Difference between revisions of "Team:ETH Zurich/Detector Module"
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<div> | <div> | ||
<h2>RESULTS</h2> | <h2>RESULTS</h2> | ||
| − | <h3>STOCHASTIC | + | <h3>STOCHASTIC PARAMETER ESTIMATION</h3> |
<p>We estimated the parameters for the reporter genes and the tet promoter stochastically using flow cytometry measurements. The simulated distribution was fitted to the measurements by <i>Approximate Bayesian computation (ABC)</i> usign the INSIGHT tool.</p> | <p>We estimated the parameters for the reporter genes and the tet promoter stochastically using flow cytometry measurements. The simulated distribution was fitted to the measurements by <i>Approximate Bayesian computation (ABC)</i> usign the INSIGHT tool.</p> | ||
<p>The figure below shows the distributions of the estimated parameters. The parameters page reports the <i>maximum a posteriori (MAP)</i> estimates, which are used in the simulation and analysis of our system.</p> | <p>The figure below shows the distributions of the estimated parameters. The parameters page reports the <i>maximum a posteriori (MAP)</i> estimates, which are used in the simulation and analysis of our system.</p> | ||
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<img src="https://static.igem.org/mediawiki/2016/6/63/T--ETH_Zurich--ptet_sfgfp_distribution.png"> | <img src="https://static.igem.org/mediawiki/2016/6/63/T--ETH_Zurich--ptet_sfgfp_distribution.png"> | ||
</a> | </a> | ||
| − | <p><b>Figure 3:</b> Distributions of the parameters stochastically estimated from the experimental data. The model is | + | <p><b>Figure 3:</b> Distributions of the parameters stochastically estimated from the experimental data. The model is identifiable except for $K_m$, this is probably due to the low induction levels (see next section).</p> |
</div> | </div> | ||
<h3>EXPERIMENTAL DESIGN IMPROVEMENT</h3> | <h3>EXPERIMENTAL DESIGN IMPROVEMENT</h3> | ||
| − | <p>Parameter estimation for the tet promoter revealed that the affinity $K_m$ of the tet promoter is in the order of | + | <p>Parameter estimation for the tet promoter revealed that the affinity $K_m$ of the tet promoter is in the order of 8000 nM. This is almost double the maximum aTc concentration we were using for induction (2000 ng/mL = 4320.6 nM) in the experiments, meaning we were not using the full range of the promoter.</p> |
| − | <p>Since cells die at higher aTc concentrations, we need to reduce the concentration of the TetR repressor in the cells. We suggested to the experimentalists to use a low (~5) copy plasmid for TetR expression instead of the medium-low (~15-20) copy plasmid originally used. We expect whis change to lower the affinity to the range | + | <p>Since cells die at higher aTc concentrations, we need to reduce the concentration of the TetR repressor in the cells. We suggested to the experimentalists to use a low (~5) copy plasmid for TetR expression instead of the medium-low (~15-20) copy plasmid originally used. We expect whis change to lower the affinity to the range 2000 nM-3000 nM. This change improved the quality of the switch characterization.</p> |
</div> | </div> | ||
</div> | </div> | ||
Revision as of 21:11, 18 October 2016
REPORTER MODULE
OVERVIEW
Figure 1: Schematic of the reporter of our circuit.
After the learning phase in which the switch is turned on if nitric oxide and AHL (or lactate) are detected at the same time, we have to identify the state of the switch and which one of the markers has been detected. The reporter is the component of the circuit that enables such a readout in the lab. The state of the switch is displayed by two different fluorescent proteins: sfGFP is expressed by the promoter that has been switched, while the promoter that didn't switch expresses mNectarine.
To allow multiplexing, the reporter proteins are expressed only if they are induced by the same candidate marker that triggered the switch earlier during the learning phase.
GOALSGOALS
- Assist the design of the reporter.
- Characterize the reporter proteins.
MODEL
Our reporter system consists of two fluorescent proteins that report the state of the switch. In the non-switched state (OFF state), the plasmid expresses mNectarine, while after activation, the switched plasmid expresses GFP (ON state).
Figure 2: Biological implementation of the integrase reporter. The figure shows both the switched and non-switched state. Expression of the reporter proteins is repressed by default and induced in presence of the candidate marker.
