Revision as of 21:25, 1 November 2017

Modelling the Behavior of CATE inside Tumor

We developed a model to gauge the behavior of our circuit in the real life conditions of solid tumor colonization.

This page gives details about the 3D model we developed to simulate the behavior of CATE inside the tumor and healthy tissue with different colonization patterns. This section presents details about the following:

The Geometry used in our 3D model.
The domain-wise Partial Differential Equations used for modelling the Growth, AND-gate Tumor sensing Switch and the Lysis & Diffusion effects.
Values of the Parameters used
Simulation Results for the AND-Gate tumor sensing switch in the different environmental conditions to validate its functioning in context of the intended application.

The 3D model presented here is used to model the behavior in conditions as close as possible to real-life scenario of tumor colonization by including the partial differential equations for diffusion of AHL and Azuin, which also helps validate the assumptions and simplifications used in the in-vitro model. Since in experiments, there is no diffusion, our 3D model helps us model scenarios that CATE will encounter, as close as possible to reality. We are also able to test the behavior of our tumor sensor. We use the parameters obtained by fitting our experimental data and also finally integrating our self-designed hybrid promoter.

Geometry

Our model structure consists of 2 domains - Tumor and Layer.

As mentioned in system specifications, the tumour has been chosen as a solid sphere of radius 20 mm and the bactierial colonization pattern as a homogenous distribution in a spherical shell-shaped 0.5 mm thick layer in the tumour at a distance of 10 mm from the centre of the tumor, as shown in Figure 1.

Geometry of tumor and bacterial colony — Figure 1: Geometry of the tumor and bacteria colony (green area: colonized by E. coli Nissle)

Details about the geometry

Due to the spherical symmetry of the system, a 2D axisymmetric COMSOL model was used as shown in Figure 2 - a semicircle of radius 20 mm represents the tumor and the 0.5 mm thick layer at a distance of 10 mm from the center of the tumor represents the bacteria colonization pattern as explained in the system specifications. COMSOL then sweeps the semi-circle between 0° and 360 ° to simulate the entire 3D problem. The symmetry helps decrease the computational time and space requirements, without having to apply simplifications based on assumptions.

Equations

Transport of Diluted Species physics was used in COMSOL to integrate diffusion into our model. The partial differential equation for diffusion of a species C with reaction source rate R_C is \[\frac{\partial \text{[C]}}{\partial t} + \nabla \cdot (-D_{\text{C}} \nabla \text{[C]})= R_{\text{C}}\]. The reaction rates of the species depends on the domain – tumor: no production and only extracellular degradation, and bacterial layer: production and intracellular degradation.

Expand the sections below for more details about the domain-wise reaction rates for each species (AHL, LuxI and Azurin) and equations used.

Disclaimer

The concentration of LuxI, LuxR and LuxR-AHL modelled above represents the intracellular concentration i.e. the concentration at a point had a bacteria been there.

However, since AHL diffuses everywhere rapidly and freely through the cell-memberane, a rapid equilibrium between the intercellular and extracellular concentration of AHL is reached, and thus we multiply the LuxI controlled AHL production rate by d_cell, since the layer has all the bacteria colonizing the tumor.

Similarly, since we are interested in the Azurin concentration after lysis, we have a d_cell multiplication factor in its reaction rate, as after lysis Azurin from all the cells diffused out of the layer. This is also then used to find the effective killing area to estimate the percentage of volume of the tumor treated.

Growth Model

An exponential growth model is used, as shown below.

\[\begin{aligned} \frac{\mathrm{d} d_{\text{cell}}}{\mathrm{d} t} &= \frac{1}{\tau} d_{\text{cell}} \left ( 1-\frac{d_{\text{cell}}}{d_{\text{cell,ss}}} \right ) \\ \Rightarrow d_{\text{cell}} &= \frac{ d_{\text{cell,0}} \, e^{\frac{t}{\tau}}}{1 - \frac{d_{\text{cell,0}}}{d_{\text{cell,ss}}} + \frac{d_{\text{cell,0}}}{d_{\text{cell,ss}}}e^{\frac{t}{\tau}}} \end{aligned}\]

Diffusion Model

Transport of Diluted Species physics of COMSOL was used to model the diffusion of AHL and Azurin. The equations below represent the diffusion models in both the Tumor and Layer domains. For simulating the effect of lysis, the diffusion of Azurin is triggered when temperature reaches 42°C, while AHL diffuses all the time. The value of the diffusion coefficients of AHL, D and Azurin, D_Azu are given in the parameter list below.

\[\begin{aligned} \frac{\partial \text{[AHL]}}{\partial t} + \nabla \cdot (-D_{\text{AHL}} \nabla\text{[AHL]}) &= R_{\text{AHL}} \\ \frac{\partial \text{[Azu]}}{\partial t} + \nabla \cdot (-D_{\text{Azu}} \nabla\text{[Azu]}) &= R_{\text{Azu}} \end{aligned}\]

Reaction Rates, R_C = d[C]/dt

Inside the Layer domain: AHL, LuxI and Azurin are both produced and degraded. This is modelled as:

\[\begin{aligned} R_{\text{AHL}} &= d_{\text{cell}}(t)\, a_{\text{AHL}} \text{[LuxI]} - d_{\text{AHL,out}}\text{[AHL]} \\ R_{\text{LuxI}} &= a_{\text{LuxI}} \, [k_{\text{LuxI}}+(1-k_{\text{LuxI}})P_{\text{Lux-}\text{Lac}}] - d_{\text{LuxI}}\text{[LuxI]} \\ R_{\text{Azu}} &= d_{\text{cell}}(t)\, k_{\text{Azu-LuxI}}\,(R_{\text{LuxI}}+d_{\text{LuxI}}\text{[LuxI]}-d_{\text{Azu}}\text{[Azu]} ) \\ \end{aligned}\]

where P_Lux-Lac is given by:

\[\begin{aligned} P_{\text{Lux-}\text{Lac}} &= P_{\text{Lux}}P_{\text{Lac}} \\ \text{where } P_{\text{Lux}} &= \frac{\left(\frac{[\text{LuxR-AHL}]}{K_{\text{LuxR}}} \right)^{n_{\text{LuxR}}}}{1 + \left(\frac{[\text{LuxR-AHL}]}{K_{\text{LuxR}}} \right)^{n_{\text{LuxR}}}} \\ \text{and } P_{\text{Lac}} &= \frac{\left(\frac{\text{[Lac]}}{K_{\text{Lac}}} \right)^{n_{\text{Lac}}}}{1 + \left(\frac{[\text{Lac}]}{K_{\text{Lac}}} \right)^{n_{\text{Lac}}}} \end{aligned}\]

where [Lac] is 1 mM in a healthy tissue and 5 mM in a tumor, as already mentioned in the system specifications.

