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This model was constructed in order to test assumptions used in the analytical solution, specifically the assumptions that the diffusion “front” would not reach the edges of the plate within the relevant timescale. To do this, we used a time-dependent study with the 2D axisymmetric geometry pictured below, where the disc on the top surface is the source electrode.

Results and Conclusions
As can be seen from the figure below, after 20 minutes, the concentration front was nowhere near the bottom of the plate, let alone the sides of the plate. Note that the edge of the domain shown is not the edge of the plate; the domain shown has a radius of 1cm, and the true edge of the plate would be much further from the electrode than this. The model corroborates the analytical solution, showing a very steep concentration gradient at the edge of the electrode, again making a good argument for the achievement of spatial resolution in a patterning application such as ours. In addition, the uniform density of flux lines emanating from the electrode, and the manner in which all of them, even the central ones, move outwards radially, shows efficient use of the electrode area for distribution of the molecule.

From this model, we can conclude that the assumptions in the analytical solution regarding the diffusion “front” are valid, and that it is valid to treat the dish in the analytical solution as both arbitrarily deep and wide. Additionally, it is reasonable to think that localised oxidised or reduced volumes can be induced in the agar plate in a period of 20 minutes by the use of electrodes, which was confirmed experimentally.

When a manufacturer produces a PCB with pads on the underside, such as the one we ordered, the pads are plated, and then the underside of the PCB lacquered. This means that the actual surface of the pad is slightly recessed from the surface of the rest of the PCB. Therefore, we wanted to test how a small recession such as this would affect the concentration profile around the electrode. We again used 2D axisymmetric geometry, shown below, where the electrode disc is the top surface of the recessed cylinder.

Results and Conclusions
The simulation shows that even very small recessions drastically reduce the diffusive flux away from the surface. This is because when the surface is recessed, even by a few um, all flux of the species must be perpendicular to the surface. This constraint drastically limits the space into which the molecules can diffuse, as there is a concentration gradient only in the z-direction, as opposed to in both the r- and z-directions in the flush case. This in turn limits the diffusive flux into the surrounding environment, and results in an increased concentration at the electrode surface due to limited diffusion out of the recession.

The flux lines emanating from the electrode in this model show how the recession, and hence the constriction of flux, adversely affect the efficiency of molecular transport. Comparing the flux line plots to those of the non-recessed model, it can be seen how they are significantly less uniform across the electrode, and cluster around its edge, as this is where the majority of the molecular flux occurs. This can also be seen in the lack of divergence of the central flux lines; molecules diffusing from the centre of the electrode can only diffuse downwards.

Figure 11. Flux lines showing diffusion paths from a recessed electrode

It should be noted that the decreased diffusive flux into the wider environment is not necessarily a bad thing, as although it limits the volume a particular electrode can affect, it increases the sharpness of the concentration profile (and hence gives a sharper on/off transition for induced cells at the boundary), and increases the concentration directly underneath the electrode, resulting in greater fold induction of any electro-inducible organisms in this region. Indeed, the magnitude of recession of electrodes within their mounting could be approached as an engineering specification, with a smaller recession resulting in wider but less well-defined fields of influence, and a larger recession resulting in smaller, sharper and more highly induced fields of influence.

Figure 12. Animation showing the concentration profiles across both the basic and recessed geometries, specifically the significant difference between the basic geometry and 1μm recession

Using parameters from literature, we constructed a model consisting of 4 coupled ODEs: one for transcription of soxR, one for translation of soxR, one for transcription of GFP, and one for translation of GFP. SoxR production was modelled as constitutive, as there was insufficient evidence in literature to support feedback control of the transcription of soxR. The binding of soxR to its promoter was modelled using the phenomenological transfer function given below . This transfer function has the effect of combining both the repressive and activatory modes of Hill functions into one, to account for the binding of both oxidised (activatory) and reduced (repressive) soxR molecules. There was no clear data to be found regarding the cooperativity of the soxR transcription factor; however, it is known that soxR forms dimers, so the maximum cooperativity is constrained at 2. Therefore, we decided to estimate the cooperativity as that of the dimeric tetR transcriptional regulator. TABLE Parameter Value Source

Formula 2. Transfer function describing soxR promoter activity as a function oxidised and reduced soxR

The reaction between Pyocyanin and soxR was modelled as a fast, reversible reaction with the following equation, where K represents the reciprocal of the concentration at half maximal GFP, and m is a measure of the gradient of the resulting sigmoid. Thus, the full set of governing equations for the system is below.

Formula 3. Full governing system for the Pixcell construct

We initially computed the ODE model using both Euler’s method and Matlab’s built-in suite of ODE solvers in order to check that the automated solvers were returning valid results. Once this had been confirmed, we switched to exclusively using the ODE solvers in order to increase model efficiency and perform feature extraction (such as maximum GFP expression and points of inflection). INSERT DIAGRAM COMPARING EULER TO ODE

Because Pyocyanin oxidises soxR directly, we assumed that the oxidation of soxR by Pyocyanin was fast compared to the timescales of the biological processes in the model, which are limited by the diffusion of large biological complexes. We therefore modelled the quantities of oxidised and reduced soxR as instantaneously created from the total available pool of soxR, splitting the pool in the following way.