The following section describes the species and reactions of the ODE model:
REACTIONS
\begin{align*} 1) && P_{mNect} & \rightarrow P_{mNect} + mRNA_{mNect} \\ 2) && P_{sfGFP} & \rightarrow P_{sfGFP} + mRNA_{sfGFP} \\ 3) && mRNA_{mNect} & \rightarrow mRNA_{mNect} + mNect \\ 4) && mRNA_{sfGFP} & \rightarrow mRNA_{sfGFP} + sfGFP \\ 5) && mRNA_{mNect} & \rightarrow \\ 6) && mRNA_{sfGFP} & \rightarrow \\ 7) && mNect & \rightarrow \\ 8) && sfGFP & \rightarrow \\ \end{align*}
SPECIES
| Name | Description |
|---|---|
| $P_{mNect}$ | Non switched promoter, facing the mNectarine gene. |
| $P_{sfGFP}$ | Switched promoter, facing the sfGFP gene. |
| $mRNA_{mNect}$ | mRNA of the mNectarine protein. |
| $mRNA_{sfGFP}$ | mRNA of the sfGFP protein. |
| $mNect$ | mNectarine fluorescent protein. |
| $sfGFP$ | Superfolder GFP protein. |
STOCHASTIC REACTION RATES:
\begin{align*} 1) \quad & k_{mRNAmnect} \cdot P_{mNect} \cdot P_{activity} \\ 2) \quad & k_{mRNAsfgfp} \cdot P_{sfGFP} \cdot P_{activity} \\ 3) \quad & k_{mNect} \cdot mRNA_{mNect} \\ 4) \quad & k_{sfGFP} \cdot mRNA_{sfGFP} \\ 5) \quad & d_{mRNAmnect} \cdot mRNA_{mNect} \\ 6) \quad & d_{mRNAsfgfp} \cdot mRNA_{sfGFP} \\ 7) \quad & d_{mNect} \cdot mNect \\ 8) \quad & d_{sfGFP} \cdot sfGFP \\ \end{align*}
PARAMETERS
| Name | Description |
|---|---|
| $P_{activity}$ | Fraction of the maximal activity of the promoter. This value is computed in the sensor module. |
| $k_{mRNAmnect}$ | mNectarine mRNA transcription rate. |
| $k_{mRNAsfgfp}$ | sfGFP mRNA transcription rate. |
| $k_{mNect}$ | mNectarine translation rate. |
| $k_{sfGFP}$ | sfGFP translation rate. |
| $d_{mRNAmnect}$ | mNectarine mRNA degradation rate. |
| $d_{mRNAsfgfp}$ | sfGFP mRNA degradation rate. |
| $d_{mNect}$ | mNectarine degradation rate. |
| $d_{sfGFP}$ | sfGFP degradation rate. |
CHARACTERIZATION MODEL
The reporter has been characterized by placing the fluorescent proteins under an aTc-inducible promoter. In this case the activity of the promoter is modeled as:
\begin{align*} P_{activity}=l_{pTet}+(1-l_{pTet})\cdot\frac{[aTc]^{n}}{K_m^n+[aTc]^{n}} \end{align*}
Where aTc is the tetracycline variant used for induction, $l_{pTet}$ is the leakiness of the promoter, $n$ the sensitivity to aTc and $K_m$ the affinity.
RESULTS
STOCHASTIC PARAMETER ESTIMATION
We estimated the parameters for the reporter genes and the tet promoter stochastically using flow cytometry measurements. The simulated distribution was fitted to the measurements by Approximate Bayesian computation (ABC) usign the INSIGHT tool.
The figure below shows the distributions of the estimated parameters. The parameters page reports the maximum a posteriori (MAP) estimates, which are used in the simulation and analysis of our system.
Figure 3: Distributions of the parameters stochastically estimated from the experimental data. The model is identifiable except for $K_m$, this is probably due to the low induction levels (see next section).
EXPERIMENTAL DESIGN IMPROVEMENT
Parameter estimation for the tet promoter revealed that the affinity $K_m$ of the tet promoter is in the order of 8000 nM. This is almost double the maximum aTc concentration we were using for induction (2000 ng/mL = 4320.6 nM) in the experiments, meaning we were not using the full range of the promoter.
Since cells die at higher aTc concentrations, we need to reduce the concentration of the TetR repressor in the cells. We suggested to the experimentalists to use a low (~5) copy plasmid for TetR expression instead of the medium-low (~15-20) copy plasmid originally used. We expect whis change to lower the affinity to the range 2000 nM-3000 nM. This change improved the quality of the switch characterization.
Thanks to the sponsors that supported our project:
