Difference between revisions of "Team:HZAU-China/Model"

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             <div id="float01" class="cur">
 
             <div id="float01" class="cur">
 
                 <div class="h1"><i>Salmonella</i> infection model</div>
 
                 <div class="h1"><i>Salmonella</i> infection model</div>
                 <p>We want to simulate the situation that tumor cells and <i>Salmonella</i> together in a liquid environment.
+
                 <p>We want to simulate the situation that tumor cells and <i>Salmonella</i> together in a liquid
 +
                    environment.
 
                     We used the law of mass action to establish a model for the infection process of <i>Salmonella</i>,
 
                     We used the law of mass action to establish a model for the infection process of <i>Salmonella</i>,
 
                     which
 
                     which
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                 </p>
 
                 </p>
 
                 <div class="h2">Identification of infection time</div>
 
                 <div class="h2">Identification of infection time</div>
                 <p>According to our experimental results, we noticed that <i>Salmonella</i> follows Poisson distribution in
+
                 <p>According to our experimental results, we noticed that <i>Salmonella</i> follows Poisson
 +
                    distribution in
 
                     normal cells, and an app was designed to judge the distribution of bacteria in the cells. We assume
 
                     normal cells, and an app was designed to judge the distribution of bacteria in the cells. We assume
 
                     that the area less than 1 in the Poisson distribution is a part of cells which are not infected by
 
                     that the area less than 1 in the Poisson distribution is a part of cells which are not infected by
                  <i>Salmonella</i>. According to our experimental results, cells which are infected by only one <i>Salmonella</i>
+
                    <i>Salmonella</i>. According to our experimental results, cells which are infected by only one <i>Salmonella</i>
 
                     can also die of pyroptosis. Based on this feature, we divide cells into uninfected and infected
 
                     can also die of pyroptosis. Based on this feature, we divide cells into uninfected and infected
 
                     cells. When the average number of bacteria in the cell changes, which means that the λ of Possion
 
                     cells. When the average number of bacteria in the cell changes, which means that the λ of Possion
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                 <p><b>Figure 1. Poisson distribution and <i>Salmonella</i> infection results.</b> <b>a.</b> Based on
 
                 <p><b>Figure 1. Poisson distribution and <i>Salmonella</i> infection results.</b> <b>a.</b> Based on
 
                     statistics of
 
                     statistics of
                     experimental results, we proved that the <i>Salmonella</i> follows Poisson distribution in normal cells.
+
                     experimental results, we proved that the <i>Salmonella</i> follows Poisson distribution in normal
                    <b>b.</b> We assume that the area less than 1 in the Possion distribution is a part of cells which
+
                    cells.
                     are not infected by <i>Salmonella</i>. When the λ of Possion distribution changes, which means the average
+
                    <b>b.</b> We assume that the area less than 1 in the Possion distribution is a part of cells which
                     number of <i>Salmonella</i> in cells changes, the proportion of infected cells changes. <b>c.</b> Cells
+
                     are not infected by <i>Salmonella</i>. When the λ of Possion distribution changes, which means the
 +
                    average
 +
                     number of <i>Salmonella</i> in cells changes, the proportion of infected cells changes. <b>c.</b>
 +
                    Cells
 
                     which are infected by only one <i>Salmonella</i> can also die of pyroptosis.</p>
 
                     which are infected by only one <i>Salmonella</i> can also die of pyroptosis.</p>
 
                 <div class="h2">Infection in tumor cell culture experiments</div>
 
                 <div class="h2">Infection in tumor cell culture experiments</div>
                 <p>We hope that the mathematical model can help the <i>Salmonella</i> infection experiment. In our final
+
                 <p>We hope that the mathematical model can help the <i>Salmonella</i> infection experiment. In our
 +
                    final
 
                     phenotypic experiment, the cells carrying the GSDMD gene are induced by ATc, and we hope that the
 
                     phenotypic experiment, the cells carrying the GSDMD gene are induced by ATc, and we hope that the
 
                     observed result is that the proportion of ATc-induced cell death is more than that of not induced
 
                     observed result is that the proportion of ATc-induced cell death is more than that of not induced
 
                     to prove the ATc promoter is effective. In this experiment, the error may be big if the proportion
 
                     to prove the ATc promoter is effective. In this experiment, the error may be big if the proportion
                     of cells infected by <i>Salmonella</i> is different. What’s even worse is that the experimental results we
+
                     of cells infected by <i>Salmonella</i> is different.</p>
                    observed may be contrary to the actual situation.</p>
+
 
