We identified two modules of our Mantis system suitable to mathematical modeling.

Phase 1: Initial system modeling

First, we aimed to find out which protein-protein interactions in the Cpx pathway were most suited to connect to a BiFC reporter gene. Three candidate systems were analyzed, which were based on either CpxR-CpxR dimerization (Figure 1A), CpxA-CpxR phosphotransfer l(Figure 1B) or CpxA-CpxR phosphotransfer combined with specific TEV-cleavage (Figure 1C). While the constructs for the wet-lab were created, initial in silico tests were performed. We decided that protein concentrations and signal activation through antigens were the most important variables to test. Using our models, we simulated the effect of CpxR protein levels and antigen levels on production of a fast and intense signal. These effects could also be later tested in the wet-lab.

Figure 1: A) eYFPn and eYFPc are fused (seperately) to CpxR. This way BiFC is used to visualize the CpxR dimerization step. B) eYFPc is fused to CpxA, and eYFPn is fused to CpxR. This way, BiFC is used to visualized the phosphorylation step of the Cpx pathway. C) TEV protease is fused to CpxR, and eYFPn and eYFPc are fused to CpxA (seperately). Upon Cpx pathway activation, the eYFP-termini are cleaved off of CpxA. Leucine zippers are fused to the eYFP-termini to enable the reassembly.

We quickly found out that, in theory, a system based on CpxR-CpxR dimerization (Figure 1A) was the most promising to construct in the lab. This is likely because one activated CpxA can amplify its signal by phosphorylating several response regulator CpxR's

Figure 2: Relative fluorescent signal intensity (orange) and signaling speed (green) are plotted against the initial concentration of CpxR.

Results of all three setups can be seen at the modeling page. Although the CpxA-CpxR setups (Figure 1B,C) is dependent on the right CpxA and antigen levels in the sample, it seems that the CpxR-CpxR setup is not dependent on any protein concentration. It even shows that the maximum reached YFP concentration is limited by CpxR, which can be increased in the lab. The strongest signal will be obtained when CpxR expression and Cpx activation are maximized (Figure 2).

We went into the lab to test these hypotheses!

Phase 2: Testing model propositions in the lab

To test these hypotheses, we created constructs in which we coupled split eYFP halves to CpxA and CpxR respectively and placed them under control of the inducible pBAD/araC promoter. We transformed E. coli K12 with these constructs. The Cpx system was activated with the known activator KCl in different concentrations to mimic different antigen concentrations at a time-point of 20 minutes. An extensive overview of the performed experiments can be found here.

We quickly found out that the systems based on CpxA-CpxR interaction did not generate a clear fluorescent signal, which matches the prediction of the model! Furthermore, we show that visualizing CpxR dimerization with BiFC is indeed a viable option. Because we put the CpxR-eYFP-termini construct under control of the inducible araC/pBAD promoter we were able to test hypothesis 1: The strongest signal will be obtained when CpxR expression is maximized. We found out that this is indeed true (Figure 3ABC). You can check this result here.

Figure 3A: CpxR dimerization visualized with different L-arabinose concentrations over time.

Figure 3B: CpxA-CpxR protein interaction visualized with different L-arabinose concentrations over time.

Figure 3C: CpxA-CpxR protein interaction visualized with different L-arabinose concentrations over time. Upon Cpx activation, eYFP-termini are cleaved off of CpxA by TEV protease and reassemble in the cytoplams using leucine zipper's natural affinity for each other.

We then set to test hypothesis 2: The strongest signal will be obtained when Cpx activation is maximized. In Figure 3 we show that this is also true. We mimic antigen binding by adding a known activator of the Cpx pathway, and by increasing its concentration the fluorescence intensity also rises.

We take this data back to the lab to further improve our computer model!

Figure 4: CpxR dimerization visualized with L-arabinose concentration of 0.2% and different activator concentrations over time.

Phase 3: Fitting the computer model to the lab data

To learn about the characteristics and potential of the system built in the lab, the experimental data was compared to the model. A parameter set was found which gave a similar YFP production to the experimental data (Figure 5). By doing this, we found that the best fitting set showed a relatively high fluorescence intensity, but it was fairly slow compared to other parameter sets. This means that there is still room for improvement!