[LuxR-AHL] is obtained by soliving the following 2 equations:

\[\begin{aligned} \text{[LuxR-AHL]} &= K_{\text{LuxR-AHL}} [\text{LuxR}]^2 [\text{AHL}]^2 &\text{(rapid binding equilibrium)} \\ \text{[LuxR]} &= [\text{LuxR}]_0 - 2 [\text{LuxR-AHL}] &\text{(mass conservation)} \end{aligned} \]

Inside the Tumor domain: AHL, LuxI and Azurin are not produced and only degraded. This is modelled as:

\[\begin{aligned} R_{\text{AHL}} &= - \, d_{\text{AHL,out}}\text{[AHL]} \\ R_{\text{LuxI}} &= 0 \\ R_{\text{Azu}} &= - \, d_{\text{Azu}}\text{[Azu]} ) \\ \end{aligned}\]

Parameters

The parameters that were used in our 3D model were obtained partly from literature, partly from characterizations of previous iGEM teams and finally the most important ones were estimated by fitting our experimental data and tuning the fitted-results in the context of the intended applciation, as explained in detail by the Functional Parameter Search.

Expand the details section to read more about the different parameters and their values used.

Learn more about the parameters in the Functional Parameter Search.

The steady state cell density for growth, d_cell,ss is chosen to be 0.05 in colonized tumor and 0.0005 in healthy tissue, based on the conclusions derived in the System Specifications. Also, [Lac] is chosen to be 1 mM for a healthy tissue and 5 mM for a tumor, and the initial cell density d_cell,0 is taken to be a very small non-zero value.

Parameters fitted to our experimental data and tuned with respect to the context of the intended application

Symbol	Description	Value	Reference
a_LuxR	Maximum expression of LuxR	1x10² nM min^-1	iGEM ETH 2014
a_LuxI	Maximum expression of LuxI	1x10⁴ nM min^-1	[3]
K_Lac	Half-activation [Lac] of the hybrid promoter, P_Lux-Lac	2 mM	Characterized lactate sensing part on which our AND-gate is based
k_LuxI	Leakiness of the hybrid promoter	0.01	Characterized lactate sensing part on which our AND-gate is based
K_LuxR	Half-activation [LuxR-AHL] of the hybrid promoter P_Lux-Lac	10 nM	iGEM ETH 2013
n_LuxR	Hill coefficient of the hybrid promoter, P_Lux-Lac regarding [LuxR-AHL]	1.7	iGEM ETH 2015
n_Lac	Hill coefficient of the hybrid promoter, P_Lux-Lac regarding [Lac]	1.7	iGEM ETH 2015
τ	Doubling time of E. coli Nissle	80 min	Fitted from our growth experiments
k_Azu-LuxI	Relative expression of Azurin compared to LuxR	10	estimated

Fixed parameters - well known

Symbol	Description	Value	Reference
a_AHL	AHL synthesis rate by LuxI	0.01 min^-1	[1]
d_AHL,out	AHL extracellular degradation rate	5x10^-4 min^-1	[2]
D_AHL	AHL diffusion coefficient in water	3x10^-8 m²min^-1	[2]
K_LuxR-AHL	LuxR-AHL quadrimer binding constant	5x10^-10 nM^-3	[3]

Fixed parameters - not very well known but redundant with respect to other parameters

Symbol	Description	Value	Reference
d_LuxI	LuxI degradation rate	0.0167 min^-1	[2]
d_LuxR	LuxR degradation rate	0.023 min^-1	[2]
d_Azu	Azurin degradation rate	0.046 min^-1	estimated
D_Azu	Diffusion coefficient of Azurin	1x10^-6 cm²s^-1	estimated
[LuxR]₀	Total initial concentration of LuxR	a_LuxR/d_LuxR	chosen same as steady state value

RESULTS

We could simulate for a geometry of the system closer to the real-life tumor conditions

Since it was not practically feasible to conduct experiments of bacterial colonization inside tumors, we simulated the bacterial colonization in a thin spherical layer inside a solid tumor considering the simplifications and assumptions as mentioned in the system specifications. This helped us to test our tumour sensing AND-Gate switch functionality in all the possible real-life scenarios that CATE might encounter in context of the intended application.

Exact diffusion physics of AHL was included witout any simplifications

Our MATLAB model uses a simplified AHL diffusion model with the assumption of negligible degradation inside the layer and and not taking into consideration the diffusion of AHL far from the source. Extending the diffusion physics ordinary differential equations into partial differential equations using the COMSOL model helped us gauge and verify the behavior of our tumor-sensing circuit in more real-life conditions pertaining to the intended application context of a solid spherical tumor. Using the results obtained from our simulations, we could check the behavior of the AND Gate Switching in different conditions of d_cell and lactate.

Diffusion physics of Azurin was included to simulate the effect of lysis

To simulate the effect of lysis, our COMSOL model stops the production of Azurin and starts its diffusion when temperature reaches 42°C. This simulates the effect of increase in temperature with FUS to cause cell lysis. Using data obtained from such a simulation, we could also find the temporal-maximum concentrations of Azurin at each point in the tumor, effectively helping us to estimate the killing area and the time-scale of the treatment.

Simulation of different colonization patterns

Using our model, we also tried a few other colonization patterns to show our system works as expected inside a tumor while stays dormant in healthy tissue. We simulated the following patterns:

Homogeneous distribution in a Single spherical-shell-shaped layer in Tumor
Heterogeneous distribution in a Single spherical-shell-shaped layer in Tumor
Heterogeneous distribution in Double spherical-shell-shaped layer in Tumor
Homogeneous distribution in Healthy tissue

Simulating The Treatment phases

Our model helped us to simulate the three main phases of the CATE treatment: Growth, AND-Gate Switching (Environment sensing) and finally Lysis & Azurin Diffusion. For details see CATE in Action.

