Contribution

Overview

In our project development, we created multiple resources that future iGEM teams may find useful:

SNP Database

While we focused on F2RL3 this year, there are numerous other SNPs that influence human health. We have created a database of 105 selected SNPs implicated in both hypercoagulability and drug metabolism. For each SNP, we have compiled a name, gene, and effect, along with links to relevant information. Using LAMP reaction design software, we have created primers for the amplification of each SNP consistent with our fluorescent detection methodology, a process that we detailed here. Future teams interested in building on our work may also choose to use our primer design protocol for detection of other SNPs.

• General SNP Database
• SNPs involved in Hypercoagulability

Optimized Protocols

The protocol we used was based on a published methodology for SNP-LAMP [1]. During our project, we converted this information into concise protocols, which we tested and kept notes on. We hope our work will assist future iGEM teams that may use similar methodology, especially in anticipating similar issues to the ones we faced and learning from our troubleshooting steps. This information can be found throughout our wiki page, especially in our experiments, protocols, results, and lab notebooks. Additionally, since information on running molecular dynamics simulations on DNA is somewhat scarce online, we've created a molecular dynamics simulation protocol for any type of nucleic acid for future teams to use. We've already shared this protocol with the UChicago team during our Computational Modeling Workshop.

• Molecular Dynamics Simulation Protocol

Software and Open Source Design

We developed a novel simulation tool, which utilizes machine learning, to model and optimize our probe-and-sink combination. We used a random forest classifier trained on a DNA binding energy dataset to predict a scaled free energy score per nucleotide in the sequence. The random forest model proved superior to other structures, such as XGBoost, support vector machines, k-nearest neighbors, and multilayer perceptron. The simulation was developed as a command line interface (CLI)-which is the format of similar software in existence-requiring an input of probes and genes; running the simulation; and ordering, displaying, and verifying the results. The software is available for future teams to use at the Michigan iGEM 2023 GitHub repository.

Fluorometer Design

To detect the presence of the SNP post-amplification, our team has been creating a low-cost fluorometer to measure fluorophore activity in our samples. During the research phase, we have documented the foundational principles employed in a fluorometer and how to choose certain components, such as the LED photoresistor pairing and the appropriate fluorophore based on wavelength properties. Our fluorometer is finely tuned to detect fluorescence strictly within the 490-520 nm range. The fluorescence emitted when the SNP is identified falls within this range, ensuring that our device is perfectly tailored for its application. Recognizing the financial constraints many research settings face when acquiring high-end fluorescence readers, we aimed to democratize the process by leveraging 3D-printing technology. Specialized electrical components were sourced externally. Initial tests should involve known fluorescent samples within the 490-520 nm range to validate the fluorometer's accuracy. It would also be beneficial to compare the readings from our device with those from commercial fluorescence readers to benchmark its performance.

Our design plans are available as a resource to other teams. Please see this attached .STL file, our circuit planning notes, our supplies list, or contact our team directly at our email at umichsynbio-team@umich.edu!