoming more and more resistant against antimicrobial drugs. One major reason for the trend is the inappropriate use of drugs. With my BaKillus super powers, I want to reduce this misuse and thus do my part to save global health.
Sensing of pathogens
To combat the pathogenic bacteria, I simply eavesdrop on their communication. Bacteria talk with each other via quorum sensing systems, which I use to detect them and trigger my responses.
Adhesion
The more specific and effective I can use my powers, the lower the danger is of provoking new resistance development. So I catch pathogens whenever I get hold of them and stick to them until my work is done.
Killing
Talking about my work - killing pathogens is finally what I am made for. In response to quorum sensing molecules of the pathogens, I export a range of antimicrobial substances leading to dissipation of biofilms and the killing of the targeted bacteria.
Suicide switch
When the job is done and all the bad guys are finished, you don't need a super hero anymore. So after fulfilling my work I say goodbye to the world by activating my suicide switch.
Application
Of course I'm not only a fictional hero, but a very real one. In two different prototypes, I could be used for diagnosis or treatment of pathogen-caused diseases. However, there is still a whole lot of regulational and economical questions that have to be answered before.
"