Phase 1: Growth

The growth is modelled using the exponential growth rate \[\frac{\mathrm{d} d_{\text{cell}}}{\mathrm{d} t} = \frac{1}{\tau} d_{\text{cell}} \left ( 1-\frac{d_{\text{cell}}}{d_{\text{cell,ss}}} \right )\], where τ is the doubling time, that we obtained by fitting E. coli Nissle growth curves from our lab experiments. For more details on the growth equation check the detailed description.

Phase 2: AND-Gate Tumor Sensing Switch

The AND gate Switch senses the environment based on Quorum sensing (d_cell) and Lactatae concentration ([Lac]), as explained in detail in the description of the Tumor Sensing circuit. Based on the different combinations of d_cell and [Lac], as mentioned in the system specifications, the AND gate switches 'ON' or 'OFF'. For detailed equations see the model description details.

ON state:
Rapid and high-fold increase in [LuxI] or [Azu]
OFF state:
Slow and negligible-fold increase in LuxI] or [Azu]

Phase 3: Lysis and Azurin Diffusion

The effect of lysis is simulated by a temperature controlled trigger of the diffusion of azurin that is produced in the cells. The temperature is increased from 0 to 42°C as a step function and when the temperature reaches 42°C, the production of Azurin and AHL stops, since the cells are lysed, and diffusion of Azurin begins which depletes Azurin out of the cell into the tumor, effectively inducing apoptosis of the tumor cells.

The model described above was simulated to test the working of the Tumor Sensing circuit. The three phases, as described above were simulated and the results for bacteria colonization of tumor are shown here.

TurnON — Figure 3: AND-Gate Switch Sensing phase: 35 hr to 69 hr; The rapid increase in [Azu shows the switch ON of the AND-gate tumor sensor

Figure 4: Cell Lysis and Azurin Diffusion phase: 69 hr to 100 hr

HighDHighL_norm — Figure 5: Normalized concentration of AHL and Azurin, Cell density (as a ratio of its steady state value), P_Lux-Lac and Temperature (as a ratio of its steady state value 42°C) probe plots at a point inside the layer of bacterial colonization in the tumor.

In Figure 5, d_cell shows the growth of the cell density inside the layer and P_Lux-Lac shows the main function responsible for the switching functionality based on the environmental conditions of d_cell and lactate.

During the growth phase, our sensing circuit is OFF (visible from Figure 1), and is triggered to turn ON once the desired cell density for quorum sensing is reached at around 40 hr, as shown in Figure 5 (and in Figure 2). Then once steady state is reached, the temperature step triggers the cell-lysis and stops the production of Azurin and AHL. Finally as is visible in Figure 3, all the Azurin diffuses out of the layer very rapidly, thus completing the treatment.

AND-Gate Tumor Sensor Characterization

To test our sensing circuit with the intended application in mind, we use the relative criteria set in the system specifications. Since our AND-Gate switch has 2 inputs for environment sensing viz. d_cell and [Lac], there are 4 possible binary combinations that CATE can encounter in real-life scenario, viz.:

Tumor Colonization: High d_cell AND High [Lac]
Healthy tissue Colonization: High d_cell AND Low [Lac]
Tumor NOT colonized: Low d_cell AND High [Lac]
Healthy tissue NOT colonized: Low d_cell AND Low [Lac]

The simulation results between 35 hr and 75 hr for all the 4 possible scenarios that CATE can encounter, are shown below.

Case1_HighDHighL — Figure 6: Case: Tumor colonized - High d_cell AND High [Lac]; AND-gate Switch is turned ON fully.

Case1_HighDLowL — Figure 7: Case: Healthy tissue colonized - High d_cell AND Low [Lac]; AND-gate Switch is turned ON partially.

Case1_LowDHighL — Figure 8: Case: Tumor NOT colonized - Low d_cell AND High [Lac]; AND-gate Switch is turned OFF fully.

Case1_LowDLowL — Figure 9: Case: Healthy tissue NOT colonized - Low d_cell AND Low [Lac]; AND-gate Switch is turned OFF fully.

As is clear from the Figures 6-9, the AND-gate switch is fully turned ON for the case of tumor colonization with steady state Azurin concentration reaching 75000 nM (as shown in Figure 10). For the case of colonization of healthy tissue, the AND-gate switch is partially turned ON with steady state Azurin concentration at about 22000 nM. This is about 3.5 times lower than when the switch is fully ON. In the case of tumor and healthy tissue not colonized the steady state Azurin concentrations are around 20 nM and 17 nM, respectively, which represents a fully turned OFF AND-gate switch.

Azu_t — Figure 10: [Azu] vs Time with linear scale; Shows the behavior of our tumor sensing circuit in the different d_cell and [Lac] conditions.

Killing Area

Using the COMSOL data, the maximum Azurin concentration over time was found for every point in the system, and the results are as shown in Figure 15.

Kill_area — Figure 16: Killing Area: Points where maximum [Azu] over time exceeds 20 µM.

From Figure 15, the effective killing area can be estimated by choosing the points in the (r,z) plane that have [Azu]_max|t > 20 µM. Figure 16 shows the maximum effective killing area is at z = 0, between 9.1 and 10.1 mm. This amounts to approximately 3.5 % of tumor being treated by volume. So we can conclude, that a more potent cytotoxic agent that Azurin is required to achieve significant tumor treatment using CATE.

Bacterial colonization patterns

To further push the limits of our system, we tested the environment sensing AND-gate switching functioning in different colonization patterns. Although any possible colonization pattern can be simulated, we simulated the logically closest patterns (compared to a spherical shell layer), that we could think of, and introduced heterogenity through partitions.

Case 1: Homogeneous distribution in a Single spherical shell layer in tumour: already simulated as shown in previous results
Case 2: Heterogeneous distribution (Partitions) in a Single spherical shell layer in tumour: another possible feasible scenario
Case 3: Heterogeneous distribution (Partitions) in Double spherical shell layer in tumour: introducing some more heterogenity in the system
Case 4: Homogeneous distribution throughout Healthy tissue: with d_cell = 0.0005, as in this geometry the AND-gate is more easily activated as comparaed to other geometries, since difusion can not occur within the inner region where bacteria colonize, and thus this colonized inner region acts as a huge source, with no diffusion between the cells, as shown in the Figure 15.

The simulation results between 35 hr and 75 hr for all the 4 possible scenarios, are shown below.