                 <div style="width: 60%; margin: 10px auto">
 
                 <div style="width: 60%; margin: 10px auto">
 
                     <img src="https://static.igem.org/mediawiki/2018/7/73/T--HZAU-China--model2.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/7/73/T--HZAU-China--model2.png" width=100% alt="">
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                     <img src="https://static.igem.org/mediawiki/2018/1/10/T--HZAU-China--model3.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/1/10/T--HZAU-China--model3.png" width=100% alt="">
 
                 </div>
 
                 </div>
                 <p><b>Figure 2.</b> Results caused by efficiency differences of infection. </p>
+
                 <p style="width: 100%; text-align: center !important;"><b>Figure 2.</b> Results caused by efficiency
                <p></p><br>
+
                    differences of infection. </p>
                 <p>The experimental results cannot prove that the pyroptosis is induced by ATc promoter. The picture showed that the
+
                <p></p><br>
 +
                 <p>If the proportions of infections are different, the experimental results may not prove atc promoter
 +
                    induced pyroptosis. The picture showed that the
 
                     ATc promoter is induced and 90% cells death are caused and 70% cells die because of the promoter
 
                     ATc promoter is induced and 90% cells death are caused and 70% cells die because of the promoter
 
                     disclosure, but the difference of the proportion of infected cells is so big that the experimental
 
                     disclosure, but the difference of the proportion of infected cells is so big that the experimental
                     results are contrary to the truth. However, we can solve this problem by improving the proportions
+
                     results are contrary to the truth. We can reduce the difference by getting the infection proportion
                     of infected cells as much as possible.</p>
+
                     of the two kind of cells both close to 100%.</p>
 
                 <p>We solve this problem by predicting the proportion of cells infected with bacteria over time.</p>
 
                 <p>We solve this problem by predicting the proportion of cells infected with bacteria over time.</p>
 
                 <p>Based on these, we designed an App with MatLab (<a href="">https://github.com/cccoolll/Pyroptosis.git</a>)
 
                 <p>Based on these, we designed an App with MatLab (<a href="">https://github.com/cccoolll/Pyroptosis.git</a>)
                     . In this App, different parameters got from experiments can be input to predict the experimental results.
+
                     . In this app, different parameters got from the experiment can be input to predict the
 +
                    experimental results.
 
                     Therefore, the App can provide guidance to our experiments.</p>
 
                     Therefore, the App can provide guidance to our experiments.</p>
 
                 <div style="width: 80%; margin: 30px auto">
 
                 <div style="width: 80%; margin: 30px auto">
 
                     <img src="https://static.igem.org/mediawiki/2018/a/a1/T--HZAU-China--model4.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/a/a1/T--HZAU-China--model4.png" width=100% alt="">
 
                 </div>
 
                 </div>
                 <p><b>Figure 3.</b> The App we designed. </p>
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                <div style="width: 80%; margin: 30px auto">
                 <p>1: The predicted <i>Salmonella</i> numbers in tumor cell and in normal cell in the single cell infection
+
                    <img src="https://static.igem.org/mediawiki/2018/3/3a/T--HZAU-China--model4.1.png" width=100% alt="">
                     experiment. The red curve is for a tumor cell and the blue curve is for a normal cell. <br>
+
                </div>
                     2: The predicted proportions of infected cells (red for tumor cell and blue for normal cell). <br>
+
                 <p style="width: 100%; text-align: center !important;"><b>Figure 3.</b> The App we designed. </p>
                     3: The predicted optimal infection time.<br>
+
                 <p> A: The condition of tumor cells infection experiments.<br>
                     4: The concentration of added <i>Salmonella</i>. <br>
+
                    A1: The predicted <i>Salmonella</i> numbers in tumor cell and in normal cell in the single cell
                     5: The density of tumor cells. <br>
+
                    infection
                     6: Rate constant of <i>Salmonella</i> infecting tumor cells.<br>
+
                     experiment.<br>
                     7: The density of normal cells;<br>
+
                    A2: The predicted proportions of infected tumor cells.<br>
                     8: Rate constant of <i>Salmonella</i> infecting normal cells.<br>
+
                    A3: The concentration of added <i>Salmonella</i>.<br>
                     9: The predicted proportion of the infected tumor cells in the infection experiment.<br> </p>
+
                    A4: The density of tumor cells;<br>
                    The input parameters Nsal, Tumor and As are obtained from our experiments.
+
                    A5: Rate constant of <i>Salmonella</i> infecting tumor cells.<br>
 +
                    A6: The proportion of tumor cells we expect to be infected.<br>
 +
                    A7: The time it takes to reach the proportion we want.<br>
 +
                    B: The condition of mixed culture experiments.<br>
 +
                    B1: The change of the number of <i>Salmonella</i> in a single cell of tumor cell infection
 +
                    experiments,
 +
                    the red curve is the condition in the cancer cell, and the blue curve is the condition within the
 +
                    normal cell.<br>
 +
                     B2: Changes of the proportion of infected cells of tumor cell and normal cell infection
 +
                     experiments, the red curve is the condition in the cancer cell, and the blue curve is the condition
 +
                    in the normal cell.<br>
 +
                     B3: The concentration of added <i>Salmonella</i>.<br>
 +
                     B4: The density of tumor cells.<br>
 +
                     B5: Rate constant of <i>Salmonella</i> infecting tumor cells.<br>
 +
                     B6: The density of normal cells.<br>
 +
                     B7: Rate constant of <i>Salmonella</i> infecting normal cells.<br>
 +
                     B8: The predicted optimal infection time.
 +
                    <br> </p>
 +
                <p>The parameters Nsal、Tumor and As is obtained from our experiments.</p>
 
                 <div class="h3">Guidance to tumor cell infection experiments</div>
 
                 <div class="h3">Guidance to tumor cell infection experiments</div>
 
                 <div style="width: 80%; margin: 30px auto">
 
                 <div style="width: 80%; margin: 30px auto">
 
                     <img src="https://static.igem.org/mediawiki/2018/a/a1/T--HZAU-China--model5.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/a/a1/T--HZAU-China--model5.png" width=100% alt="">
 
                 </div>
 
                 </div>
                 <p><b>Figure 4 (a part of Figure 3).</b> The input parameters and the predicted optimal infection time and tumor infection rate curve. <br><br>  
+
                 <p><b>Figure 4 (a part of Figure 3).</b> Guidance for tumor cells infecting experiments.<br><br>
                  According to our experiment protocol, the MOI (multiplicity of infection) is 100, corresponding to the concentration of cells. The result showed that the infection time is at least 2 hours to eliminate unnecessary variables.</p>
+
                    According to our experimental protocol, the MOI is 100 and the we correspond the MOI to
 +
                    concentration of cells. We want the 98% of the tumor cells to be infected.</p>
 
                 <div class="h3">Guidance to mixed culture experiments</div>
 
                 <div class="h3">Guidance to mixed culture experiments</div>
 
                 <div style="width: 60%; margin: 30px auto">
 
                 <div style="width: 60%; margin: 30px auto">
 
                     <img src="https://static.igem.org/mediawiki/2018/b/b7/T--HZAU-China--model6.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/b/b7/T--HZAU-China--model6.png" width=100% alt="">
 
                 </div>
 
                 </div>
                 <p><b>Figure 5 (a part of Figure 3).</b> The predicted proportions of infected cells (red for tumor cell and blue for normal cell). <br><br>
+
                 <p><b>Figure 5 (a part of Figure 3).</b> The predicted proportions of infected cells (red for tumor
 +
                    cell and blue for normal cell). <br><br>
 
                     In order to reflect the affinity of <i>Salmonella</i> to tumor cells and to normal cells, we
 
                     In order to reflect the affinity of <i>Salmonella</i> to tumor cells and to normal cells, we
                     hope that the difference between experimental results of tumor cells and normal cells is obvious. However, the number of bacteria in different cells is difficult to count, and we can only count the number of infected cells and calculate the proportion of infected cells. Therefore, we need to predict the time when the difference is most obvious. Our App just can do this for us. </p>
+
                     hope that the difference between experimental results of tumor cells and normal cells is obvious.
 +
                    However, the number of bacteria in different cells is difficult to count, and we can only count the
 +
                    number of infected cells and calculate the proportion of infected cells. Therefore, we need to
 +
                    predict the time when the difference is most obvious. Our App just can do this for us. </p>
  
  
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                 <div class="h3">Equations<sup>5</sup>:</div>
 
                 <div class="h3">Equations<sup>5</sup>:</div>
 
                 <p>Based on Hill function, we can determine the amount of activated tetR, tetR<sub>act</sub>:</p>
 
