When I first started in science, I did a lot of reading. It seemed like a good idea at the time: read the literature. Get familiar with the field. Think deeply. And come up with a new idea. But this approach no longer works and we need to radically re-imagine the way we approach science.
When I first started in science, I was a pre-med student in a chemistry lab. As a young undergraduate, I was trying to understand mesoporous silica materials. I then did a PhD combining materials science and microbiology, exploring new imaging applications for semiconductor quantum dots. I then went to a post-doc that brought me back to human biology and medicine, where I learned metabolism and physiology. Finally, when I started my own lab, the lab took a unique approach combining these seemingly disparate fields to understand metabolic regulation from a chemical and biochemical perspective. Each of my starts and restarts across fields required me to do a lot of reading. It seemed like a good idea at the time: read the literature. Get familiar with the field, as best as I could. Think deeply. And then come up with a new idea to test. But this approach no longer works and we need to radically re-imagine the way we approach science.
This realization became apparent a few years ago. The lab uses multiple ‘omic approaches to better understand the systems we study: mainly metabolomics and proteomics. This means we measure proteins and metabolites in cells and tissues, and then get A LOT of data to determine what’s going on in our experiment. After too many times of manually crunching the numbers, I thought there must be a better way. I think a lot about systems to make repeated processes easier, and this is a perfect example. The emerging field of data science focuses on tools to turn data into knowledge. I tried to convince the members in my lab that we should dive into data science because we need to be able to analyze our own data; no one listened. I said it louder; still, no one listened. So I decided to do it myself.
Like anything new, it was hard at first. I did most of my learning between 9pm-midnight, after everyone was asleep. But like any learning project, consistency + time meant I eventually made progress. And before I long, I was spending more and more time programming, solving problems, gaining efficiency. So how does a pre-med student, turned chemist, turned molecular biologist-animal physiologist who’s running an academic research lab become a computational biologist and computer programmer? Looking back I see three key drivers.
Because creativity is connecting things, always explore new technologies and innovations, especially in other fields. An exploratory mindset will connect ideas across fields and lead to new ideas of how can that be applied to your field. As Linus Pauling famously quipped, “The best way to get a good idea is to have a lot of ideas.” A circuitous route can lead you down a new and important path.
2. Experiment and Learn.
Always experiment to learn. A flurry of random activity does not result in mastery. Instead, thinking like a scientist and running experiments to understand the problem, and then systematically exploring possible solutions leads to learning. But also experiment about learning. Academics can take lessons from YouTube, cohort-based course creators, and experiments in education that are playing out in digital spaces. By exposing yourself to new ideas and new approaches across disciplines, you’ll learn what is already happening, and finally what is possible.
3. Imagine the future.
When trying to consider solutions to a problem, I often say “suspend reality for a moment”. This trigger creates a safe space to explore and experiment where no idea is too absurd. Radically reimagining possible futures requires radical new ideas.
Skeuomorphism is a term often used in graphical user interface (GUI) design to describe when an interface object mimics a real-world counterpart in how it appears or how the user interacts with it. Some well-known examples are:
- the ubiquitous folder-file structure to organize a computer’s contents to mimic a filing cabinet
- the recycle or trash bin icon to mimic discarding files
- a rich, leather-trimmed version of calendar apps that mimic a daily planner
The idea skeuomorphic is now more broadly used to describe designing software or applications that mimic real-world experiences. Zoom recently rolled out a feature that allows a meeting host to place their meeting participants in virtual rows, mimicking a classroom or amphitheater. Skeuomorphism makes objects familiar to users by using concepts they recognize.
However, skeuomorphism is inherently limiting. Digital spaces allow for new interactions, new designs, and new concepts untethered to the limitations of physical reality. The first iteration of something new often mimics the real-world (mobile phones needed a pen or keyboard to input information). The next iteration should suspend reality, and look for new, digital-native ways to interact (like the voice dictation I’m using to write some of this article). Skeuomorphic thinking is stuck in the real world, but the future is not.
While I’ve learned these lessons through science, they are not restricted to scientific disciplines. In fact, a new Silicon Valley battle cry is “experiment like a scientist!" These principles are being applied across disciplines. Nobel laureate Niels Bohr quipped, “It is difficult to make predictions, especially about the future.” You can’t know how an experiment will work out before you do it. But you also can’t know how it’ll turn out if you never do it. So while some might look on and think your approach is radical, experimentation is the only way to reimagine the future.