Sph_Nopartition — Figure 11: Case 1: Tumor colonized in a single spherical shell-shaped layer; AND-gate Switch is turned ON fully.

Sph_partition — Figure 12: Case 2: Tumor is colonized in partitions in the shape of a single shell-shaped layer; AND-gate Switch is turned ON fully.

DoubleSph_partition — Figure 13: Case 3: Tumor is colonized in alternative paritions in two spherical shell-shaped layers; AND-gate Switch is turned ON fully.

Figure 14: Case 4: Homogeneous colonization (d_cell) of a healthy tissue; AND-gate switch turns ON but only partially.

The above figures show that our Tumor sensing circuit functions properly in the different colonization patterns simulated. According to Figure 14, in case of colonization of a healthy tissue, the switch is only partially triggered (slower rise and lesser final steady state [Azu]), when compared to Case 1 in Figure 12, that has a fully ON switch indicated by the rapid rise and approximately 3.5 times higher-fold increase in [Azu]

Final Conclusions

Our model finally helped us implement a comprehensive in-silico test to prove that our system already exhibits an excellent performance for the clinical application it has been designed for. This helped us to take into account the fitting of the parameters using our experimental data and integrate our own hybrid promoter tuned in reference to the intended application context, to verify the functioning of our environment sensing function.

Limitations of our Model

Our model has some limitations. We do not model protein E production and cell lysis caused by it. Instead lysis is just simulated in effect as the end of production of AHL and Azurin and start of diffusion of Azurin. Moreover, a step signal is used as a trigger for the lysis. As mentioned in the parameters description, Azurin production is taken to be 10 times proportional to LuxI production. Also, killing mechanism of Azurin has not been modelled since that was not necessary to demonstrate the working of our project CATE in the scope of iGEM.

Tools used

COMSOL Multiphysics 5.2a by COMSOL Inc.
MATLAB R2016b by MathWorks

References

Jordi Garcia-Ojalvo, Michael B. Elowitz, and Steven H. Strogatz Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing PNAS 2004 101 (30) 10955-10960 | doi:10.1073/pnas.0307095101
A.B. Goryachev, D.J. Toh T.Lee, Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants Biosystems, Volume 83, Issues 2–3, February–March 2006, Pages 178-187 | doi:10.1016/j.biosystems.2005.04.006
A synthetic multicellular system for programmed pattern formation Subhayu Basu, Yoram Gerchman, Cynthia H. Collins, Frances H. Arnold & Ron WeissNature 434, 1130-1134 (28 April 2005) | doi:10.1038/nature03461

