The Pursuit of Reproducibility/Predictability
A poet is, after all, a sort of scientist, but engaged in a qualitative science in which nothing is measurable. He lives with data that cannot be numbered, and his experiments can be done only once. The information in a poem is, by definition, not reproducible. … He becomes an equivalent of scientist, in the act of examining and sorting the things popping in [to his head], finding the marks of remote similarity, points of distant relationship, tiny irregularities that indicate that this one is really the same as that one over there only more important. Gauging the fit, he can meticulously place pieces of the universe together, in geometric configurations that are as beautiful and balanced as crystals.
— Lewis Thomas
The Medusa and the Snail: More Notes of a Biology Watcher (1974, 1995), 107.
I think we, as humans, take reproducibility for granted. Egregiously for granted. Our live’s hinge on the fact that certain things must be reproducible. Health, sleep, travel, the quality of the music in my headphones right now, everything. Our expectation of reproducibility in life is like a hidden undercurrent, pushing life predictably forward. I should say here that I am not here to argue about the correct balance of reproducibility versus creativity (even though you could argue that artists/entrepeneurs/thinkers are reproducible in their creativity). It is simply worth noting, that when you think about all of the aspects of life that are predictable/reproducible, it is pretty astonishing. It may be the holidays, but I for one, am grateful.
In Venture Capital, we strive for reproducibility — in entrepreneurs, business models, returns across fund sizes and vintages, etc… In fact, as venture capitalists, we actively seek industries that are exerting predictability/reproducibility on markets and experiences. In this way, it makes sense to look at areas where tools are enabling us to build reproducible systems, and actively seek to deploy capital there.
It is worth while for a very brief divergence here. Reporducibility and predictability of systems generally relys on the principle of accumulation of data (observation and measurement) followed by the understanding of that data. I do not need to go into a long diatribe here about the value of the generation of proprietary data here, but you can quickly see where, if we simply follow a basic framework of the evolution of engineering reproducibility, data plays the central role. In the end, the understanding of data makes it easier to predict and reproduce, opening up verticals to the discipline of engineering, where those principles are applied.
At KdT Ventures, we invest in companies generating and/or using next generation data (think sequencing for example) and analysis tool-sets (think ML/AI for example) to make science, and therefore the entire physical layer (chemicals, agriculture, and medicine) reproducible and therefore engineer-able. The pursuit of standardization and reliability in biology has achieved, in recent years, a number of advances in the design of more predictable genetic parts for a variety of applications (biological circuits, gene editing systems, diagnostics, etc…). These applications are truly changing (read: attacking) the status quo, whether it be petroleum based chemicals, drought, or cancer. They are even creating new markets, just as we have seen in pure technology…
However, even with the development of high-throughput screening methods and whole-cell models, it is still not possible to predict 100% reliably how a synthetic genetic construct interacts with all cellular endogenous systems. Having the digital read out of a sequence of DNA is not enough to understand the physiologic/cellular effects of perturbing it. We are moving faster than ever before, and understand more than ever before, but I do not want folks to take away from any of my writing that we are at an absolute inflection point where anything is possible in biology. But we sure are getting closer, fast…
On a more happy ending, it is awesome that biology can do all of this on its own, with no human intervention, and we are just now starting to understand it… Thankful to be involved. Let’s talk if you are too!