Team:Concordia-Montreal/Model
The best practices in modelling are for predicting reactions and outcomes of different components of the project. Doing this before the execution of the project allows for the modification of the design without the investment of materials and time into an ineffective prototype. Modelling in the case of our project was used in a deterministic way: to assess the effectiveness and visualize the molecular and physical properties of our project before its execution.
Three kinds of models were generated:1- A structural model of our transcription factors was done in Rosetta for analysis of protein binding affinities, folding and interactions with other biological and non-biological elements, which may generate false positives for example.
2- a model for diffusion of our signalling protein was analysed to assess the feasibility of a chromogenic signal for fentanyl detection as amilCP chromoprotein is expressed.
3- a model of the physical design of the electronic circuit helped us determine how to construct our hardware with respect to various restrictions such as size.
All three models were made to measure the feasibility of our design with consequent changes made upon analysis of the modelling results.
Contents:
1. Structural models of transcription factors
2. Diffusion model for chromoprotein amilCP
3. Physical model of electrical device
1. Structural Model of Transcription Factors
A structural model of our transcription factor was generated which could be used to calculate binding affinities or to find out how well it binds to other molecules, generating false positives. It was modelled in Rosetta Commons from the known structures of the proteins being fused together with our amino acid sequences overlaid.
Figure 1. GGV
Figure 2. Fen49
Figure 3. Fen21
2. Diffusion Model of AmilCP
AmilCP blue chromoprotein from Acropora millepora is expressed when fentanyl is bound. In our design, the hydrogel changes colour as AmilCP diffuses in the gel. This diffusion and change in colour being a chromogenic alert that fentanyl is being detected. Modelling was essential to determine whether the chromoprotein would diffuse to a visible extent.
Figure 1. Initial design for diffusion of chromoprotein in hydrogel as a chromogenic signal.
The linear model for diffusion of amilCP in water shows very little migration at high concentrations of protein. From this we have deduced that the chromogenic signal, or colour change in the hydrogel, may not be sufficient for users to see a visible difference. The model has guided our focus to the electrochemical signal, which can be translated to electronic notifications such as texting first responders, heavy vibrations or lights on the device to name a few. We reached out through our modelling to iGEM ULaval and asked them to developed algorithms for diffusion for our electrochemistry sub team. The sub team was then able to work with the algorithms in Matlab to generate models.
Figure 2. Algorithm for Infinite Diffusion generated by iGEM ULaval, inspired by The Mathematics of Diffusion by J.Crank
To predict a reduction in diffusion due to porosity and interactions within the hydrogel, varying diffusion coefficients were introduced at varying concentrations over time. The model shows diffusion is occurring, but over a minute distance. Fluorescence Recovery After Photobleaching (FRAP) Microscopy is to be used to determine the diffusion coefficient of chromoprotein in the chitosan hydrogel. In obtaining the diffusion coefficient, we will be able to come back to the model to determine the expected position of diffusion from origin.
Our diffusion model shows minimal, albeit sufficient concentration distribution within a linear space over time to continue with this aspect of the project. The modelling did encourage us to place more focus on the reduction-oxidation reaction, which produces an electronic signal.
Figure 3. Concentration gradient of chromoprotein (diffusion constant= µm2) in water.
Figure 4. Concentration gradient of chromoprotein in water with varying diffusion coefficients so as to envision reduced diffusion due to hydrogel.
Reference:
Crank, J. (n.d.). THE MATHEMATICS OF DIFFUSION. Retrieved August 2019, from http://www-eng.lbl.gov/~shuman/NEXT/MATERIALS&COMPONENTS/Xe_damage/Crank-The-Mathematics-of-Diffusion.pdf.
3. Physical Model of Electronic Device
Three-Dimensional Model of the Electronic Device
KiCAD, which is used to create the circuit schematics and printed circuit board layout, also provides the capacity to generate a 3D model of the electronic boards. The 3D model is essential to the design of a viable product. The model provides visual insight into the printed circuit board’s structure.
Major Insights were derived from this model. The precise location of the connectors was determined based on the 3D model. To ensure that the inter-board connectors were perfectly aligned with the edge of the board surface, a visual confirmation is needed prior to manufacturing and assembly. The position of the battery connector with respect to the microcontroller is determined from the 3D model. Since the board’s battery connector requires a certain space in front of it to accommodate the external battery connector, the microcontroller’s position with respect to the board’s battery connector must be adjusted accordingly. The 3D model also provides a good visual estimate of the height of the components which provides strong insight into the available space between the boards. This insight can be used to determine the constraints on the battery’s thickness.
In Figure 1, the 3D model can be observed from an angled view. The model is generated using the printed circuit board layout as a blueprint and by obtaining the necessary 3D computer-assisted design files from the manufacturer of each component. The 3D model shows the electronic components, traces, vias and other drill holes. The furthest board in Figure 4 is the top board containing the primary power module and the command and data module. The closest board in Figure 4 is the bottom board which contains the sensor module and the secondary power module.
Some assumptions were made when generating and studying the 3D model. First, the thickness of the boards viewed in the model are a standard size and cannot be changed, therefore, the designer must assume that the actual boards will have the same thickness as the board in the model and if not they must be aware of such a difference. Second, the 3D model is based on the exact dimensions of the electronics board determined in the printed circuit board layout. However, manufacturing of printed circuit boards cannot guarantee a zero tolerance product, therefore, the actual electronic boards will come with small discrepancies in dimensions.Third, some electronic components in the 3D model are represented by rectangles or by other approximative shapes, such as the microcontroller and some connectors. Therefore, the designer must also refer to the datasheets for additional insights.
