Data and Digitization – The key to better biology and more effective therapies

Data is the cornerstone of any scientific endeavor. But throughout history it’s been managed pretty poorly. Most data ever gathered on cells remain in hand-written lab notebooks, gathering dust on shelves after the experiment’s ended. Small snippets of data make it to the public eye through accepted publications, which shape future experiments and lines of investigation. But all that other data collected from hours of planning experiments and measuring endpoints that isn’t up to publication standards – like negative data – is essentially lost. Imagine how much more could be learned if all the data collected over the past century on cells was lifted, centralized, and analyzed.

Whilst this may not be feasible for the past, using digital systems could make this a reality in the future. Things like electronic lab notebooks are already used in the lab, helping to digitize note taking at the source making it easy to record, transfer, and store data. But the evolution of digital tools and equipment will:

• Automate monitoring of cells and the cell culture environment during and after experiments
• Process data to make it exploitable (e.g., images of cells into cell circularity, cell size, cell count, cell eccentricity)
• Organize data so it can be easily visualized
• Analyze data through machine learning algorithms to help uncover insights that isn’t currently possible

Before we can unlock the incredible power of digital systems, research labs and manufacturing sites need the right infrastructure and equipment. Communications standards are starting to come together to allow this, for example with OPC-UA, but there is an unmet need for hardware that can generate the actual data during cell culture.

At MFX, we build platforms that can manage up to 30 bioreactors in one device, with each bioreactor generating hundreds of thousands of data points thanks to integrated online sensors and mutualized analytical equipment. All the data is processed, organised, and presented to the user in a digestible format, and we partner with the best machine learning providers to generate insights.

The benefits of this are countless and include:

• Reducing process development time
• Reducing the number of experiments necessary to uncover insights
• Making it easier to transfer knowledge from one person/organization to another
• Discovering new treatments faster

What’s more, these benefits building exponentially as your database expands. Now, that’s an exciting future for biology.