Figure 5: Comparison of YFP concentration over time using data measured in the wet-lab and a model simulation using the best fitting parameter set.

Of course, to improve the current signaling system we want to know which parameters should be changed in order to increase the YFP signal and the response speed. To assess this, each parameter of the best fitting set was individually varied between 0.001 and 1000, keeping the other parameters constant. The effect of this change on the maximum achievable YFP concentration and production speed was calculated (Figure 6).

Figure 5: *Placeholder HEATMAP* A) Effect of parameter variation on the maximum fluorescent concentration. B) Effect of parameter variation on the speed at which the fluorescent signal is produced.

This figure shows that the maximum YFP fluorescence has more possibility to be improved than the speed at which YFP increases. Both system properties could be profoundly improved by increasing the kinetic rates for antigen binding by the affibody and CpxR-eYFPc phosphorylation (check out all the used parameters here). Given the iGEM time limits, we could not manage to confirm this hypothesis in the lab. One viable option, though, to improve system performance is to use a fluorophore with a faster maturation time (k6).

Phase 4: Implementing faster maturation

We aim to improve the response time of our visualization system. As stated, our model shows that this can be done by using a faster maturing fluorescent protein. During our "Fluorescent Protein" project we tested a number of fluorescent proteins, of which mVenus showed the fastest maturation time. Furthermore mVenus is designed to have a fast and efficient maturation time [1], exactly what we need! We show here how this experiment was designed.

Figure 6: CpxR dimerization visualized using eYFP (left) and mVenus (right), with L-arabinose concentration = 0.2% and different activator concentrations over time

The results show that usage of mVenus over eYFP as a reporter protein increases the produced fluorescent signal some five times! Unfortunately, the background signal also increases a lot, which means we lose a lot of specificity. We hypothesize that the maturation rate of mVenus is too high, which means that many non-specific interaction become irreversible, leading to high fluorescent signals, even when no activator is present. This means that mVenus is not a suitable candidate to visualize antigen binding. Through literature research we confirmed this hypothesis [2].

During this project, more reporter proteins were tested. Due to time constraints, we were unable to model these systems. At this moment, we recommend using sfGFP as a reporter for antigen binding. We found out sfGFP is thermostable, it has efficient maturation at high temperatures, while still being one of the fastest and brightest reporters we tested.

We wanted to incorporate quorum sensing (QS) to make Mantis more robust and increase sensitivity to be able to sense very low antigen concentrations. By using QS, we aim to convert the antigen concentration into a singaling molecule concentration. Every biosensor cell in our Mantis system will then be able to detect the signaling molecule. To do this, we need a quorum sensing system maybe link to background info on LuxR, LuxI, AHL, [LuxR-AHL], dimer, etc that can self-activate, but only does so upon induction. As a starting point we took the BBa_K1913005 biobrick. This contains the LuxI and LuxR proteins from Aliivibrio fischeri, each with their native promoter. It also contains the GFP protein under control of a promoter identical to the promoter of LuxI. LuxI can auto-activate itself while GFP can report on the amount of auto-activation.

Previous experiments have shown that BBa_K1913005 produces fluorescence even at low cell densities. This suggests that leaky expression of LuxI and high basal levels of LuxR will result in a significant amount of GFP production. The goal is to construct a variation on this part that no longer has spontaneous activation, but can be induced by an outside mechanism, for example a two-component system.

To confirm the properties of BBa_K1913005 reported in the repository, we first analyzed the fluorescence produced by cells containing this biobrick. As a negative control, cells containing pLuxR-GFP, as well as cells containing pLuxR-GFP and pLuxL-LuxR (i.e. BBa_K1913005 without LuxI) were also analyized (Figure 7).

Figure 7: The fluorescence produced by cells containing BBa_K1913005 (Reporter Quorum Sensing), pLuxR-GFP, and pLuxR-GFP & pLuxL-LuxR (Negative control 2).