@@ Line 21: / Line 21: @@
          <ul>
              <li>
-                 The Geometry used in the COMSOL model.<br>
+                 The Geometry used in our 3D model.<br>
                  <p class="description"></p>
              </li>
@@ Line 37: / Line 37: @@
              </li>
          </ul>
-         <p><em>The 3D model presented here is used to model the behavior in conditions as close as possible to real-life scenario of tumor colonization by including the partial differential equations for diffusion of AHL and Azuin, which also helps validate the assumptions and simplifications used in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/model"><em>in-vitro</em> model</a>. Since in experiments, there is no diffusion, our 3D model helps us model scenarios that CATE will encounter, as close as possible to reality. We are also able to test the behavior of our <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor">tumor sensor</a>.</em></p>
+         <p><em>The 3D model presented here is used to model the behavior in conditions as close as possible to real-life scenario of tumor colonization by including the partial differential equations for diffusion of AHL and Azuin, which also helps validate the assumptions and simplifications used in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/model"><em>in-vitro</em> model</a>. Since in experiments, there is no diffusion, our 3D model helps us model scenarios that CATE will encounter, as close as possible to reality. We are also able to test the behavior of our <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor">tumor sensor</a>.</em> We use the parameters obtained by <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting">fitting our experimental data</a> and also finally integrating our <em>self-designed</em> <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting">hybrid promoter</a>.</p>
      </section>
@@ Line 68: / Line 68: @@
          <h1>Equations</h1>
          <p class="description">
              Transport of Diluted Species physics was used in COMSOL to integrate diffusion into our model. The partial differential equation for diffusion of a species <em>C</em> with reaction source rate <em>R<sub>C</sub></em>  is <span class="math">\[\frac{\partial \text{[C]}}{\partial t} + \nabla \cdot (-D_{\text{C}} \nabla \text{[C]})= R_{\text{C}}\]</span>. The reaction rates of the species depends on the domain – tumor: no production and only extracellular degradation, and bacterial layer: production and intracellular degradation.
          </p>
          <p>Expand the sections below for more details about the domain-wise reaction rates for each species (AHL, LuxI and Azurin) and equations used.</p>
@@ Line 74: / Line 74: @@
          <p>The concentration of LuxI, LuxR and LuxR-AHL modelled above represents the intracellular concentration i.e. the concentration at a point had a bacteria been there.<p>
          <p>However, since AHL diffuses everywhere rapidly and freely through the cell-memberane, a rapid equilibrium between the intercellular and extracellular concentration of AHL is reached, and thus we multiply the LuxI controlled AHL production rate by <em>d</em><sub>cell</sub>, since the layer has all the bacteria colonizing the tumor.</p>
-         <p>Similarly, since we are interested in the Azurin concentration after lysis, we have a <em>d</em><sub>cell</sub> multiplication factor in its reaction rate, as after lysis Azurin from all the cells diffused out of the layer. This is also then used to find the </p>
+         <p>Similarly, since we are interested in the Azurin concentration after lysis, we have a <em>d</em><sub>cell</sub> multiplication factor in its reaction rate, as after lysis Azurin from all the cells diffused out of the layer. This is also then used to find the effective killing area to estimate the percentage of volume of the tumor treated.</p>
          <div class="multi-summary">
              <details>
@@ Line 158: / Line 158: @@
              </details>
          </div>
+    </section>
+    <section id="InVivo_Parameters">
+        <h1>Parameters </h1>
+        <p class="description">The parameters that were used in our 3D model were obtained partly from literature, partly from characterizations of previous iGEM teams and finally the most important ones were estimated by fitting our experimental data and tuning the fitted-results in the context of the intended applciation, as explained in detail by the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">Functional Parameter Search</a>.</p>
+        <p>Expand the details section to read more about the different parameters and their values used.</p>
+        <p>Learn more about the parameters in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">Functional Parameter Search</a>.</p>
+        <div class="multi-summary">
+            <p>The steady state cell density for growth, d<sub>cell,ss</sub> is chosen to be 0.05 in colonized tumor and 0.0005 in healthy tissue, based on the conclusions derived in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/system_specifications">System Specifications</a>. Also, [Lac] is chosen to be 1 mM for a healthy tissue and 5 mM for a tumor, and the initial cell density d<sub>cell,0</sub> is taken to be a very small non-zero value.</p>
+            <details>
+                <summary>Parameters fitted to our experimental data and tuned with respect to the context of the intended application</summary>
+                <p class="description"></p>
+                <table>
+                    <tr>
+                        <th>Symbol</th>
+                        <th>Description</th>
+                        <th>Value</th>
+                        <th>Reference</th>
+                    </tr>
+                    <tr>
+                        <td>a<sub>LuxR</sub></td>
+                        <td>Maximum expression of LuxR</td>
+                        <td>1x10<sup>2</sup> nM min<sup>-1</sup></td>
+                        <td><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/parameters">iGEM ETH 2014</a></td>
+                    </tr>
+                    <tr>
+                        <td>a<sub>LuxI</sub></td>
+                        <td>Maximum expression of LuxI</td>
+                        <td>1x10<sup>4</sup> nM min<sup>-1</sup></td>
+                        <td><a href="#bib3" class="forward-ref">[3]</a></td>
+                    </tr>
+                    <tr>
+                        <td>K<sub>Lac</sub></td>
+                        <td>Half-activation [Lac] of the hybrid promoter, P<sub>Lux-Lac</sub></td>
+                        <td>2 mM</td>
+                        <td><a href="http://parts.igem.org/Part:BBa_K1847008">Characterized lactate sensing part on which our AND-gate is based</a></td>
+                    </tr>
+                    <tr>
+                        <td>k<sub>LuxI</sub></td>
+                        <td>Leakiness of the hybrid promoter</td>
+                        <td>0.01</td>
+                        <td><a href="http://parts.igem.org/Part:BBa_K1847008">Characterized lactate sensing part on which our AND-gate is based</a></td>
+                    </tr>
+                    <tr>
+                        <td>K<sub>LuxR</sub></td>
+                        <td>Half-activation [LuxR-AHL] of the hybrid promoter P<sub>Lux-Lac</sub></td>
+                        <td>10 nM</td>
+                        <td><a href="https://2013.igem.org/Team:ETH_Zurich/Parameter">iGEM ETH 2013</a></td>
+                    </tr>
+                    <tr>
+                        <td>n<sub>LuxR</sub></td>
+                        <td>Hill coefficient of the hybrid promoter, P<sub>Lux-Lac</sub> regarding [LuxR-AHL]</td>
+                        <td>1.7</td>
+                        <td><a href="https://2015.igem.org/Team:ETH_Zurich/Modeling/Parameters">iGEM ETH 2015</a></td>
+                    </tr>
