iGEM 2022 — Team mcmaster-canada

# Section 3: Notebook

#### Dry Lab-Design of modeling methods

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In order to simulate the two-cell consortium model, we used Simbiology to model our system with the hopes of testing the sensing ability and logic-gate functionality of the system (see [Model](https://2022.igem.wiki/mcmaster-canada/model)). In order to determine reaction rates and product concentrations over time, we modeled a number of different reactions using various kinetic equations within Simbiology. Each of these reactions required the related association, dissociation and kinetic constants in order to create an appropriate model. While our ability to determine these constants was limited initially, by surveying literature and making frequent improvements to our analytical procedure, we were able to create a computational pipeline that could predict our binding constants.

We initially planned on finding these constants using previous literature and making reasonable constant approximations based on proteins of similar function. This is a strategy that has been used by numerous iGem teams in the past, and we therefore believed it was appropriate. However, after reviewing the literature related to the HrpS, HrpV and HrpR proteins, we found there were little to no studies that explored their binding and activity to their respective ligands and proteins. Further complicating our issue was the fact that HrpR and HrpS, which we assumed to be the rate limiting step for this set of proteins, created a dimerized complex before acting on its ligand. This made it difficult to approximate constants based on proteins of similar function. The lack of information was a huge issue, as this set of proteins was crucial to the logic-gate functionality of our system, and without appropriate constants our model’s precision and validity were limited.

To combat this issue, we surveyed literature for tools that could determine the molecular dynamics of proteins based on their 3D structure. We decided to use transcomp, which can predict the Kon and Ka (from which we could calculate Koff) for bound proteins. In order to determine the structural binding of dimerized proteins, we decided to use Hawkdock, a well-cited method that uses a docking algorithm to determine protein-protein interactions. A more detailed breakdown of this pipeline can be found in [2.3.2 of our Modelling section.](https://2022.igem.wiki/mcmaster-canada/model)

Despite creating this pipeline, we were still missing the PDB files of the proteins in the HRP system that described the 3D structure of each protein. These files were necessary for Hawkdock to determine dimer interactions, and for transcomp to determine protein binding. While we surveyed the literature and relevant protein and gene databases, we could not find the PDB structures for the proteins in question, and could only find their amino acid sequences. At this point, we were left with an unfinished pipeline.

However, the July 2021 release of AlphaFold by Deep Mind gave us our final piece of the puzzle. Alpha Fold is a revolutionary tool that can predict the 3D structure of a protein given its 2D amino acid sequence. By using AlphaFold we could generate the necessary PDB files, and create predictions for our dimerization reaction kinetics using the aforementioned tools. From this our Dry Lab Team was able to design a streamlined procedure that could predict the association, dissociation and affinity constants for dimerization reactions only from their amino acid sequence. By consistently determining the limitations of our procedure and seeking new tools from the literature, we were able to design a computational pipeline using novel tools that can be used to find constants when literature is limited, a problem that is frequent for iGem Dry Lab teams.

#### Wet Lab-Experimental design

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Due to limitations in our access to the wet lab, we were unable to move past certain roadblocks in the building phase of our experimentation. However, we had designed a series of procedures that involved testing and troubleshooting at different expected checkpoints. This included testing and troubleshooting all of our system components individually to ensure their independent success. Then, we would build different combinations of our components to see their success in pairs before moving on to assembling the entire system.

Our Wet-Lab engineering design process began with the actual designing of the gene fragments, which took quite a bit of trial and error in itself. First, we gathered the necessary gene fragments for the different components of each receptor system from various sources. Once we inputted these into the Benchling software, we worked on combining the individual parts (e.x promoter, target protein, restriction enzyme sites, RBS, and fluorescent marker). Then, we designed primers for each receptor system. This was done through designing and redesigning to get to the optimal sequence of base-pairs, to be checked on by our PhD advisors. Once the primers were complete, we would input our sequences into IDT to scan them for complexity, and more often than not we were faced with over-complex sequences that needed to be optimized. Thus, we would go back to the drawing board and fix up the sequences in the software then proceed to re-input them into IDT. Finally, once we had IDT-approved sequences, we ordered them.

Since our system is quite complex with multiple sub-parts playing into the overall design, we decided it would be best to test the individual sub-systems individually, then gradually move into testing them together, and then finally the whole system as one. While we were unfortunately not able to carry through our planned series of experiments in-lab due to time and resource constraints, we did have a thorough step-by-step experimental system to ensure the success of each sub-part before moving on to the next. This set of experiments can be found on our [Experiments](https://2022.igem.wiki/mcmaster-canada/experiments) page, but involves measuring the success of each sub-part before testing the logic-gate functionality of our system.

