diff --git a/static/js/animate.js b/static/js/animate.js
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@@ -22,61 +22,3 @@ $('.pic-display').each(function (item) {
});
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diff --git a/wiki/pages/model.html b/wiki/pages/model.html
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@@ -324,10 +324,10 @@ endblock %}
the validation experiments of Csm6 in tandem with Cas13a. Therefore, in our modeling, we simulated this process of
Csm6 reacting in tandem with cas13a and releasing the fluorescence effect, and simulated the effect of shielding the
mutant chain with PNA shackles.</h3>
- <div class="opic annotation">
- <img src="" alt="png">
- <p>Reaction system diagram</p>
- </div>
+ <div class="pic-box largebox">
+ <img src="" alt="png" class="avatar">
+ </div>
+ <p class="c">Reaction system diagram</p>
<h3>1.Shielding of mismatched-strand RNA by shackled PNA</h3>
<p>In the process of crispr/cas detection, the cleavage of target by csm6 can be misidentified due to
misidentification with similar targets, and we designed the shackled PNA for shielding the mismatched chain to solve
@@ -607,15 +607,86 @@ endblock %}
<p>The residual variance of the network we built is relatively scattered as shown in Figure 6, and a little high
variance residuals in the low location region may have an impact on the low eaves data, which still needs to be
improved in the future.</p>
- <div class="opic annotation">
- <img src="" alt="png">
- <p> Figure 6 Residual Plot</p>
- </div>
+ <div class="pic-box largebox">
+ <img src="" alt="png" class="avatar">
+ </div>
+ <p class="c"> Figure 6 Residual Plot</p>
<p> Here, we evaluated the nearest neighbor method model by DEA-SBM and implemented the prediction of nucleic acid unstranding temperature using neural networks based on the nearest neighbor method model. For future teams, you can complete the determination of the parameters related to the nearest neighbor dimers such as CG/GG/GA of PNA/DNA in the laboratory, and then use the neural networks to make the prediction of the optimal temperature at annealing.</p>
<h2 class="l">Annex 1 Network Factor Table</h2>
<a href="https://static.igem.wiki/teams/4223/wiki/annex-1-network-factor-table.pdf">（Network-Factor-Table.pdf)</a>
<h2 class="l">Annex 2 Test data set</h2>
<a href="https://static.igem.wiki/teams/4223/wiki/annex-2-test-data-set.pdf">（Test-data-set.pdf)</a>
+ <h2 class="l">Annex 3 Dynamics model code</h2>
+ <code>function [dy] = f_PNA(t,y,kr1,kr2,ka,da,ka2,da2)
+ dy = zeros(3,1);
+ dy(1) = -kr2*y(2)-kr1*y(1);
+ dy(2) = -(ka+da)*y(2);
+ dy(3) = -(ka2+da2)*y(3);
+ end
+ %y(1)=[PNA];y(2)=[Target RNA];y(3)=[Mutate RNA];
+ function [dx] = f_RNA1(t,x,k_Csm6_a,k_Csm6_da,k_ccs6mon,v_mcsm6off,k_mcsm6on)
+ dx = zeros(5,1);
+ dx(1) = -k_Csm6_a*x(3).*x(1)+k_Csm6_da*x(2);
+ dx(2) = k_Csm6_a*x(3).*x(1)-k_Csm6_da*x(2);
+ dx(3) =-k_Csm6_a*x(3).*x(1)+k_Csm6_da*x(2);
+ dx(4)=-((k_ccs6mon*x(2).*x(4))./(k_mcsm6on+x(4)))-((v_mcsm6off*x(5))./(k_mcsm6on+x(5)));
+ dx(5)=((k_ccs6mon*x(2).*x(4))./(k_mcsm6on+x(4)))-((v_mcsm6off*x(5))./(k_mcsm6on+x(5)));
