If you’ve ever played The New York Times game Connections, you know the feeling. You’re staring at a grid of words, knowing the solution is there, but unable to see how the pieces fit together. All you can do is work with the words in front of you. There are no extra clues, no new information coming. The only option is to shuffle, to look at the same information in a different arrangement until patterns begin to appear.
Nothing about the problem changes. Then something about how you see it does.
This pattern of reframing is a familiar truth in scientific research as well. In a recent NPR podcast, “The Medical Matchmaking Machine,” Radiolab explores this idea through a deeply human story. The episode features Dr. David Fajgenbaum, who survived a rare, life-threatening illness and came away with a new realization about the limits of how existing knowledge was being connected. By systematically reexamining existing data and research through a new lens, he was able to identify life-saving connections for his own disease. Through his nonprofit research organization Every Cure, Fajgenbaum then began applying the same approach more broadly across diseases.
In many cases, potential treatments already exist, but they are buried in data, scattered across studies, or confined to discovery pathways that make connections difficult to see.
When Discovery Isn’t the Problem
We have more knowledge than ever, but our ability to connect and interpret it hasn’t kept pace. This tension is familiar to anyone working in life science research. Today’s labs generate enormous amounts of data. Experimental systems are increasingly complex. Signals can be subtle. Valuable insights are often missed, not because they are absent, but because they are difficult to surface using traditional tools and workflows across large, fragmented bodies of research.
In these situations, biology itself is not always the limiting factor. The tools available to explore it often are.
New Tools and New Visibility
The work of Every Cure shows how researchers can uncover hidden connections by reexamining existing data through computational approaches. By looking across existing drugs, biological pathways and datasets in new ways, researchers can surface possibilities that were not visible when each piece was examined within its original framework. These tools do not replace scientific expertise. They make it possible for researchers to explore more of the solution space than would be practical to evaluate manually. This shift in perspective is also changing how scientists approach targets once considered “undruggable,” a shift possible not because the biology changed, but because the tools did.
This perspective is reflected in peer-reviewed research showing that new insights can emerge from reanalyzing existing clinical and biological data, rather than generating entirely new data from scratch.
The same principle applies across modern life science research. Advances in experimental design, assay development, automation and data analysis don’t change the goal of science. They change what researchers can test and see with confidence.
Helping Scientists See Differently
At moments when seeing differently matters most, our work helps make that possible. For decades, we’ve focused on helping scientists move past constraints they have learned to work around. By creating tools that improve how biology is measured, experiments are carried out and results are interpreted, we help researchers get more insight from what they already have.
That approach aligns closely with the idea that progress often comes from recombination rather than reinvention.
One example of this shift is how researchers use HiBiT tagging combined with live-cell imaging to gain a clearer, real-time view of protein degradation. By pairing existing technologies in new workflows, scientists can observe biological processes that were previously difficult to measure.
As research becomes more data-rich and interconnected, the limits of existing frameworks become harder to ignore. In response, we are also exploring how computational approaches, including AI, can support scientists as they rethink how information is connected and interpreted. The goal is not to promise shortcuts, but to help researchers identify patterns, test assumptions and revisit existing knowledge from new angles.
Progress Through Perspective
The most important takeaway from stories like these is a simple one. Sometimes the solution is already there. It just isn’t visible yet.
As tools evolve, scientists gain new ways to rearrange what they already know. Connections become clearer, and possibilities that once felt out of reach start to come into focus. Our goal is to help make those moments possible by giving researchers better ways to see, test and turn existing knowledge into new insights.
References
- Radiolab. “The Medical Matchmaking Machine.” NPR.
- Clinical Insights from Reanalyzed Data. JAMA Network Open. 2021;4(10):e2133352. doi:10.1001/jamanetworkopen.2021.33352.
- Promega Connections. “A New View of Protein Degradation with HiBiT and Live-Cell Imaging.”
Elise Johnson
Latest posts by Elise Johnson (see all)
- Light Has a Favorite Color, But It’s Complicated - March 12, 2026
- More Than Beer and Cheese: Why Wisconsin Has Always Been Good Ground for Science - February 15, 2026
- The Breakthrough Was There All Along: Rethinking “Undruggable” Targets Through New Ways of Seeing - January 27, 2026