We were hoping that by theoretically conducting these experiments in a methodical manner, we would be able to establish clear checkpoints for troubleshooting. For example, while initially working with the indole system, we faced some trouble. Since we had to split the indole system gene sequence into two fragments, we had to perform PCR assembly to assemble them together. However, we faced quite a bit of trouble with this as this was a procedure none of our members had performed prior and we had very minimal guidance. As such, we decided to continue researching and trying to troubleshoot the proble with the indole sequence while simultaneously beginning to work with the butyrate and gaba sequences as we were able to get both as full single sequences. This was an important part of our design process, because it allowed us to recognize the issue we were facing in the building and testing phase of the design cycle, work towards learning what went wrong and how we can move forward, while simultaneously testing other systems that we knew we would not face the same issues with.

Our trouble shooting steps typically went as follows:

1. The members in-lab at that moment would discuss amongst themselves, research online, and try to come up with ways that experiment went wrong and possible solutions.
2. If step 1 failed, in-lab members would approach the PhD student and lab members belonging to the lab we are working in if they have any ideas of what could be going wrong and how we can fix it.
3. If step 2 failed, the in-lab members would pose the issue to the rest of the team on-line to gather different perspectives and brainstorm possible solutions.
4. If step 3 failed, the issue would be tabled for the day and our out-of-lab PhD student advisors would be contacted to schedule an online meeting to discuss the issues, the team would present the problem and hopefully a solution can be reached.
5. If all previous steps failed,the team would table this experiment temporarily while conducting more research out of lab and trying to contact other knowledgable advisors, in the meantime the team will pivot their focus in lab to focus on other aspects of the project.

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There were two major lab experiments: successful transformation of designed parts into bacteria, and testing of the final components and system.

#### Transformation of engineered bacteria

The majority of our time spent in the lab this summer was related to transforming our bacteria with our designed parts and receptors. We began by individually working on each metabolite receptor system, ensuring that the receptors were functional and would respond to the metabolites accordingly. The following list are example procedures that were carried out to begin this process:

1. Amplifying sequences via PCR
2. Gel electrophoresis
3. Gel Extraction
4. RE digest
5. Ligation
6. Miniprep
7. DNA transformation
8. Growing bacterial colonies

Unfortunately, we were unable to complete these systems prior to Jamboree, as we faced difficulties with ordering materials which hindered our timeline. However, we did manage to successfully amplify sequences via PCR, run Gels, Extract DNA from Gels, Grow bacterial colonies, and Miniprep Plasmids from bacterial cells, all of which will be elaborated on in the next section.

#### Testing engineered system

As we were still working on validating our transformations, we did not reach the second part of the experiment that allows us to test the system as a whole. We plan on completing these experiments in the following year.

The following is a list of planned experiments:

* Basal expression readings
  + No indole, GABA, or butyrate in the cell and measuring basal GFP, BFP, and RFP
* Input to output time
  + Establish timing between input/addition of metabolites and output fluorescent readings (note half-life decay time of fluorescence if applicable)
* Fluorescent decay timing
  + How long does it take for the FPs to decay? How does this correspond to the input to output time?
* Testing fragments individually and together
  + Testing indole fragment through measuring GFP expression
  + Testing GABA fragment through measuring BFP expression
  + Testing butyrate fragment through measuring YFP expression
  + Testing LuxI/R (only indole) through measuring GFP and RFP expression
  + Testing high indole, low GABA, we expect to see high GFP, low BFP, high RFP expression (also testing coactivation by HrpS/HrpR in CELL1+2)
  + Testing low indole, high GABA, we expect to see low GFP, high BFP, no RFP expression (also testing HrpV inhibition in CELL1)
  + Testing high indole, high GABA, we expect to see high GFP, high BFP, no RFP expression
  + Testing high indole, low butyrate, we expect to see high GFP, low YFP, high RFP expression (also testing HrpV inhibition in CELL2)
  + Testing low indole, high butyrate, we expect to see low GFP, high YFP, no RFP expression
  + Testing high indole, high butyrate, we expect to see high GFP, high YFP, no RFP expression
  + Testing high GABA, high butyrate, we expect to see high BFP, high YFP, no RFP expression
  + Testing butyrate sensitivity by adding other SFCAs
* Dose response curves
  + Percent-fold increase in GFP after 25%, 50%, 75%, and 100% fold increase in [indole]
  + Percent-fold increase in BFP after 25%, 50%, 75%, and 100% fold increase in [GABA]
  + Percent-fold increase in RFP after 25%, 50%, 75%, and 100% fold increase in [butyrate]
  + Establish saturation effects
    - Indole, GABA, and buyrate minimum and maximum threshold concentrations
  + LuxI to 3OC6HSL turnover rate via activation of LuxR and subsequent coactivation of PHrpL and CFP

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