Serokell developed an MVP of a data analysis tool for biotech researchers
One of our partners – an international biotech company that works on full-cycle pharmaceutical manufacturing, starting from initial molecule search to mass production. Their research teams had problems with their closed-source data analysis tool, Magellan, and wanted a tool that would be open-source and have more possibilities for individualization.
Problems with the closed‑source solution
of curves is small
To answer the client’s needs, we designed and built Edna, an open-source data analysis tool that also provides a convenient way of storing data.
Edna is web-based and does data analysis in the cloud, enabling researchers to use personal machines for work. Compared to some of the other alternatives in the market, the solution is:
- Completely free and extensible.
- Due to the open-source nature of the app, it can be used and improved by multiple biotech companies in collaboration, if necessary.
- It can also be personalized to fit the processes of the company that uses it.
Via our web UI, researchers can see experiment data and plot graphs for specific compounds.
We built a simple data analysis tool that can analyze experiment data and display needed values and metrics.
We created a tool that stores and displays past data about experiments, test methodologies, and particular compounds.
Minimum viable product
To start off we decided to build an MVP that enables researchers to measure the IC50 of substances by fitting a curve to the 4PL (4 Parameter Logistic Regression) model. 4PL is one of the most common models for bioassays and immunoassays
IC50 is a metric that shows how much of an inhibitory substance is required to inhibit a given biological process or component by 50%.
Because of our expertise with Haskell, we were able to build an extensible prototype quickly. To help users interact with the app, we created an attractive and intuitive user interface that can display experiment data and plot graphs for different compounds. For numerical computations, we used Python.
In the end, our MVP was able to import, store, display, and analyze data from experiments in a highly accessible fashion.
By frequently interacting with our client through demos and stakeholder meetings, we were able to receive rapid feedback on our tool, test it in real-life biotech research environments, and make sure our project is something that biotech research companies require.
Our tech stack
The MVP is ready for testing by Biocad.
If necessary, we can easily extend the application with plugins, an expanded data analysis toolbox, or any other functionality.