                 <p>Based on Hill function, we can determine the amount of activated tetR, tetR<sub>act</sub>:</p>
                 <p>$$ tetR_{act} + n \times S(t) \rightarrow [tetR - S(t)_n] $$</p>
+
                 <p>$$ tetR_{act} + n \times S_x(t) \rightarrow [tetR - S_x(t)_n] $$</p>
                 <p>$$ K_X = \dfrac {tetR_{act} \times S^n (t)} {[tetR - S(t)_n]} $$</p>
+
                 <p>$$ K_X = \dfrac {tetR_{act} \times S_x^n (t)} {[tetR - S_x(t)_n]} $$</p>
                 <p>$$ tetR = tetR_{act} + [tetR - S(t)_n] $$</p>
+
                 <p>$$ tetR = tetR_{act} + [tetR - S_x(t)_n] $$</p>
                 <p>$$ tetR_{act} = \dfrac {tetR} {1 + \dfrac {S^n (t)} {K_{X}}} $$</p>
+
                 <p>$$ tetR_{act} = \dfrac {tetR} {1 + \dfrac {S_x^n (t)} {K_{X}}} $$</p>
 
                 <p>Based on Hill function, we can determine the amount of activated promoter, with which we can
 
                 <p>Based on Hill function, we can determine the amount of activated promoter, with which we can
 
                     calculate the total transcription speed of all promoters per cell:</p>
 
                     calculate the total transcription speed of all promoters per cell:</p>
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                     0.009 \), \(K_{deg\_tetR} = 0.631 \), \(K_{trans\_tetR} = 235.5 \) <b>(All units are combined of
 
                     0.009 \), \(K_{deg\_tetR} = 0.631 \), \(K_{trans\_tetR} = 235.5 \) <b>(All units are combined of
 
                         nM and s)</b>. Considering that both <i>Salmonella</i>
 
                         nM and s)</b>. Considering that both <i>Salmonella</i>
                     and <i>E. coli</i> are in <i>Enterobacteriaceae</i>, we assumed that in <i>Salmonella</i> these parameters are the same
+
                     and <i>E. coli</i> are in <i>Enterobacteriaceae</i>, we assumed that in <i>Salmonella</i> these
 +
                    parameters are the same
 
                     with those in <i>E. coli</i> since we just wanted to figure out a useful instruction to wet lab.</p>
 
                     with those in <i>E. coli</i> since we just wanted to figure out a useful instruction to wet lab.</p>
 
                 <p>To gain the parameters in bacteria growth curve, we carried out an experiment to measure the growth
 
                 <p>To gain the parameters in bacteria growth curve, we carried out an experiment to measure the growth
                     of <i>Salmonella</i>. Then we fitted the obtained data into a logistics model. By doing these we figured out that
+
                     of <i>Salmonella</i>. Then we fitted the obtained data into a logistics model. By doing these we
 +
                    figured out that
 
                     \(r = 60min^{-1} \), \(K_{max} = 0.9997 \) and \(C = 7.2319 \). Results and diagram are shown below
 
                     \(r = 60min^{-1} \), \(K_{max} = 0.9997 \) and \(C = 7.2319 \). Results and diagram are shown below
 
                     (<b>Figure 5</b>):</p>
 
                     (<b>Figure 5</b>):</p>
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                 <p>After complete the work above, we used MATLAB<sup>TM</sup> to solve the equations above and
 
                 <p>After complete the work above, we used MATLAB<sup>TM</sup> to solve the equations above and
                     acquired a series of diagrams which visually demonstrated the relationships and helped the wet lab group get
+
                     acquired a series of diagrams which visually demonstrated the relationships and helped the wet lab
 +
                    group get
 
                     an overall view of how ATc influences the expression of GSDMD. We assumed that \(P_{tet\_copy} =
 
                     an overall view of how ATc influences the expression of GSDMD. We assumed that \(P_{tet\_copy} =
 
                     4
 
                     4
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                     <img src="https://static.igem.org/mediawiki/2018/7/79/T--HZAU-China--ATC2.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/7/79/T--HZAU-China--ATC2.png" width=100% alt="">
 
                 </div>
 
                 </div>
                 <p style="width: 100%; text-align: center !important;"><b>Figure 6.</b> Concentration of tetR (nM) - time (s).</p><br>
+
                 <p style="width: 100%; text-align: center !important;"><b>Figure 6.</b> Concentration of tetR (nM) -
 +
                    time (s).</p><br>
 
                 <div style="width: 80%; margin: 0px auto">
 
                 <div style="width: 80%; margin: 0px auto">
 
                     <img src="https://static.igem.org/mediawiki/2018/9/90/T--HZAU-China--ATC3.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/9/90/T--HZAU-China--ATC3.png" width=100% alt="">
 
                 </div>
 
                 </div>
                 <p style="width: 100%; text-align: center !important;"><b>Figure 7.</b> Concentration of GSDMD (nM) - time (s).</p><br>
+
                 <p style="width: 100%; text-align: center !important;"><b>Figure 7.</b> Concentration of GSDMD (nM) -
 +
                    time (s).</p><br>
 