+                    <tr>
+                        <td>n<sub>Lac</sub></td>
+                        <td>Hill coefficient of the hybrid promoter, P<sub>Lux-Lac</sub> regarding [Lac]</td>
+                        <td>1.7</td>
+                        <td><a href="https://2015.igem.org/Team:ETH_Zurich/Modeling/Parameters">iGEM ETH 2015</a></td>
+                    </tr>
+                    <tr>
+                        <td>&tau;</td>
+                        <td>Doubling time of <span class="bacterium">E. coli</span> Nissle</td>
+                        <td>80 min</td>
+                        <td>Fitted from our growth experiments</td>
+                    </tr>
+                    <tr>
+                        <td>k<sub>Azu-LuxI</sub></td>
+                        <td>Relative expression of Azurin compared to LuxR</td>
+                        <td>10</td>
+                        <td>estimated</td>
+                    </tr>
+                </table>
+            </details>
-         </section>
+            <details>
-     <section>
+                <summary>Fixed parameters - well known</summary>
+                <p class="description"></p>
+                <table>
+                    <tr>
+                        <th>Symbol</th>
+                        <th>Description</th>
+                        <th>Value</th>
+                        <th>Reference</th>
+                    </tr>
+                    <tr>
+                        <td>a<sub>AHL</sub></td>
+                        <td>AHL synthesis rate by LuxI</td>
+                        <td>0.01 min<sup>-1</sup></td>
+                        <td><a href="#bib1" class="forward-ref">[1]</a></td>
+                    </tr>
+                    <tr>
+                        <td>d<sub>AHL,out</sub></td>
+                        <td>AHL extracellular degradation rate</td>
+                        <td>5x10<sup>-4</sup> min<sup>-1</sup></td>
+                        <td><a href="#bib2" class="forward-ref">[2]</a></td>
+                    </tr>
+                    <tr>
+                        <td>D<sub>AHL</sub></td>
+                        <td>AHL diffusion coefficient in water</td>
+                        <td>3x10<sup>-8</sup> m<sup>2</sup>min<sup>-1</sup></td>
+                        <td><a href="#bib2" class="forward-ref">[2]</a></td>
+                    </tr>
+                    <tr>
+                        <td>K<sub>LuxR-AHL</sub></td>
+                        <td>LuxR-AHL quadrimer binding constant</td>
+                        <td>5x10<sup>-10</sup> nM<sup>-3</sup></td>
+                        <td><a href="#bib3" class="forward-ref">[3]</a></td>
+                    </tr>
+                </table>
+            </details>
+            <details>
+                <summary>Fixed parameters - not very well known but redundant with respect to other parameters</summary>
+                <p class="description"></p>
+                <table>
+                    <tr>
+                        <th>Symbol</th>
+                        <th>Description</th>
+                        <th>Value</th>
+                        <th>Reference</th>
+                    </tr>
+                    <tr>
+                        <td>d<sub>LuxI</sub></td>
+                        <td>LuxI degradation rate</td>
+                        <td>0.0167 min<sup>-1</sup></td>
+                        <td><a href="#bib2" class="forward-ref">[2]</a></td>
+                    </tr>
+                    <tr>
+                        <td>d<sub>LuxR</sub></td>
+                        <td>LuxR degradation rate</td>
+                        <td>0.023 min<sup>-1</sup></td>
+                        <td><a href="#bib2" class="forward-ref">[2]</a></td>
+                    </tr>
+                    <tr>
+                        <td>d<sub>Azu</sub></td>
+                        <td>Azurin degradation rate</td>
+                        <td>0.046 min<sup>-1</sup></td>
+                        <td>estimated</td>
+                    </tr>
+                    <tr>
+                        <td>D<sub>Azu</sub></td>
+                        <td>Diffusion coefficient of Azurin</td>
+                        <td>1x10<sup>-6</sup> cm<sup>2</sup>s<sup>-1</sup></td>
+                        <td>estimated</td>
+                    </tr>
+                    <tr>
+                        <td>[LuxR]<sub>0</sub></td>
+                        <td>Total initial concentration of LuxR</td>
+                        <td>a<sub>LuxR</sub>/d<sub>LuxR</sub></td>
+                        <td>chosen same as steady state value</td>
+                    </tr>
+                </table>
+            </details>
+         </div>
+     </section>
-        <h2>Parameters   <button><a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed#InVivo_Parameters" class="more">Get Values</a></button> </h2>
-        <p class="description">The parameters that were used in the COMSOL model were obtained partly from literature, partly from characterizations of previous iGEM teams and finally the most important ones were estimated by fitting our experimental data and tuning the fitted-results in the context of the intended applciation, as explained in detail by the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">Functional Parameter Search</a>. Check out <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed">details about the model</a> to read more about the different parameter values used.</p>
-        <p>For more details about the model go to the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed">detailed description</a> and <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">Functional Parameter Search</a>.</p>
+    <section class="emphasize">
+        <h1><span style="color:white">RESULTS</span></h1>
      </section>
      <section>
-        <h1>Results</h1>
          <!--<p>The COMSOL model helped us to extend our MATLAB model in the following ways:</p>-->
          <h2>We could simulate for a geometry of the system closer to the real-life tumor conditions <button><a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed#InVivo_Treatment" class="more">Simulating the Treatment</a></button></h2>
@@ Line 199: / Line 350: @@
          </ul>
+    </section>
+    <section id="InVivo_Treatment">
+        <h1>Simulating The Treatment phases</h1>
+        <p>Our model helped us to simulate the three main phases of the CATE treatment: Growth, AND-Gate Switching (Environment sensing) and finally Lysis & Azurin Diffusion. For details see <a href="https://2017.igem.org/Team:ETH_Zurich/Applied_Design</p>">CATE in Action</a>.</p>
+        <div class="multi-summary">
+            <details>
+                <summary>Phase 1: Growth</summary>
+                <p class="description">The growth is modelled using the exponential growth rate <span class="math">\[\frac{\mathrm{d} d_{\text{cell}}}{\mathrm{d} t} = \frac{1}{\tau} d_{\text{cell}} \left ( 1-\frac{d_{\text{cell}}}{d_{\text{cell,ss}}} \right )\]</span>, where &tau; is the doubling time, that we obtained by fitting <span class="bacterium">E. coli</span> Nissle growth curves from our lab experiments. For more details on the growth equation check the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed">detailed description</a>.</p>
+            </details>
+            <details>
+                <summary>Phase 2: AND-Gate Tumor Sensing Switch</summary>
+                <p class="description">The AND gate Switch senses the environment based on Quorum sensing (<em>d</em><sub>cell</sub>) and Lactatae concentration ([Lac]), as explained in detail in the description of the <a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fa_Tumor_Sensor">Tumor Sensing circuit</a>. Based on the different combinations of <em>d</em><sub>cell</sub> and [Lac], as mentioned in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/system_specifications">system specifications</a>, the AND gate switches 'ON' or 'OFF'. For detailed equations see the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed">model description details</a>.</p>
