Many synthetic biologists cite one of Richard Feynman's many famous quotations as the inspiration for their work: "What I cannot create I do not understand." For synthetic biology the interpretation is clear--only by designing and building living systems will we truly understand the principles underlying the functions of living cells.
This connection between knowing and making that defines much of synthetic biology research today is, however, a much older idea, rooted in the philosophy of the enlightenment and centuries of western science. The 18th century philosopher Giambattista Vico said "Verum et factum convertuntur" or "The true and the made are convertible." Immanuel Kant discussed similar sentiments in 1787 in his Critique of Pure Reason, "Reason has insight only into what it itself produces according to its own plan."
During the early days of computer science, this concept was given a computational flavor by John von Neumann--"If you can't compute it you don't understand it!", an interesting twist considering how many synthetic biologists think of DNA as the "software" of the cell that synthetic biologists can "program" to behave how we want.
While none of the other quotations have the same quotability and staying power of Feynman's, they are all interesting for historically grounding the concepts of synthetic biology and the creative aspects of the scientific process, critical to much of the science and philosophy of the past three hundred years.
great stuff. Really been enjoying the blog.
I really have no idea why your blog isn't more popular.
I like these quotes, and agree with them pretty much. However one can understand something without being able to influence its course. We understand (pretty much) the fluid and energy flows that cause and influence the weather, and can write down the equation that describe what is going on. Those equations can't be solved, but that is not because of a lack of understanding, but because of the computational difficulty.
I particularly like the von Neumann quote which brings in computability. It might be useful to consider the history of artificial intelligence, particularly what is known as the AI winter, when funding for AI was withdrawn because the hype of AI claims didn't match what AI developers were able to achieve.
Synthetic biology will likely face similar problems and for similar reasons. Physiology is (I think) a lot more complicated than is intelligence. Physiology has already produced intelligence, intelligence must be simpler than physiology.
Physiology is comprised of at least thousands of non-linear coupled parameters. Such systems are inherently chaotic and are inherently non-predictable over the long term. That chaos is why the weather is not predictable long term. The same chaotic-type effects are important in physiology too.
Great quotes and post.
As I (an engineer) learn more about this fascinating field of synthetic biology I am also beginning to appreciate more and more the differences in how most scientists think vs. engineers.
"What I cannot create I do not understand"
A goal is implicit in this quote: knowledge. This is, of course, the goal of all science that makes our world possible and life worth living.
But engineers look at the world differently, and our goal is "to solve the problem." Hence the continuous investment in software abstraction layers that allow us to work more efficiently (solve more problems) while diminishing our knowledge of the underlaying systems that drive it.
As this exciting field of synthetic biology develops it will be interesting to see the human interface between inter-disciplinary teams.