                 <div style="width: 80%; margin: 0px auto">
 
                 <div style="width: 80%; margin: 0px auto">
 
                     <img src="https://static.igem.org/mediawiki/2018/5/55/T--HZAU-China--ATC4.png" width=100% alt="">
 
                     <img src="https://static.igem.org/mediawiki/2018/5/55/T--HZAU-China--ATC4.png" width=100% alt="">
 
                 </div>
 
                 </div>
  
                 <p style="width: 100%; text-align: center !important;"><b>Figure 8.</b> Max concentration of GSDMD (nM) -
+
                 <p style="width: 100%; text-align: center !important;"><b>Figure 8.</b> Max concentration of GSDMD (nM)
 +
                    -
 
                     time (s).</p><br>
 
                     time (s).</p><br>
  
 
                 <p> With this software, one can adjust all the parameters needed in the equations above and
 
                 <p> With this software, one can adjust all the parameters needed in the equations above and
 
                     attain the diagrams which indicates the relations between concentration of GSDMD and time,
 
                     attain the diagrams which indicates the relations between concentration of GSDMD and time,
                     concentration tetR and time and the maximum value of GSDMD and the initial concentration of ATc. The
+
                     concentration tetR and time and the maximum value of GSDMD and the initial concentration of ATc.
                     program will also generate a function describing the relationship between the maximum concentration of
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                    The
 +
                     program will also generate a function describing the relationship between the maximum concentration
 +
                    of
 
                     GSDMD and the concentration of ATc. With the help of this program, members in wet lab group can
 
                     GSDMD and the concentration of ATc. With the help of this program, members in wet lab group can
 
                     conveniently decide how much ATc should be added into cultivation environment according to their
 
                     conveniently decide how much ATc should be added into cultivation environment according to their
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                 </p>
 
                 </p>
 
                 <div class="h2">The source code of the software and the scripts used above can be found with this link:</div>
 
                 <div class="h2">The source code of the software and the scripts used above can be found with this link:</div>
                 <p><a href="https://github.com/tom13amy/atc_modelling_software">https://github.com/tom13amy/atc_modelling_software</a></p>  
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                 <p><a href="https://github.com/tom13amy/atc_modelling_software">https://github.com/tom13amy/atc_modelling_software</a></p>
  
 
             </div>
 
             </div>

Revision as of 17:01, 17 October 2018

Salmonella infection model

We want to simulate the situation that tumor cells and Salmonella together in a liquid environment. We used the law of mass action to establish a model for the infection process of Salmonella, which is dimensionless.

$$N_{normal} + S_{almonella} \overset{Aw}{\rightarrow} N_{w} + S_{al\_normal}$$

$$N_{tumor} + S_{almonella} \overset{As}{\rightarrow} N_{s} + S_{al\_tumor}$$

$$S_{almonella} = S_{almonella0} - N_{normal\_cell} - N_{tumor}$$

$$\dfrac {dN_{w}} {d_{t}} = A_{w} S_{almonella} N_{w}$$

$$\dfrac {dN_{s}} {d_{t}} = A_{s} S_{almonella} N_{s}$$

$$\dfrac {dS_{almonella}} {d_{t}} = - \dfrac {dN_{w}} {d_{t}} - \dfrac {dN_{s}} {d_{t}}$$

\(N_{normal\_cell}\): The density of normal cells.
\(S_{almonella}\): The density of Salmonella in the liquid environment.
\(N_{tumor}\): The density of tumor cells.
\(N_w\): The number of Salmonella in the normal cells.
\(N_s\): The number of Salmonella in the tumor cells.
\(A_w\): The affinity between Salmonella and normal cells.
\(A_s\): he affinity between Salmonella and tumor cells.

Salmonella begins to replicate two hours after infection1 .

$$\dfrac {dN_{sal}} {d_{t}} = K_{break} N_{s} + N_{sal} 2^{\dfrac {t} {T}} \ln{2} \dfrac {1} {T} $$

Identification of infection time

According to our experimental results, we noticed that Salmonella follows Poisson distribution in normal cells, and an app was designed to judge the distribution of bacteria in the cells. We assume that the area less than 1 in the Poisson distribution is a part of cells which are not infected by Salmonella. According to our experimental results, cells which are infected by only one Salmonella can also die of pyroptosis. Based on this feature, we divide cells into uninfected and infected cells. When the average number of bacteria in the cell changes, which means that the λ of Possion distribution changes, the ratio of the two kind of cells will change. In summary, when the average number of Salmonella in cells changes, the proportion of dead cells will change.

