NEXT: The Future of (Bio)Coding
We wait with impatience for the widespread vaccination of the population. The news about COVID-19 has lately been focused on the relatively rapid speed with which the vaccines were produced. In what is usually a years-long process, the Pfizer BioNTech and Moderna vaccines are now being injected—albeit slowly—in American arms.
What has been notable about the rollout of these vaccines is the unusual speed with which they have been generated. Before now, producing a vaccine meant isolating a small part of the virus, then injecting this into the body, which would trigger the immune system to develop a resistance to this and any other attack by the virus.
In the case of the BioNTech vaccine, especially, rather than being a weaker or deadened form of the virus, it is instead “manufactured,” coded to produce the same effect: to instigate the body’s immune system to this synthetic ersatz virus. In effect, the vaccine is genetic code; genetic “instructions” are used rather than an actual virus, these instructions compelling the body to produce an immunity as if it were attacking a “real” virus. The DNA in our cells translate the code of their genes onto RNA, which then leave their cells to help carry out the construction of proteins. Because they carry out the coded instructions of the DNA, these are referred to as mRNA (messenger RNA).
In the case of the BioNTech vaccine, mRNA was manipulated by the researchers. I am struck that the reporting of this development makes reference to “mRNA technology.” This continues with a theme I have written about previously, that in the future “technology” is as likely to mean something alive and squishy as it is a digital phone app.
And now the “source code” for the BioNTech vaccine has been made publicly available. One wonders if future engineered mRNA code will be available to “biodevelopers” in the same way today’s coders can go to GitHub. Are we soon to see a GitHub for “biocode?”
Within the next ten years, “writing code” will not mean working with R or Python, but instead will mean manipulating the syntax of mRNA and the code of life. If today a coder is a person who writes code for computer programs, then that definition will be expanded to include anyone who manipulates DNA source code—strings of GTACs instead of 1s and 0s—to engineer mRNA. Indeed, by 2030 we may find that when we say “coder,” we are referring to someone who manipulates genetic lines of code.
Like the history of computer coding, biocoding will at first be a highly specialized skill, requiring expert training and an advanced degree. We may find that biology becomes the new major of choice for undergraduates drawn to the promise of lucrative jobs in (bio)technology. Biological engineering then becomes the discipline graduate students flock to. Indeed, we could see a new “arms race,” where countries compete for the development of biocoders. In such a scenario, biocoding becomes a geostrategic imperative, and Congressional debates rage over whether federal funds should be lavished on universities, earmarked for training those who manipulate genetic code. In the same way that data security has become a necessity, “biosecurity” emerges as an ancillary field, training specialists who monitor and thwart those who would hack biological systems.
But over time, like computer coding today, biocoding will become a more commonplace skill set. In the way that we have computer coding boot camps, to instruct people to write code for apps, we may very well see the emergence of biocoding academies. These providers will promise that students will learn how to manipulate mRNA in 20 weeks of training, and have a biotech job waiting for them at the conclusion of the program. The expansion of university-level biology and bioengineering programs—which would have bloomed in the 2020s—start to contract by the late-2030s as students discover they can receive entry-level positions in the thriving biotech industry with a much smaller investment of time and tuition.
At one time—like when I was in high school, some time ago—the thought of there being computers in the classroom, or that students would need to learn to write code for computers seemed preposterous. Similarly, as it is judged to be a more accessible skill and a career necessity, we should come to expect that high schoolers, and perhaps even younger students, will be taught how to biocode, as preposterous as that sounds right now. The effectiveness of such biological education will become central to policy debates, as countries begin to measure their educational effectiveness on whether or not their young people are learning to biocode. Champions will call it “the most important mid-21st century skill.” Alarmists will decry that we are “losing to China,” where it will be observed that our geostrategic rival teaches their young people gene sequencing in middle school.
Eventually, say twenty years from now, biocoding will probably be a relatively low-paying position, the 21st century equivalent of the typing pool. And it is very possible that it will become automated, as artificial intelligence takes over, not unlike the way computer coding is becoming automated today.