Coping with Complexity: A Design Perspective of Biotech and Science
The biotech world is a fascinating place to be right now.
In that world, I’ve seen artists and software engineers working alongside biologists to create open source insulin for diabetics. I’ve seen donated lab equipment rattling with bacterial trays in bedrooms and garages. I’ve seen beer made from yeast that expresses firefly proteins so that it glowed green (which you can make at home, btw). I’ve seen people from all walks of life burrowing into science with that deep, unquenchable curiosity that burns like a bright roman candle across the stars.
Back home, among family, friends, and colleagues, I’ve seen a similar excitement for biotech and science take shape. Apple HealthKit and Google searches are introducing everyday people to their own biological, chemical systems and variables. Gym rats overheard describing the impact of sugar alcohol on muscle development. Elysium Health convincing athletes and entrepreneurs of their product’s NADH efficiency. WebMD telling mothers that they probably have AIDs. Young folks at farmer’s markets speaking vaguely of the dangers of GMOs, while describing with certainty details of their genetic ancestral breakdown.
So biotech is spreading beyond the iron fortresses of academia and industry. But this poses a new problem…
Biotech and science are complex: Society is being given this complex information, but not the means to properly make sense of it. What I’ve seen is citizen scientists held back by this complexity, and consumers confused, overconfident, and eager.
As this information grows, methods for understanding, managing, and communicating that complexity are becoming more important.
What I learned from Peacock Tobacco
When I entered the citizen science world myself, I decided to start my own project called Peacock Tobacco. The goal from the beginning was to create plants that share the same iridescent properties as peacock feathers. Biotech had a shortage of “fun”, aesthetic projects, and even if it wasn’t successful, it would be a great learning experience for everyone involved.
This project gave me deep insight into the challenges of biotech’s complexity epidemic. Here’s the breakdown:
Step 1: Literature review of biological iridescence.
Right off the bat, I found complexity in the organization of scientific knowledge itself.
We cast a wide net to find research on biological iridescence. We studied the physics of iridescence, and iridescent physiology in the animal world. We identified the people behind the research and got in contact with them.
How else might we uncover knowledge aside from journal abstracts and references? Must it take 15 pages to communicate one unit of knowledge? What might a post-journal, post-article world look like? What about the research that doesn’t get published?
Step 2: Choose an approach.
After the literature review, I saw that levels of confidence are hugely important for science and the body of knowledge describing it. Based on what we’ve learned, which method would we be most confident in pursuing?
There were a few possibilities. We could modify the leaf cuticles, but cuticle development is poorly understood and required expensive equipment to investigate. Alternatively, certain species have an iridescent “cell within a cell” known as an iridoplast, which we might be able to transplant. Modularity seemed convenient–we could drop it into tobacco, an elegant plant that has a well understood genome.
So the decision was made, but made somewhat blindly.
Step 3: Understand the biological systems.
Next, we needed to dive deep to learn as much as possible about the specific components and processes involved. But here we were again reminded of how poorly modeled and communicated some very basic biological systems can be.
When we found the info we needed (or hoped we found it), we then had to decrypt it into our own diagrams and symbols on sketchpads. Nucleus-to-plastid and plastid-to-plastid communication. Iridoplast formation. Extraction, preservation, modification, and transplant protocols.
Why so much reading, and so little seeing and interacting? How might research be pulled together and communicated in concise, intuitive ways?
Step 4: Generate hypotheses.
By their very nature, hypotheses are defined by uncertainty and hunches. When it came time to write our own, we learned how important context and assumptions are, yet how poorly acknowledged they are in science.
Most hypotheses are built on a foundation of primary research that others have done — but what assumptions were made in that primary research itself that led to the results? What was the current of thought at the time? Are the underlying assumptions still relevant?
Admittedly, this problem is a bit more ominous, almost a byproduct of the evolution of knowledge itself. And most of the time it comes down to the year a particular journal article was published, and the background of the author.
Step 5: Map out the experiment tree.
After the previous step, things become fairly straightforward. Now that we have an understanding of what we’re hoping to try out, it needs to be put down as a plan.
Flowcharts and whiteboards can suffice for this, but what if the protocols, methods, research, and supplies are all linked to each node in the experiment tree? Managing all of these things is an ugly, time consuming process and can be made better quite easily.
Steps 6–10: Experimentation.
Here’s where the rubber hits the road, and you better hope you’re working with someone who knows what they’re doing.
Two big lessons stands out here: Logistics are a nightmare, and few things go according to plan.
Along the way, you discover that more investigation is needed. Stock solutions, dishes, and sensitive resources managed, labeled, dated, and kept fresh on a weekly basis. Authors need to be contacted. Lab notes taken and tracked. Your car breaks down while transporting sensitive material. You order the wrong materials, or they don’t arrive on time.
Most of the takeaways from above apply within here as well, so maybe we’ll break these steps out in the future.
The Path Forward
This isn’t a new problem, but it’s one that’s growing in importance. Some brilliant minds have already put thought into some key questions:
- What are the most pressing needs for biotech and science communication?
- What languages lie between cybernetics and math/chemistry?
- How can people acquire intuition?
- How can we account for cognitive shortcomings?
- How can interactivity enable deep understanding?
- What can we learn from pedagogy?
My hope is to carve out a path by further developing these questions. Along this path will lie a collection of new tools and systems for citizens, scientists, and everyone in between to cope with the growing tidal wave of information and possibilities.
Finally, I hope I’m not alone here — more people should join in creating a world where complexity literacy is part of our collective fabric of thought.
Acknowledgements
A big thanks goes out to: Nathan Kendrick and Chuck Moore for bouncing ideas around and guiding me on this journey. To Emily Auchard for helping me refine my thoughts and words. To Audrey Crane for introducing me to cybernetics. To all of my colleagues at DesignMap for continually teaching me how to make sense of complexity, and for sharing in this cause. To Marc Beban and Patrik D’Haeseleer for being mentors in the lab. To Daniel Poynter for getting my brain churning years ago with ideas on Digital Literacy.
