Compartment Model

Introduction and Goals

Figure 1. Genetic design

The combined compartment model studies the connections between the lactate module and the AHL module. The lactate sensor's output is LuxR. LuxR is the input of the AHL module. The system seems sequential but the actual input of the AHL module is the signaling molecule AHL. LuxR only induces leaky expression of the signaling molecule. However to activate the AHL module, a significant density of E. coli has to be reached. That is why the system should work as an AND gate.

AND gate

In our system we want to reduce the amount the amount of false positives . That’s why cells displaying intermediary characteristics should not be detected by our system. We consider that cells showing increased lactate production rate but do not expose phosphatidylserine, or cells exposing phosphatidylserine but not an increased lactate production rate should not be recognized by our system. We implemented the system to obtain an AND gate. The system works as two sequential filtering step. The sequential design was used in order to limit the self-activation of the quorum sensing module . Indeed as we have seen in the AHL module, the difference between the two modules strongly depends on the amount of LuxR in the E. coli . This design has a disadvantage though, it requires fine-tuning in order to avoid that one signal prevails on the second one. In the scheme displayed below, we describe in which situation, the E. coli should display fluorescence.

One particularity of our system is that even healthy cells will produce lactate. That is why we implemented a lactate module that works as a fold-change sensor. The fold change sensor will produce a pulse of LuxR. We will study here how the pulsed response influence the output of the system.

Figure 2. Description of the AND gate

Combined Compartment Model

Overview

With this model we plan to check whether our system can work as an AND gate. We will compare the output from the simple lactate detection system with the one from the system including the fold-change sensor.

The equations are the integration of both modules in one compartment model.

Assumptions

We assume:

1. Instant diffusion in the compartments.
2. In all the following we assume the nanowell volume to be 1 nL.
3. All the E. coli receive the same amount of lactate. This is the worst-case scenario since probably the E. coli on the doughnut will sense a higher lactate production rate than the ones not bound, i.e., in the bulk.

Equations

The equations are the combination of the compartment model of the AHL module and the lactate module.

The internal states: Lactate, LuxR, LuxRAHL, LuxI have the same ODEs than the single cell model. As already done in the compartment model, we just add convection between the different compartments.

Simulation when the lactate concentration reach the same steady states

Below you will find one example of a comparison between the response of a system including the fold change sensor and the one harboring only the natural detection system for the four different cases described earlier. The lactate inputs give two different delayed pulses of LuxR. The different pulses of LuxR lead to different delay of self-activation of the GFP output. The first GFP output to activate is the one with the cancer cell, the second and third one represent cells with intermediary characteristics. The latest activation time is for the one with no colocalization and no lactate production. With the fold-change sensor, we obtain a significant time difference between the different inputs. With only the natural detection system we do not obtain a clear difference between the different cases. The lactate signal prevails on the quorum sensing signal.

Figure 3. Simulation of the full system with the fold change sensor

Figure 4. Simulation of the full system with the natural lactate detection system

Simulation when the lactate concentration reach different steady states

Here we will compare again the response of the system when including the fold change sensor and the natural lactate detection system.

We can see that here again the fold change gives a better response for the same parameters.

Figure 5. Simulation of the full system with the fold change sensor

Figure 6. Simulation of the full system with the natural lactate detection system

Advantages of the fold change

The fold-change sensor solved the problem of leakiness and the problems about the size of the nanowell plate we had earlier. The pulse of LuxR should be large enough in order to activate the quorum sensing module. A broader peak can be obtained by introducing a delay in the response of LacI. This can be easily implemented biologically (if needed) by introducing an intermediary protein between lactate induction and LacI production.

Simulation of GFP measurement

As we have seen earlier, lactate production signals still prevail on the quorum sensing signal. In the broader context of our system, the experimenter will take the measurement at a specific time point. The heatmap represents the ratio of GFP output with and without colocalization on the doughnut for different rates of lactate production and different number of E. coli colocalized on the doughnut. In both cases, the E. coli receive the same amount of lactate.

We can see that depending on the rate of lactate production, there is an optimum for the fold change ratio between the GFP output. The GFP output depends a lot on the timescales because of the fold-change sensor.

Figure 7. Simulation of the full system with the fold change sensor

Single cell model

Overview

The single cell model is provided here to simulate the combined model.

Chemical species

Reactions

\begin{align*} &\mathop{\xrightarrow{\hspace{4em}}}_{a_{LacI},K_{A,appLact}}^{\displaystyle\mathop{\downarrow}^{\text{Lact}}} \text{LacI}\\ \text{IPTG} + \text{LacI} &\mathop{\mathop{\xrightarrow{\hspace{4em}}}^{\xleftarrow{\hspace{4em}}}}_{k_{\mathrm{IL}}}^{k_{\mathrm{-IL}}} \text{IL}\\ &\mathop{\xrightarrow{\hspace{4em}}}_{a_{LuxR},K_{A,appLact}}^{\displaystyle\mathop{\downarrow}^{\text{Lact}}} \text{LuxR}\\ &\mathop{\xrightarrow{\hspace{4em}}}_{a_{LuxR},K_{R,LacI}}^{\displaystyle\mathop{\bot}^{\text{LacI}}} \text{LuxR}\\ \text{AHL} + \text{LuxR} &\mathop{\mathop{\xrightarrow{\hspace{4em}}}^{\xleftarrow{\hspace{4em}}}}_{k_{\mathrm{LuxRAHL}}}^{k_{\mathrm{-LuxRAHL}}} \text{LuxRAHL}\\ &\mathop{\xrightarrow{\hspace{4em}}}_{a_\mathrm{LuxI},K_{\mathrm{a,LuxRAHL}}}^{\displaystyle\mathop{\downarrow}^{\text{LuxRAHL}}} \text{LuxI}\\ &\mathop{\xrightarrow{\hspace{4em}}}_{a_\mathrm{GFP},K_{\mathrm{a,LuxRAHL}}}^{\displaystyle\mathop{\downarrow}^{\text{LuxRAHL}}} \text{GFP}\\ \end{align*}

