Transport of CRISPR Systems in Gene Editing for Sickle Cell

Inspiration

We were inspired by the most basic form of transportation--the transportation inside of our body and our cells. We are both interested in CRISPR and other gene editing techniques, which gave us the idea of researching different transport mechanisms used to transport CRISPR inside of cells for in vivo somatic cell editing.

What it does

The program shows how single-nucleotide substitution diseases, such as sickle cell disease, can be corrected using specific CRISPR Cas9 mechanisms to target the mutation. It opens up a simulation that models the percentages of edited cells inside the bloodstream over time in both nanoparticles and viral vectors.

How we built it

We built the code in Python, which we used to create the model and simulation.

Challenges we ran into

It was difficult to figure out how to keep the simulation running and have it continue to update the cells consistently. It would have to be refined in order to be more adaptable to a wider variety of systems.

Accomplishments that we're proud of

The simulation is able to be adapted to model gene editing for other genetic mutations, like beta thalassemia, and could hypothetically be adapted to model all types of cellular transport inside somatic cells.

What we learned

We learned a lot about the different methods of transportation that can be used to transport CRISPR systems, and the differences between ex vivo and in vivo gene editing. We also learned a lot about modeling using simulations in Python.

What's next for Transport of CRISPR Systems in Gene Editing for Sickle Cell

Ideally, we would like to expand our simulation to be able to be applied to a wider variety of cellular transportation, and account for size of molecules, amount of packaging room in the vectors, and other variations that can affect the overall amount of cells with the edited gene.

Built With

• python