Now that our time as an iGEM competition team comes to an end, it is not only time to look back on our journey and present our results, but to showcase our project's potential and the next steps to be taken towards a real impact on global health.
Time restrictions as well as financial constraints and lack of skillset and access to equipment restricted the number of experiments we were able to run during this project. To advance this platform toward clinical application, we propose the following experimental roadmap:
The computational models we have built are not static records of our work; they are living tools that will guide, accelerate, and de-risk the entire future development of our diagnostic platform. The path forward is a deeply integrated one, where every step in the lab is informed and enhanced by our "digital twin.”
Our results have laid out a clear and logical set of next steps. The immediate future is focused on closing the engineering loop - using our model's predictions to build and validate a functional, real-world device.
The highest priority is to experimentally validate our model's most critical insight. We will build and test the "two-chamber" hardware prototype that our model proposed. This is the ultimate test of our work: to confirm in the lab that a physical separation of the RPA and CRISPR reactions successfully resolves the "cis-cleavage deadlock" that we predicted in silico. The kinetic model will be instrumental in this process, helping us to simulate and define the optimal incubation times and flow rates before we even begin prototyping, saving significant time and resources.
Simultaneously, we will focus on transforming our theoretical frameworks into fully predictive tools. The Enzyme Stability Model, currently a logical blueprint, will be parameterized using data from the planned long-term stability and freeze-drying experiments. Once validated, this will become an invaluable tool for forecasting the shelf-life of our diagnostic under various real-world storage conditions, a critical step for manufacturing and distribution. In the same way, we will complete the asymmetrical RPA / DNA hybridization model, parameterizing and validating it against experimental data to enable a direct, quantitative comparison of the two detection strategies we explored.
Beyond these immediate goals, the tools we have developed open the door to a truly visionary future, evolving our project from a single diagnostic test into a platform for global health innovation.
We see our kinetic model evolving from a "digital twin" into an "AI co-pilot" for diagnostic design. By using our current model to generate tens of thousands of simulations, we can train a machine learning algorithm to understand the deep patterns of the reaction. This would create an ultra-fast predictive tool. A researcher could then ask the AI, "What is the fastest possible time-to-result for a new pathogen?" and receive an optimized set of biochemical conditions in seconds, dramatically accelerating the development of new tests during future outbreaks.
Furthermore, our model's predictive power can be harnessed to create a quantitative diagnostic tool, moving beyond a simple "yes or no" answer. The speed at which a signal develops is directly related to the amount of pathogen in the original sample. Our kinetic model can precisely map this relationship. We envision a smartphone application that analyzes a real-time signal from our test to provide not just a positive result, but also a reliable estimate of the viral or bacterial load. This would transform our test into a more powerful clinical tool, allowing doctors to monitor infection severity and the effectiveness of treatment.
Ultimately, we envision our platform contributing to a global pathogen surveillance network. Imagine a future where thousands of our low-cost, paper-based tests are used in communities worldwide. Each result could be anonymously uploaded to a central database, feeding real-time data into our epidemiological model. This would create a decentralized, community-powered system for tracking and predicting outbreaks on a local and global scale, providing an early warning for the next pandemic. In this vision, our project becomes more than a tool for individual diagnosis; it becomes a cornerstone of global biosecurity.
Medical diagnostic tools are subject to stringent local and international regulations. To transform our project into a market-ready product, these regulatory requirements must be carefully fulfilled. Our Human Practices team collaborated with experts to identify the specific development steps and compliance standards necessary for our test to be approved for its intended clinical use. Moving forward, it will be critical to compile the necessary technical documentation, and engage with regulatory agencies to initiate the approval process.
As part of the Entrepreneurship Special Prize track, our team evaluated the market potential of our diagnostic tool. This included exploring potential user segments, pricing strategies, and distribution channels to ensure that our product can be both commercially viable and accessible to those who need it most. Next steps will focus on securing partnerships and on obtaining funding to support production and distribution.
Our Dry Lab's epidemiological model has already provided a powerful quantitative argument for the importance of accessible diagnostics. In the future, this model will serve as a crucial advocacy and planning tool. We believe that increasing global testing rates is essential in the battle against infectious diseases. Our model can be adapted to different diseases and regional demographics to provide data-driven projections to policymakers and public health organizations, helping them understand the potential return on investment from deploying rapid, low-cost tests like ours. Our goal is to ensure that our work not only succeeds in the lab but also makes a measurable impact on global health.