Briefly Bio, a startup based in London, has announced a small but meaningful $1.2 million round from Compound VC and others with the goal of making scientific experiments and data more reproducible.
In fact, so many thousands of results of scientific experiments haven’t yet — or possibly can’t be reproduced — that some researchers have deemed this a reproducibility or replication crisis.
In an effort to help solve this problem, Briefly Bio’s platform uses large language models (LLM) like the kind powering leading AI products such as ChatGPT to turn complex lab documentation into a consistent, structured format. The idea is that anyone can use and build on the original documents in their own lab more easily.
The company has already roped in early customers for the paid version of the offering and has plans in place to create a public version of the platform for open sharing and collaboration on scientific data that it says will be similar to GitHub’s code repository for sharing open source and permissively licensed software.
“As GitHub helped software engineers collaborate and build on each other’s code, we think Briefly can help scientists and engineers do the same with their experiments,” Harry Rickerby, the CEO and co-founder of Briefly Bio, said in a statement.
The data reproducibility problem in science
When scientists try to solve a complex biological problem, they take different approaches. Some methods work, some partially do the job and some don’t at all, but in all cases, the protocol for the lab work — the plan for research, covering objectives, design, methodology and statistics — and the details of the experiment itself are documented thoroughly.
The idea behind collating this data is to give other scientific teams a base of sorts to continue the research or solve any other closely related problem. However, this is also where the problem of reproducibility begins.
Essentially, every scientist has their own way of documenting their work, which in many cases leads to ambiguity and the loss of critical details crucial for shared understanding.
For instance, some researchers may go into extensive detail when describing their approach to gene editing in their own words, while others may just scratch the surface with the notion that other teams may have similar knowledge. This can easily lead to inefficient collaboration and failure at reproducing experiments, costing the industry over $50 billion each year.
Rickerby told VentureBeat he and his colleagues at LabGenius Katya Putintseva and Staffan Piledahl saw the problem first-hand at different levels.
“Katya worked as a scientist in academia and faced the challenges of re-using and adapting work from the published literature. She then moved into data science, where she needed to understand precisely how the data was generated to analyze and model it. On the other hand, Staffan worked as an automation engineer and needed complete definitions of a lab workflow to transfer them to robots. After leaving, we realized that many struggles across our careers shared a common root cause – there wasn’t consistent documentation of how lab work was being run,” he said.
To address this, the trio came together and launched Briefly Bio. At the core, the company provides scientists with a platform that can convert any scientific protocol documented in natural language into a consistent, structured format containing step-by-step information. All the user has to do is provide the blob of text from the original author and the tool comes up with a structured output detailing the method for reproducing or building on the experiment.
“Briefly’s tool is powered by generative AI, which helps structure plain text descriptions of procedural knowledge and convert them into a hierarchical representation. The large language models under the hood automatically extract the key pieces of information and categorize them into different processes, actions, explanations and parameters. This structured representation is then transformed into a visual representation that is clearer and easier to digest than a wall of text,” Rickerby explained.
The offering not only creates a shared language for data understanding but also paints a clear picture showcasing how scientific methods change and evolve, in a way that just hasn’t been possible with traditional text descriptions.
More importantly, in addition to converting existing scientific descriptions into a structured format, Briefly Bio also includes an AI copilot, which can be triggered via natural language to spot errors and find as many parameters as it can find in connection to the lab work being done. The AI generates missing parameters in a matter of seconds, enriching the hierarchical representation of the method for reuse in a lab experiment.
The CEO did not share the exact details of the models powering the whole experience but said they are building on top of existing models, enriching them with additional experimental context to improve the lab work understanding.
For reusing the generated data in experiments, teams can launch Briefly Bio’s workspace. It copies the enriched, structured method as is while allowing users and their team members to mark each step as complete/incomplete with associated calculations, text and sample layout painting a picture of what’s in each well of the user’s plate, layer by layer.
Briefly Bio’s to create a Github-like platform
While Briefly Bio is still in its early stages, the company claims it has started booking revenue from first customers on a per-user-based SaaS model.
Our users are typically wet lab scientists, working in early-stage research and development – whether this is in academia, or in biotech and pharma – and looking for a clearer way to document and share their work. We’ve also found a lot of interest from those working in laboratory automation, using Briefly as a way to collaborate with scientists to properly describe their workflows before they program the robots,” Rickerby noted.
In the long run, the company wants to build this work and also open up a public version of the platform for sharing experiments and protocols. “This will allow scientists to discover complete, reproducible methodology that they can easily adapt and use in their own labs – just as Github did for open-source software development,” the CEO added.
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