Figure 1. Poisson distribution and Salmonella infection results. a. Based on statistics of experimental results, we proved that the Salmonella follows Poisson distribution in normal cells. b. We assume that the area less than 1 in the Possion distribution is a part of cells which are not infected by Salmonella. When the λ of Possion distribution changes, which means the average number of Salmonella in cells changes, the proportion of infected cells changes. c. Cells which are infected by only one Salmonella can also die of pyroptosis.

Infection in tumor cell culture experiments

We hope that the mathematical model can help the Salmonella infection experiment. In our final phenotypic experiment, the cells carrying the GSDMD gene are induced by ATc, and we hope that the observed result is that the proportion of ATc-induced cell death is more than that of not induced to prove the ATc promoter is effective. In this experiment, the error may be big if the proportion of cells infected by Salmonella is different.

Figure 2. Results caused by efficiency differences of infection.


If the proportions of infections are different, the experimental results may not prove atc promoter induced pyroptosis. The picture showed that the ATc promoter is induced and 90% cells death are caused and 70% cells die because of the promoter disclosure, but the difference of the proportion of infected cells is so big that the experimental results are contrary to the truth. We can reduce the difference by getting the infection proportion of the two kind of cells both close to 100%.

We solve this problem by predicting the proportion of cells infected with bacteria over time.

Based on these, we designed an App with MatLab (https://github.com/cccoolll/Pyroptosis.git) . In this app, different parameters got from the experiment can be input to predict the experimental results. Therefore, the App can provide guidance to our experiments.

Figure 3. The App we designed.

A: The condition of tumor cells infection experiments.
A1: The predicted Salmonella numbers in tumor cell and in normal cell in the single cell infection experiment.
A2: The predicted proportions of infected tumor cells.
A3: The concentration of added Salmonella.
A4: The density of tumor cells;
A5: Rate constant of Salmonella infecting tumor cells.
A6: The proportion of tumor cells we expect to be infected.
A7: The time it takes to reach the proportion we want.
B: The condition of mixed culture experiments.
B1: The change of the number of Salmonella in a single cell of tumor cell infection experiments, the red curve is the condition in the cancer cell, and the blue curve is the condition within the normal cell.
B2: Changes of the proportion of infected cells of tumor cell and normal cell infection experiments, the red curve is the condition in the cancer cell, and the blue curve is the condition in the normal cell.
B3: The concentration of added Salmonella.
B4: The density of tumor cells.
B5: Rate constant of Salmonella infecting tumor cells.
B6: The density of normal cells.
B7: Rate constant of Salmonella infecting normal cells.
B8: The predicted optimal infection time.

The parameters Nsal、Tumor and As is obtained from our experiments.

Guidance to tumor cell infection experiments

Figure 4 (a part of Figure 3). Guidance for tumor cells infecting experiments.

According to our experimental protocol, the MOI is 100 and the we correspond the MOI to concentration of cells. We want the 98% of the tumor cells to be infected.

Guidance to mixed culture experiments

Figure 5 (a part of Figure 3). The predicted proportions of infected cells (red for tumor cell and blue for normal cell).

In order to reflect the affinity of Salmonella to tumor cells and to normal cells, we hope that the difference between experimental results of tumor cells and normal cells is obvious. However, the number of bacteria in different cells is difficult to count, and we can only count the number of infected cells and calculate the proportion of infected cells. Therefore, we need to predict the time when the difference is most obvious. Our App just can do this for us.

Chemical control model
Profile

The Tet repressor protein (tetR) regulates transcription of tetracyclines resistance protein, tetA. The expression of tetA must be strictly regulated since tetA is a membrane-spanning H+-[Tc-Mg]2+ antiporter which means it can lower the pH environment of cytoplasm. As a result, the natural circuit of tetracyclines regulation is a negative-feedback circuit2. Tc is the inducer, which shows high affinity to tetR protein. The tetR protein binds to tetO sequence on DNA specifically, thus inhibits the expression of Tet promoter. When Tc or other similar molecules like ATc (anhydrotetracycline) diffuse into bacteria, they will bind to tetR protein and unleash the tetR protein from DNA, and thus relieve the inhibition and start the expression of Tet promoter.

In our project, we choose ATc (anhydrotetracyclines) as the inducer. ATc is less harmful to bacteria than Tc and about 100-fold higher affinity to tetR than Tc2.

The ATc model aims to predict and solve two problems: first, how fast does the circuit react to ATc; second, how many target gene will express in the bacteria community under a certain concentration of ATc.