+ end
+ %x(1)=[Csm6_off];x(2)=[Csm6_on];x(3)=[RNA];x(4)=[CARF];x(5)=[HEPN];
+ %RNA系数为1
+ function [dx] = f_RNA2(t,x2,k_Csm6_a,k_Csm6_da,k_ccs6mon,v_mcsm6off,k_mcsm6on)
+ dx = zeros(5,1);
+ dx(1) = -k_Csm6_a*x2(3).^2.*x2(1)+k_Csm6_da*x2(2);
+ dx(2) = k_Csm6_a*x2(3).^2.*x2(1)-k_Csm6_da*x2(2);
+ dx(3) =2*(-k_Csm6_a*x2(3).^2.*x2(1)+k_Csm6_da*x2(2));
+ dx(4)=-((k_ccs6mon*x2(2).*x2(4))./(k_mcsm6on+x2(4)))-((v_mcsm6off*x2(5))./(k_mcsm6on+x2(5)));
+ dx(5)=((k_ccs6mon*x2(2).*x2(4))./(k_mcsm6on+x2(4)))-((v_mcsm6off*x2(5))./(k_mcsm6on+x2(5)));
+ end
+ %x(1)=[Csm6_off];x(2)=[Csm6_on];x(3)=[RNA];x(4)=[CARF];x(5)=[HEPN];
+ %RNA系数为2
+ function [dx] = f_RNA3(t,x3,k_Csm6_a,k_Csm6_da,k_ccs6mon,v_mcsm6off,k_mcsm6on)
+ dx = zeros(5,1);
+ dx(1) = -k_Csm6_a*x3(3).^3.*x3(1)+k_Csm6_da*x3(2);
+ dx(2) = k_Csm6_a*x3(3).^3.*x3(1)-k_Csm6_da*x3(2);
+ dx(3) =3*(-k_Csm6_a*x3(3).^3.*x3(1)+k_Csm6_da*x3(2));
+ dx(4)=-((k_ccs6mon*x3(2).*x3(4))./(k_mcsm6on+x3(4)))-((v_mcsm6off*x3(5))./(k_mcsm6on+x3(5)));
+ dx(5)=((k_ccs6mon*x3(2).*x3(4))./(k_mcsm6on+x3(4)))-((v_mcsm6off*x3(5))./(k_mcsm6on+x3(5)));
+ end
+ %x(1)=[Csm6_off];x(2)=[Csm6_on];x(3)=[RNA];x(4)=[CARF];x(5)=[HEPN];
+ %RNA系数为3
+ clear;
+ tspan=[0,120];
+ x0 = [2.04667e-5;0;6.80008e-5;2.40213e-5;0]; %åˆå§‹æµ“度4.30667e-5
+ k_Csm6_a=1.0e9;
+ k_Csm6_da=2.6e-2;
+ k_ccs6mon=2.0e-2;
+ v_mcsm6off=1.9547e-5;
+ k_mcsm6on=1.27e-3;
+ [t1,x1] = ode45(@f_RNA1,tspan,x0,[],k_Csm6_a,k_Csm6_da,k_ccs6mon,v_mcsm6off,k_mcsm6on);
+ [t2,x2] = ode45(@f_RNA2,tspan,x0,[],k_Csm6_a,k_Csm6_da,k_ccs6mon,v_mcsm6off,k_mcsm6on);
+ [t3,x3] = ode45(@f_RNA3,tspan,x0,[],k_Csm6_a,k_Csm6_da,k_ccs6mon,v_mcsm6off,k_mcsm6on);
+ plot(t1,x1(:,5),'r',t2,x2(:,5),'g',t3,x3(:,5),'b');
+ axis on;
+ xlabel('time(min)');
+ ylabel('Fluorescence');
+ legend('n=1','n=2','n=3')
+ clear;
+ tspan=[0,30];
+ y0 = [2.11;2.11;2.11]; %åˆå§‹æµ“度
+ kr1=0.96;
+ kr2=0.001;
+ ka=0.004;
+ da=0.0001;
+ ka2=0.106;
+ da2=0.00001;
+ options = odeset('RelTol',1e-10,'AbsTol',[1e-10 1e-10 1e-10]);
+ [t,y] = ode45(@f_PNA,tspan,y0,[],kr1,kr2,ka,da,ka2,da2);
+ plot(t,y(:,1),'r',t,y(:,2),'g',t,y(:,3),'b');
+ axis on;
+ xlabel('time');
+ ylabel('concentration');
+ legend('PNA','Target RNA','Mutate RNA')
+ %x(1)=[PNA];x(2)=[Target RNA];x(3)=[Mutate RNA];</code>
<h2 class="l">Reference</h2>
<p>[1]Liu Wenbin,Zhu Xiangou,Liu Xiangrong. DNA destriling temperature prediction model based on BP neural network[J].Computer Engineering and Applications, 2006(10):1-4.</p>
<p>[2]Liu TY, Knott GJ, Smock DCJ, Desmarais JJ, Son S, Bhuiya A, Jakhanwal S, Prywes N, Agrawal S, DÃaz de León Derby M, Switz NA, Armstrong M, Harris AR, Charles EJ, Thornton BW, Fozouni P, Shu J, Stephens SI, Kumar GR, Zhao C, Mok A, Iavarone AT, Escajeda AM, McIntosh R, Kim S, Dugan EJ; IGI Testing Consortium, Pollard KS, Tan MX, Ott M, Fletcher DA, Lareau LF, Hsu PD, Savage DF, Doudna JA. Accelerated RNA detection using tandem CRISPR nucleases. Nat Chem Biol. 2021 Sep;17(9):982-988. doi: 10.1038/s41589-021-00842-2. Epub 2021 Aug 5. Erratum in: Nat Chem Biol. 2021 Nov;17(11):1210. PMID: 34354262.</p>