+                <ul>
+                    <li>
+                        ON state:
+                        <p class="description">Rapid and high-fold increase in [LuxI] or [Azu]</p>
+                    </li>
+                    <li>
+                        OFF state:
+                        <p class="description">Slow and negligible-fold increase in LuxI] or [Azu]</p>
+                    </li>
+                </ul>
+            </details>
+            <details>
+                <summary>Phase 3: Lysis and Azurin Diffusion</summary>
+                <p class="description">The effect of lysis is simulated by a temperature controlled trigger of the diffusion of azurin that is produced in the cells. The temperature is increased from 0 to 42&deg;C as a step function and when the temperature reaches 42&deg;C, the production of Azurin and AHL stops, since the cells are lysed, and diffusion of Azurin begins which depletes Azurin out of the cell into the tumor, effectively inducing apoptosis of the tumor cells.</p>
+            </details>
+        </div>
+        <p class="description">The model described above was simulated to test the working of the <a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fa_Tumor_Sensor">Tumor Sensing circuit</a>. The three phases, as described above were simulated and the results for bacteria colonization of tumor are shown here. </p>
+        <figure class="fig-float">
+            <img alt="Growth" src="https://static.igem.org/mediawiki/2017/c/c7/T--ETH_Zurich--Azu0To35h.gif" style="width: 200px;" />
+            <figcaption>Figure 2: Growth phase: 0 hr to 35 hr.</figcaption>
+        </figure>
+        <figure class="fig-float">
+            <img alt="TurnON" src="https://static.igem.org/mediawiki/2017/9/9f/T--ETH_Zurich--Azu35To69h.gif" style="width: 200px;" />
+            <figcaption>Figure 3: AND-Gate Switch Sensing phase: 35 hr to 69 hr; The rapid increase in [Azu shows the switch ON of the AND-gate tumor sensor</figcaption>
+        </figure>
+        <figure class="fig-float">
+            <img alt="Lysis" src="https://static.igem.org/mediawiki/2017/1/1e/T--ETH_Zurich--Azu69To100h.gif" style="width: 200px;" />
+            <figcaption>Figure 4: Cell Lysis and Azurin Diffusion phase: 69 hr to 100 hr</figcaption>
+        </figure>
+        <figure class="fig-nonfloat">
+            <img alt="HighDHighL_norm" src="https://static.igem.org/mediawiki/2017/0/03/T--ETH_Zurich--HighDHighL_norm.png" />
+            <figcaption>Figure 5: Normalized concentration of AHL and Azurin, Cell density (as a ratio of its steady state value), P<sub>Lux-Lac</sub> and Temperature (as a ratio of its steady state value 42&deg;C) probe plots at a point inside the layer of bacterial colonization in the tumor.</figcaption>
+        </figure>
+        <p>In Figure 5, <em>d</em><sub>cell</sub> shows the growth of the cell density inside the layer and <em>P</em><sub>Lux-Lac</sub> shows the main function responsible for the switching functionality based on the environmental conditions of <em>d</em><sub>cell</sub> and lactate.</p>
+        <p>During the growth phase, our sensing circuit is OFF (visible from Figure 1), and is triggered to turn ON once the desired cell density for quorum sensing is reached at around 40 hr, as shown in Figure 5 (and in Figure 2). Then once steady state is reached, the temperature step triggers the cell-lysis and stops the production of Azurin and AHL. Finally as is visible in Figure 3, all the Azurin diffuses out of the layer very rapidly, thus completing the treatment.</p>
+    </section>
+    <section id="InVivo_AND_Switch">
+        <h1>AND-Gate Tumor Sensor Characterization</h1>
+        <p class="description">To test our sensing circuit with the intended application in mind, we use the relative criteria set in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/system_specifications">system specifications</a>. Since our AND-Gate switch has 2 inputs for environment sensing viz. <em>d</em><sub>cell</sub> and [Lac], there are 4 possible binary combinations that CATE can encounter in real-life scenario, viz.:</p>
+        <dl>
+            <dt>Tumor Colonization</dt>
+            <dd>High <em>d</em><sub>cell</sub> AND High [Lac]</dd>
+            <dt>Healthy tissue Colonization</dt>
+            <dd>High <em>d</em><sub>cell</sub> AND Low [Lac]</dd>
+            <dt>Tumor NOT colonized</dt>
+            <dd>Low <em>d</em><sub>cell</sub> AND High [Lac]</dd>
+            <dt>Healthy tissue NOT colonized</dt>
+            <dd>Low <em>d</em><sub>cell</sub> AND Low [Lac]</dd>
+        </dl>
+        <p>The simulation results between 35 hr and 75 hr for all the 4 possible scenarios that CATE can encounter, are shown below.</p>
+        <figure class="fig-float">
+            <img alt="Case1_HighDHighL" src="https://static.igem.org/mediawiki/2017/6/65/T--ETH_Zurich--Case1_HighDHighL.gif" style="width: 200px;" />
+            <figcaption>Figure 6: Case: Tumor colonized - High <em>d</em><sub>cell</sub> AND High [Lac]; AND-gate Switch is turned ON fully.</figcaption>
+        </figure>
+        <figure class="fig-float">
+            <img alt="Case1_HighDLowL" src="https://static.igem.org/mediawiki/2017/9/96/T--ETH_Zurich--Case1_HighDLowL.gif" style="width: 200px;" />
+            <figcaption>Figure 7: Case: Healthy tissue colonized - High <em>d</em><sub>cell</sub> AND Low [Lac]; AND-gate Switch is turned ON partially.</figcaption>
+        </figure>
+        <figure class="fig-float">
+            <img alt="Case1_LowDHighL" src="https://static.igem.org/mediawiki/2017/5/58/T--ETH_Zurich--Case1_LowDHighL.gif" style="width: 200px;" />
+            <figcaption>Figure 8: Case: Tumor NOT colonized - Low <em>d</em><sub>cell</sub> AND High [Lac]; AND-gate Switch is turned OFF fully.</figcaption>
+        </figure>
+        <figure class="fig-nonfloat">
+            <img alt="Case1_LowDLowL" src="https://static.igem.org/mediawiki/2017/d/d6/T--ETH_Zurich--Case1_LowDLowL.gif" style="width: 200px;" />
+            <figcaption>Figure 9: Case: Healthy tissue NOT colonized - Low <em>d</em><sub>cell</sub> AND Low [Lac]; AND-gate Switch is turned OFF fully.</figcaption>
+        </figure>
+        <p>As is clear from the Figures 6-9, the AND-gate switch is fully turned ON for the case of tumor colonization with steady state Azurin concentration reaching 75000 nM (as shown in Figure 10). For the case of colonization of healthy tissue, the AND-gate switch is partially turned ON with steady state Azurin concentration at about 22000 nM. This is about 3.5 times lower than when the switch is fully ON. In the case of tumor and healthy tissue not colonized the steady state Azurin concentrations are around 20 nM and 17 nM, respectively, which represents a fully turned OFF AND-gate switch. <!--This high-fold difference is better visible in the semi-log plot in Figure 10.--></p>
+        <!--<figure class="fig-nonfloat">
+            <img alt="SemiLog_Azu_t" src="https://static.igem.org/mediawiki/2017/2/2a/T--ETH_Zurich--AzuTime_allCases.png" />
+            <figcaption>Figure 10: [Azu] vs Time with log scale on Y-axis; Shows the behavior of our tumor sensing circuit in all the different <em>d</em><sub>cell</sub> and [Lac] conditions.</figcaption>
+        </figure>-->
+        <figure class="fig-nonfloat">
+            <img alt="Azu_t" src="https://static.igem.org/mediawiki/2017/4/43/T--ETH_Zurich--AzuTime_NoLogScale.png" />
+            <figcaption>Figure 10: [Azu] vs Time with linear scale; Shows the behavior of our tumor sensing circuit in the different <em>d</em><sub>cell</sub> and [Lac] conditions.</figcaption>
+        </figure>
+    </section>
+    <section id="InVivo_Killing">
+        <h1>Killing Area</h1>