Hypothesis

There are two tetO sites on the Tet promoter and both can bind to tetR protein randomly and inhibit the promoter’s expression independently. To make the condition simple, we consider the two tetO sites into one as we just want to explain the relationship between the promoter inhibition and the tetR protein expression.

In our project, the ATc concentration in our incubation environment is uniform, and the diffusion rate of anhydrotetracycline can be ignored3. In spite of this, the degradation rate of ATc under 37℃ must be taken into account as reported4.

Based on these facts, we give the following hypotheses:

1. Regard two tetO operons as one equivalently.
2. Ignore the diffusion of ATc.
3. The reaction time between ATc and tetR, tetR and DNA is much shorter than transcription and translation.

Description and Equation
Reactions implicated:

$$tetR + [tetR - ATc_2] = tetR_{total}$$

$$tetR + 2 \times ATc = [tetR - ATc_2]$$

$$P_{tet} + [tetR_2 - P_{tet}] = [P_{tet}]_{total}$$

$$2 \times tetR + P_{tet} = [tetR_2 - P_{tet}]$$

$$$$

Equations5:

Based on Hill function, we can determine the amount of activated tetR, tetRact:

$$ tetR_{act} + n \times S_x(t) \rightarrow [tetR - S_x(t)_n] $$

$$ K_X = \dfrac {tetR_{act} \times S_x^n (t)} {[tetR - S_x(t)_n]} $$

$$ tetR = tetR_{act} + [tetR - S_x(t)_n] $$

$$ tetR_{act} = \dfrac {tetR} {1 + \dfrac {S_x^n (t)} {K_{X}}} $$

Based on Hill function, we can determine the amount of activated promoter, with which we can calculate the total transcription speed of all promoters per cell:

$$ P_{tet\_act} + n\cdot tetR_{act} \rightarrow [P_{tet} - (tetR_{act})_n] $$

$$ P_{tet\_copy} = P_{tet\_act} + [P_{tet} - (tetR_{act})_n] $$

$$ K_d = \dfrac {P_{tet\_copy} \times tetR^n_{act} } {[P_{tet} - (tetR_{act})_n]} $$

$$ A_{mRNA} = P_{tet\_act} \times beta $$

$$ A_{mRNA} = \dfrac {P_{tet\_copy} \times beta } { 1 + \dfrac {tetR^n_{act}} {K_{d}}} $$

Kinetic equations of transcription and translation:

$$ \dfrac {dmRNA} {dt} = A_{mRNA} - K_{deg\_mRNA} \times mRNA $$

$$ \dfrac {dtetR} {dt} = K_{trans\_tetR} \times mRNA - K_{deg\_tetR} \times tetR $$

$$ \dfrac {dGSDMD} {dt} = K_{trans\_GSDMD} \times - K_{deg\_GSDMD} \times GSDMD $$

Degradation function of ATc by time3:

$$ \dfrac {dS_x(t)} {d_t} = -K_{deg\_ATc} \times S_x(t) $$

$$ \ln(S_x(t)) = \ln(S_x(0)) - K_{deg\_ATc} \times t $$

Growth curve of bacteria based on logistics model from P. F. Verhulst:

$$ N(t) = \dfrac {K_{max}} {1 + C \cdot e^{-rt}} $$

Total GSDMD expressed in bacteria community:

$$ GSDMD_{total} = N(t) \cdot GSDMD $$

The symbols in the equations:

\(S_x(t)\): concentration of ATc, as a function of time.
\(tetR_{act}\): concentration of activated tetR.
\(tetR \): concentration of total tetR.
\(GSDMD \): concentration of GSDMD.
\(A_{mRNA} \): transcription rate constant of the promoter.
\(P_{tet\_copy} \): plasmid copy number.
\(K_X \): disassociation rate constant of tetR and ATc.
\(K_d \): disassociation rate constant of tetR and DNA.
\(beta \): original (unrepressed) transcription rate constant of the promoter.
\(K_{deg\_mRNA} \): degradation rate constant of mRNA.
\(K_{deg\_tetR} \): degradation rate constant of tetR.
\(K_{trans\_tetR} \): translation rate constant of tetR.
\(mRNA \): concentration of mRNA.
\(K_{deg\_GSDMD} \): degradation rate constant of GSDMD.
\(K_{trans\_GSDMD} \): transcription rate constant of GSDMD.
\(K_{deg\_ATc} \): degradation rate constant of ATc.
\(n \): Hill coefficient.
\(N(t) \): initial OD600 value of the bacteria.
\(r \): growth rate of the bacteria.
\(K_{max} \): maximum OD of the bacteria in cultivation.