\begin{align*} \text{LuxI}&\mathop{\xrightarrow{\hspace{4em}}}^{a_{\mathrm{AHL}}}\text{AHL}+\text{LuxI}\\ \text{LuxR}&\mathop{\xrightarrow{\hspace{4em}}}^{d_{\mathrm{LuxR}}}\varnothing\\ \text{AHL}&\mathop{\xrightarrow{\hspace{4em}}}^{d_{\mathrm{AHL}}}\varnothing\\ \text{LuxRAHL}&\mathop{\xrightarrow{\hspace{4em}}}^{d_{\mathrm{LuxRAHL}}}\varnothing\\ \text{LuxI}&\mathop{\xrightarrow{\hspace{4em}}}^{d_{\mathrm{LuxI}}}\varnothing\\ \end{align*}

Equations including the fold change sensor

Combining all of the equations from the two different modules, it yields the following system:

\begin{align*} \frac{d[LacI]}{dt}&=\frac{a_\mathrm{LacI} \cdot (\frac{[Lact]}{K_\mathrm{A,appLact}})^{n_1}}{1+(\frac{[Lact]}{K_\mathrm{A,appLact}})^{n_1}}-d_{\mathrm{LacI}}[LacI]\\ \frac{d[LuxR]}{dt}&=\frac{a_\mathrm{LuxR} \cdot (\frac{[Lact]}{K_\mathrm{A,appLact}})^{n_1}}{1+(\frac{[Lact]}{K_\mathrm{A,appLact}})^{n_1}} \cdot \frac{1}{1+(\frac{[LacI]}{K_{\mathrm{R,LacI}}\cdot (\gamma_2+1)})^{n_\mathrm{2}}}-d_{\mathrm{LuxR}}[LuxR]\\ [LuxRAHL]&= \frac{[AHL]\cdot [LuxR]}{K_{\mathrm{d,LuxRAHL}}+[AHL]}\\ \frac{d[LuxI]}{dt}&=a_{\mathrm{LuxI}}k_{\mathrm{leaky}}([LuxR]-[LuxRAHL])+\frac{a_{\mathrm{LuxI}}(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}{1+(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}-d_{\mathrm{LuxI}}[LuxI]\\ \frac{d[AHL]}{dt}&=a_{\mathrm{AHL}}[LuxI]-d_{\mathrm{AHL}}[AHL]\\ \frac{d[GFP]}{dt}&=a_\mathrm{GFP}k_{\mathrm{leaky}}([LuxR]-[LuxRAHL])+\frac{a_\mathrm{GFP}(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}{1+(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}-d_{\mathrm{GFP}}[GFP]\\ K_\mathrm{d,LuxRAHL} &= \frac{k_\mathrm{-LuxRAHL}}{k_\mathrm{LuxRAHL}}\\ \gamma_2 &= \frac{IPTG_{tot}}{K_{IL}} \end{align*}

Equations including the natural detection system

\begin{align*} \frac{d[LuxR]}{dt}&=\frac{a_\mathrm{LuxR} \cdot (\frac{[Lact]}{K_\mathrm{A,appLact}})^{n_1}}{1+(\frac{[Lact]}{K_\mathrm{A,appLact}})^{n_1}}-d_{\mathrm{LuxR}}[LuxR]\\ [LuxRAHL]&= \frac{[AHL]\cdot [LuxR]}{K_{\mathrm{d,LuxRAHL}}+[AHL]}\\ \frac{d[LuxI]}{dt}&=a_{\mathrm{LuxI}}k_{\mathrm{leaky}}([LuxR]-[LuxRAHL])+\frac{a_{\mathrm{LuxI}}(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}{1+(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}-d_{\mathrm{LuxI}}[LuxI]\\ \frac{d[AHL]}{dt}&=a_{\mathrm{AHL}}[LuxI]-d_{\mathrm{AHL}}[AHL]\\ \frac{d[GFP]}{dt}&=a_\mathrm{GFP}k_{\mathrm{leaky}}([LuxR]-[LuxRAHL])+\frac{a_\mathrm{GFP}(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}{1+(\frac{[LuxRAHL]}{K_{\mathrm{A,LuxRAHL}}})^2}-d_{\mathrm{GFP}}[GFP]\\ K_\mathrm{d,LuxRAHL} &= \frac{k_\mathrm{-LuxRAHL}}{k_\mathrm{LuxRAHL}}\\ \gamma_2 &= \frac{IPTG_{tot}}{K_{IL}} \end{align*}