This indicates that cells containing BBa_K1913005 auto-activate and produce the active transcription factor [LuxR-AHL}₂. Without LuxI, there is no AHL production and there is no active form of the LuxR transcription factor. Our goal now is to make an inducible version of BBa_K1913005, one that produces no GFP initially, but can be activated to produce fluorescence.

In paralel to the lab experiments, we developed a model of our QS system. The global parameter space was explored showing the important role of certain parameters in preventing spontaneous auto-activation.

The model showed that in simulations, a low rate for the cellular AHL degradation is associated with spontaneous auto-activation. The role of using aiiA to combat leaky expression has been explored by previous iGEM teams; ETH

The next step was to add aiiA to the spatial model of the entire system. This includes both the QS system, as well as the lytic mechanism responsible for producing a fluorescent signal. In the spatial model, parameter sets that result in spontaneous auto-activation of the system were simulated after including a production term of aiiA. Now, cells induced by antigen detection stop producing aiiA. In the model, aiiA is able to suppress auto-activation. Then, when a signal is detected and aiiA production is halted, levels of aiiA will drop until that specific cell starts auto-activation spontaneously, similar to how all cells behaved before addition of aiiA. Auto-activation of a small number of cells would eventually lead to auto-activation of all cells in the system. After adding aiiA to our model, we simulated a model where cells are able to communicate the presence of antigen, resulting in a population-wide signal. But in the absence of antigen, cells remain inactive and no fluorescence is generated.

We were able to tune the kinetic parameters of aiiA so that aiiA would prevent spontaneous auto-activation caused by leaky expression while not preventing the detection of low levels of AHL produced by neighboring cells.

These modeling results support the strategy of using aiiA to tune the sensitivity of the QS construct and indicate that in principle the functionality we want to engineer can be obtained using only the components we are using.

Inspired by the modeling result, we decided to add aiiA to BBa_K1913005. The aiiA gene had to be under an inducible and tight promoter. We used pTet promoter to regulate aiiA, causing aiiA to be produced constitutively. To turn on fluorescence, aiiA production can be halted by introducing the TetR protein, which represses production from the pTet promoter. Production of TetR is regulated by the inducable promoter pBAD. Addition of arabinose should lead to production of TetR. TetR can then bind and repress pTed, halting aiiA production. Lower levels of aiiA in the cells would mean higher levels of AHL, which should then lead to auto-activation and production of GFP. In this way, addition of arabinose should induce a fluorescent signal.

Therefore, we made a new biobrick BBa_K2387070 that allows for repression of aiiA. We combined our repressible aiiA biobrick with the QS reporter BBa_K1913005. Next, the fluorescence produced by cells containing both biobricks, with and without induction using arabinose, was analyzed (Figure 8).

Figure 8: The fluorescence produced by cells containing the Reporter QS (BBa_K2387070), Inducible QS (Reporter QS with repressible aiiA BBa_K1913005. Arabinose was added to the 'Induced' cells how much?. Negative control 1 encodes only for GFP, negative control 2 for both GFP and LuxR.

For Mantis to produce fluoresence after triggering of QS, we need a system that can produce fluoresence, but only in response to detection of antigen. Our goal was to engineer a construct where QS could be induced, for example by a two-component system or by addition of arabinose. We started off with a QS system that always produces fluoresence. The goal was to reduce the sensitivity to AHL. Through the mathematical model of the QS module of Mantis we discovered that aiiA can be used to tune the sensitivity of the QS system. Inspired by the model, a biobrick that allows for inducable repression of aiiA production was engineered. Combining our biobrick with an existing QS reporter biobrick, we managed to only produce fluoresence after addition of arabinose. Our module was therefore able to detect arabinose and turn on QS. In the cells engaging in QS, GFP was produced.

Our biobrick allows the engineering of whole-cell biosensors that encorporate QS as a means of signal amplification or to synchronize the reporter system across an entire population. For this biobrick to work with a detection module, that module has to trigger TetR production upon detection. In doing so, QS will be triggered, resulting in a fluorescent signal.