Suggestions to our experiments (see Results)

As is hard to obtain the initial parameters in the equations above on our own without any experiments, the only way to obtain these parameters is to look up in former research or other teams work. Fortunately we got a copy of these parameters from team William and Mary iGEM 20166. These parameters include \(K_X = 0.36 \), \(K_d = 0.1 \), \(beta = 0.0023 \), \(K_{deg\_mRNA} = 0.009 \), \(K_{deg\_tetR} = 0.631 \), \(K_{trans\_tetR} = 235.5 \) (All units are combined of nM and s). Considering that both Salmonella and E. coli are in Enterobacteriaceae, we assumed that in Salmonella these parameters are the same with those in E. coli since we just wanted to figure out a useful instruction to wet lab.

To gain the parameters in bacteria growth curve, we carried out an experiment to measure the growth of Salmonella. Then we fitted the obtained data into a logistics model. By doing these we figured out that \(r = 60min^{-1} \), \(K_{max} = 0.9997 \) and \(C = 7.2319 \). Results and diagram are shown below (Figure 5):

Figure 5. Bacteria growth curve.


After complete the work above, we used MATLABTM to solve the equations above and acquired a series of diagrams which visually demonstrated the relationships and helped the wet lab group get an overall view of how ATc influences the expression of GSDMD. We assumed that \(P_{tet\_copy} = 4 \), \(K_{deg\_GSDMD} = 0.8 \), \(K_{trans\_GSDMD} = 200 \), \(K_{deg\_ATc} = 0.0007 \) (All unites are combined of nM and s). Results are shown below (Figures 6, 7, 8.):


Figure 6. Concentration of tetR (nM) - time (s).


Figure 7. Concentration of GSDMD (nM) - time (s).


Figure 8. Max concentration of GSDMD (nM) - time (s).


With this software, one can adjust all the parameters needed in the equations above and attain the diagrams which indicates the relations between concentration of GSDMD and time, concentration tetR and time and the maximum value of GSDMD and the initial concentration of ATc. The program will also generate a function describing the relationship between the maximum concentration of GSDMD and the concentration of ATc. With the help of this program, members in wet lab group can conveniently decide how much ATc should be added into cultivation environment according to their requirements (Figures 9, 10.).


Figure 9. Software parameters.


Figure 10. Software diagrams.


Significance

The model of ATc induced circuit is very common and well-known to biology researchers. The common-known significance to this model is that it can demonstrate the relationship between concentration of target gene and concentration of inducer added, which can instruct the researchers modulate their circuit precisely. In our project, this model will tell the members in wet lab group that how much GSDMD will be expressed under a certain concentration of ATc in the Salmonella community formed in the tumor cell. Another significance for this model is that, the response time is very short and the response speed is extremely fast. We anticipate that just minutes are needed to induce the fluorescence. This phenomenon is also verified in our experiment. In less than 10 minutes, fluorescence can be detected under fluorescence microscope. Especially, a remarkable significance to our project is that it’s a self-destructive system, which means, without any further operation, the process of induction can be self-terminated. As ATc degrades, the expression of GSDMD will significantly decreases, thus the process of pyroptosis can be inhibited. Based on the features, we think that the cytokine storm caused by pyroptosis is controllable.

The source code of the software and the scripts used above can be found with this link:

https://github.com/tom13amy/atc_modelling_software

Reference

1. I. Hautefort, A. Thompson, et al. During infection of epithelial cells Salmonella enterica serovar Typhimurium undergoes a time-dependent transcriptional adaptation that results in simultaneous expression of three type 3 secretion systems. Cellular Microbiology 10(4), 958–984 (2008).

2. Berens, C. & Hillen, W. Gene regulation by tetracyclines: Constraints of resistance regulation in bacteria shape TetR for application in eukaryotes. Eur. J. Biochem. 270, 3109–3121 (2003).

3. Nevozhay, D., Adams, R. M., Murphy, K. F., Josic, K. & Balazsi, G. Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. Proc. Natl. Acad. Sci. 106, 5123–5128 (2009).

4. Politi, N. et al. Half-life measurements of chemical inducers for recombinant gene expression. J. Biol. Eng. 8, 1–10 (2014).

5. Alon, U. An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman & Hall/CRC Mathematical and Computational Biology).Pdf.

6. William and Mary iGEM 2016. A Kinetic Model of Molecular Titration. 1–11 (2016).

Model

Salmonella infection model

Chemical control model

Reference

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