Connecting Wet Lab and AI Simulation with SciNote: Turbine’s Metrics-Driven Success
3 min read
At SciNote, we see every implementation as a partnership. This was especially true during our collaboration with Turbine, a team working at the intersection of wet lab biology and simulation. I had the pleasure of working as the Customer Success Manager for this project and partnering closely with Rawan Abukhairan, a Lab Scientist and Molecular Biologist at Turbine. From the very beginning, Rawan was deeply invested in making SciNote an integral part of the lab’s workflow.
Turbine is virtualizing biological experiments with AI to accelerate drug discovery and enhance clinical translatability.
Defining Success Metrics
Together, we set out to define what success would look like for Turbine. We didn’t want general milestones — we wanted specific, measurable goals that could be reviewed every three months. And I’m happy to say: we’ve hit all of them!
Here’s what the key success metrics looked like:
- Facilitate data flow from the lab to the simulation team
- Reduce manual work and minimize human error
- Create a centralized, accessible repository for experiment data
Challenges and Collaborative Solutions
One of the biggest challenges was connecting wet lab work to the simulation team’s process, But through collaborative problem-solving, we identified a way using protocol templates and our API — an example of real-world flexibility.
Since the launch of SciNote Result Templates, the connection of wet-lab and simulation team is able to be direct – supporting Turbines workflow goals of optimization and automation.
A Look at the Workflow
This diagram summarizes how Turbine structures their work in SciNote:
Turbine structures their projects in SciNote using a clear folder hierarchy — main projects, sub-projects, and smaller sub-projects. Experiments and tasks flow directly from this tree. When tasks are completed, data is exported manually to feed into Turbine’s simulation model. While full automation is still in progress, this setup already reduces friction and boosts clarity for all teams involved.
Who’s Using SciNote?
Turbine’s lab scientists and technicians are the most active users of SciNote, while the Heads of Lab and DataFlow teams use it occasionally when needed.
The most commonly used features are:
- Protocol Templates (SOPs) for standardizing workflows
- Inventories, including items and equipment management
What SciNote Helped Turbine Achieve
The Turbine team reports several key improvements from using SciNote:
- Increased data visibility within and across teams
- Better preservation of documentation and lab protocols
- Faster access to experimental data for the simulation team
- Standardization of lab processes
- Reduced manual work and lower risk of errors
In their own words:
“SciNote has provided a protocol template that allows us to standardize how we load data at the end of each project… Even though full automation is not yet in place, SciNote’s suggested workaround lets us enter new data in Excel-based templates, approve the tasks, and then the Simulation team pulls it using the API.”
While there’s still more to be done, particularlly around automating data transfer,the foundation is now strong, scalable, and built with both lab and simulation teams’ needs in mind.
Turbine’s implementation journey is a great example of what happens when technical collaboration meets well-defined goals. Rawan’s commitment, backed by a results-driven plan and flexible workarounds, helped ensure SciNote became a working part of daily lab life rather than just a side tool.
If you’re considering a similar journey with SciNote, we’d love to support you in doing exactly what Turbine did: define your success clearly and work together to achieve it, one milestone at a time. This is valid for new and existing customers, as your needs evolve over time.
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