+        <p class="description">Using the COMSOL data, the maximum Azurin concentration over time was found for every point in the system, and the results are as shown in Figure 15. </p>
+        <figure class="fig-float">
+            <img alt="Azu_max" src="https://static.igem.org/mediawiki/2017/5/56/T--ETH_Zurich--Azu_max_t.png" style="max-width:200px" />
+            <figcaption>Figure 15: Maximum [Azu] (nM) over time at all points.</figcaption>
+        </figure>
+        <figure class="fig-nonfloat">
+            <img alt="Kill_area" src="https://static.igem.org/mediawiki/2017/e/e4/T--ETH_Zurich--KillingArea.png" style="max-width:200px" />
+            <figcaption>Figure 16: Killing Area: Points where maximum [Azu] over time exceeds 20 &micro;M.</figcaption>
+        </figure>
+        <!--TODO: HIGHLIGHT-->
+        <p>From Figure 15, the effective killing area can be estimated by choosing the points in the <em>(r,z)</em> plane that have [Azu]<sub>max|t</sub> > 20 &micro;M. <em></em>Figure 16 shows the maximum effective killing area is at <em>z</em> = 0, between 9.1 and 10.1 mm. This amounts to approximately <span style="color:red">3.5 %</span> of tumor being treated by volume. So we can conclude, that a more potent cytotoxic agent that Azurin is required to achieve significant tumor treatment using CATE.</em></p>
+    </section>
+    <section id="InVivo_Patterns">
+        <h1><span title="BONUS">Bacterial colonization patterns</span></h1>
+        <p class="description">To further push the limits of our system, we tested the environment sensing AND-gate switching functioning in different colonization patterns. Although any possible colonization pattern can be simulated, we simulated the logically closest patterns (compared to a spherical shell layer), that we could think of, and introduced heterogenity through partitions.</p>
+        <dl>
+            <dt>Case 1: Homogeneous distribution in a Single spherical shell layer in tumour</dt>
+            <dd>already simulated as shown in previous results</dd>
+            <dt>Case 2: Heterogeneous distribution (Partitions) in a Single spherical shell layer in tumour</dt>
+            <dd>another possible feasible scenario</dd>
+            <dt>Case 3: Heterogeneous distribution (Partitions) in Double spherical shell layer in tumour</dt>
+            <dd>introducing some more heterogenity in the system</dd>
+            <dt>Case 4: Homogeneous distribution throughout Healthy tissue</dt>
+            <dd>with <em>d</em><sub>cell</sub> = 0.0005, as in this geometry the AND-gate is more easily activated as comparaed to other geometries, since difusion can not occur within the inner region where bacteria colonize, and thus this colonized inner region acts as a huge source, with no diffusion between the cells, as shown in the Figure 15.</dd>
+        </dl>
+        <p>The simulation results between 35 hr and 75 hr for all the 4 possible scenarios, are shown below.</p>
+        <div>
+            <figure class="fig-float">
+                <img alt="Sph_Nopartition" src="https://static.igem.org/mediawiki/2017/6/65/T--ETH_Zurich--Case1_HighDHighL.gif" style="width: 200px;" />
+                <figcaption>Figure 11: Case 1: Tumor colonized in a single spherical shell-shaped layer; AND-gate Switch is turned ON fully.</figcaption>
+            </figure>
+            <figure class="fig-nonfloat">
+                <img alt="Sph_partition" src="https://static.igem.org/mediawiki/2017/e/e6/T--ETH_Zurich--Sph_partition.gif" style="width: 200px;" />
+                <figcaption>Figure 12: Case 2: Tumor is colonized in partitions in the shape of a single shell-shaped layer; AND-gate Switch is turned ON fully.</figcaption>
+            </figure>
+            <figure class="fig-float">
+                <img alt="DoubleSph_partition" src="https://static.igem.org/mediawiki/2017/8/8f/T--ETH_Zurich--DoubleSph_partition.gif" style="width: 200px;" />
+                <figcaption>Figure 13: Case 3: Tumor is colonized in alternative paritions in two spherical shell-shaped layers; AND-gate Switch is turned ON fully.</figcaption>
+            </figure>
+            <figure class="fig-nonfloat">
+                <img alt="Homogen" src="https://static.igem.org/mediawiki/2017/2/25/T--ETH_Zurich--Homogen.gif" style="width: 200px;" />
+                <figcaption>Figure 14: Case 4: Homogeneous colonization (<em>d</em><sub>cell</sub>) of a healthy tissue; AND-gate switch turns ON but only partially.</figcaption>
+            </figure>
+        </div>
+        <p>The above figures show that our Tumor sensing circuit functions properly in the different colonization patterns simulated. According to Figure 14, in case of colonization of a healthy tissue, the switch is only partially triggered (slower rise and lesser final steady state [Azu]), when compared to Case 1 in Figure 12, that has a fully ON switch indicated by the rapid rise and approximately 3.5 times higher-fold increase in [Azu]  </p>
      </section>
@@ Line 204: / Line 507: @@
          <h1>Final Conclusions <button><a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Silico_Final" class="more">VOILA!</a></button></h1>
          <p>Our model finally helped us implement a comprehensive <em>in-silico</em> test to prove that our system already exhibits an excellent performance for the clinical application it has been designed for. This helped us to take into account the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting">fitting of the parameters</a> using our experimental data and integrate our own <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting">hybrid promoter</a> tuned in reference to the intended application context, to verify the functioning of our <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensingenvironment">environment sensing function</a>. </p>
      </section>
      <section>
-         <h1>Limitations</h1>
+         <h1>Limitations of our Model</h1>
          <p>Our model has some limitations. We do not model protein E production and cell lysis caused by it. Instead lysis is just simulated in effect as the end of production of AHL and Azurin and start of diffusion of Azurin. Moreover, a step signal is used as a trigger for the lysis. As mentioned in the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo_Detailed">parameters description</a>, Azurin production is taken to be 10 times proportional to LuxI production. Also, killing mechanism of Azurin has not been modelled since that was not necessary to demonstrate the working of our project CATE in the scope of iGEM. <!--Moreover, Azurin is not potent enough to kill the tumor but CATE allows it to be replaced with any powerful cytotoxic agent.--></p>
      </section>
@@ Line 225: / Line 528: @@
              </li>
          </ul>
+    </section>
+    <section class="references">
+        <h1>References</h1>
+        <ol>
+            <li id="bib1">Jordi Garcia-Ojalvo, Michael B. Elowitz, and Steven H. Strogatz Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing <source>PNAS</source> 2004 101 (30) 10955-10960 | <a href="http://www.pnas.org/content/101/30/10955.full">doi:10.1073/pnas.0307095101</a></li>
+            <li id="bib2">A.B. Goryachev, D.J. Toh T.Lee, Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants <source>Biosystems</source>, Volume 83, Issues 2–3, February–March 2006, Pages 178-187 | <a href="http://www.sciencedirect.com/science/article/pii/S0303264705001267">doi:10.1016/j.biosystems.2005.04.006</a></li>
+            <li id="bib3">A synthetic multicellular system for programmed pattern formation Subhayu Basu, Yoram Gerchman, Cynthia H. Collins, Frances H. Arnold & Ron Weiss<source>Nature</source> 434, 1130-1134 (28 April 2005) | <a href="http://www.nature.com/nature/journal/v434/n7037/full/nature03461.html">doi:10.1038/nature03461</a></li>
+        </ol>
      </section>

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