Formula 4. Proportions of oxidised and reduced soxR as a function of Pyocyanin concentration

We also assumed that the cells had been left to grow to steady state before the simulation. Thus, as the model does not contain any feedback or otherwise complex dynamic features with respect to soxR transcription and translation, the ODEs describing soxR do not affect the rest of the system. Thus, they can be set to their steady state value before running the model. We know from literature that the steady state expression of our soxR gene in its parent strain is 75 molecules per cell, so we can multiply this by our copy number, and set the total soxR in our model to this value. This removes the need for parameters for the transcription and translation of soxR, and the need for the governing equations themselves, but these were left in for the sake of consistency and in case future work requires analysis of soxR dynamics. INSERT PARAMETER TABLE

Most notably, this regime was used to estimate parameters for the reaction between Pyocyanin and SoxR, which were unknown, as well as the basal expression, the GFP:RFU scale of the measurements, and the degradation rate of GFP, given the transcription/translation rates sourced from literature.
In order to utilise the entire available dataset, rather than merely the steady state GFP values for each Pyocyanin concentration, we constructed an objective function that could generate ODE plots. The reasoning behind using this more complicated strategy is as follows.
The steady state GFP reading can be computed using equation [INSERT EQUATION NO] below. This is done by assuming that the system has been left for a long time and has settled, so that no quantity within the system is varying with time. Thus, all derivative terms in the model are zero, and so can be rearranged and combined together into one equation. Observing this steady state equation, it can be seen that there is orthogonality in all of the parameters in table INSERT TABLE NO, besides the Scale and the degradation rate of GFP; a twofold increase in the Scale parameter can be compensated for by a twofold increase in the GFP degradation rate. However, the same is not true in the time-dependent model. It can be seen from figure INSERT FIG NO that a twofold increase or decrease in these parameters leads to a differing transient response, and the same steady state response. Thus, an objective function that utilises transient data can fit both the GFP degradation rate and the GFP:RFU scale simultaneously.

Formula 5. Steady State GFP as a function of Pyocyanin

INSERT table: fitted params

Figure 12. Time-dependent curves show the non-equivalence of parameters

Figure 13. Differential transient behaviour highlighted by the removal of basal expression

It should be noted that, due to the steady state nature of the soxR dynamics in the fitted data, we can remove the ODEs describing soxR transcription and translation in order to decrease the significant computational cost associated with running many ODE solvers for each optimisation iteration. The governing equations to which the optimiser is fitting are then given below.

Formula 6. Reduced set of equations to which are used by the optimisation algorithms to fit experimental data

Convergence of optimisation algorithms is of vital importance, and plotting the norm of the residuals (ie the dataset-wide error of the model) can be qualitatively very informative. An optimisation problem can be said to have converged if the norm of the residuals tends towards a certain value, at which the parameters allow the model to best fit the data. It should be noted that “best fit” is a misnomer; in practice, it can be very difficult to find a global minimum for the objective function, and so the computer can only find the best possible parameters close to the region it starts within. It can be seen from Figure 14 that convergence is obtained, and from Figure 15 that a reasonable fit of the data is achieved.

Figure 14. Convergence of model utilising the entire available dataset

Figure 15. Model function with fitted parameters compared to experimental data

However, upon inspection of the residual plot, below, it can be seen that there is noticeable patterning; there are regions which are consistently above or below zero. For a good model fit, the residuals should be randomly distributed about zero.

Figure 16. Dataset-wide plot of the residuals of the fitted model, showing undesirable clustering in their distribution

One noticeable feature of the residual plot is the overestimation of GFP expression at high Pyocyanin concentrations. Upon observation of the OD600 data (LINK TO OD600), it can be seen that, for Pyocyanin concentrations for 10μM and above, cell growth is significantly hindered. Although the model normalises for cell number, this lower OD600 is a sign of struggling cells, which activate stress pathways and downregulate non-essential genes, such as the synthetic construct. Therefore, the fit, and therefore the accuracy of extracted parameters, can be improved by omitting this data. Observing the convergence of the fitting algorithm for data up to 5μM, it can be observed that the norm of the residuals is drastically reduced. This signifies a better fit to the data. Note that this does not necessarily mean a better model, but is a useful measure nonetheless.

Figure 17. Convergence of the model for healthy cell data, noting the improvement of fit

Figure 18. Model function with parameters fitted to healthy cell data, compared to experimental data

Figure 19. Healthy cell model residual plot, noting reduced clustering

Observing the plot of the residuals for the reduced dataset, additional patterning can be seen in low time points. Plotting the initial point for every time series in the reduced dataset, it can be seen that there is a strong correlation between Pyocyanin concentration and initial GFP expression, due to nonidealities in experimental conditions. INSERT t = 0 GRAPH
In order to account for this bias, data could be normalised and then fitted. For example, in the plots below, the average of the first six data points are subtracted uniformly from the data. Although this provides a small reduction in the value of the norm of the residuals upon model convergence, the fit is not markedly improved, and the residuals show higher clustering than before the normalisation. Thus, we concluded that the model cannot be easily normalised without significantly altering the data to improve the fit, which would invalidate the model and destroy any predictive power it might have. We therefore conclude that our best estimates of the parameters are found by fitting the model to the unaltered, healthy cell data.

Figure 20. Convergence for healthy cells, normalised to account for correlation between initial fluorescence and Pyocyanin concentration

Figure 21. Model fit for healthy cells, normalised for initial variable bias

Figure 22. Plot of residuals for normalised healthy cell data, noting higher clustering than the non-fitted group

The fitted parameters returned by the curve fitting regime are given in the table below. INSERT TABLE

It can be seen that most parameters lie around their expected ranges, with the exception of tagged GFP degradation rate. To check this, the steady-state models are plotted against the final timepoint from the data. This model suggests that the degradation tag only increases the degradation rate by approximately a factor of 1.2, not by the expected factor of 8 (Tschirhart et al. 2017). On the basis of this unexpected result, the construct was sent for sequencing, and a mutation was confirmed within the degradation tag. It was decided, however, to keep using the mutated sequence, as it provided a good balance between reducing background and still retaining a measurable fluorescence when induced.

Figure 23. Plot of model fits for tagged and untagged constructs at steady state