[nobodyknowsyoda]

I’m probably preaching to the choir, but yup that inherent uncertainty seems to distinguish biology from the other sciences. There is a huge stochasticity & serendipity in everything because there is no intentionality in the design of any biological component; any convergence toward a chemically or physically optimized component or behavior is driven by evolution by natural selection but remains imperfect. Warts, quirks, and all

It also explains how the folks on HN positing “Covid is just the flu” and “long Covid isn’t real” have been so confident yet so gravely mistaken. They are used to other sciences where there is much less room for uncertainty

[hprotagonist]

Yep. For better or worse i've been up to my eyeballs in "hey, you know, biological systems are actually insane!" since high school, and now through a few advanced degrees :)

I've been working in tech more than normal for a few years now, and the endemic nature of "the andy grove fallacy" among my coworkers has ceased to startle me, but it's just kind of bothersome every time.

https://blogs.sciencemag.org/pipeline/archives/2007/11/06/an...

[nitrogen]

Despite the common trope that software/hardware engineers think they understand everything, automation can make exponential development possible in bio. I know someone who works at a biotech company, and the stories I hear lead me to believe that traditional thinking in e.g. medicine and insurance, and from colleagues, really is a bottleneck. Maybe exponential growth will never apply to the number of diseases cured, but it can and should apply to the hardware and software that facilitates the next iteration of biological discovery.

[hprotagonist]

It's not that software/hardware engineers think they understand everything, it's that the absolutely common idea "my way of thinking about understanding things is appropriate in this other domain" breaks down very fast.

I'm not speaking about running hospitals or dancing with insurance companies. I'm speaking about much earlier in the pipeline: fundamental, blue-sky biological research is fundamentally different from software. The reason is that we are not studying designed systems; the effect is that there is so much that we don't know that we don't know that things that sound easy are in fact basically impossible because the prior knowledge is simply not established. (Until they aren't. When does that change? First slowly, then all at once.)

As a biomedical engineer, i'm on board with hardware and software improvements: it's kind of my job. The trick is knowing what you're doing, what you aren't, and what you can expect, and to balance confidence in the value of what you _can_ tightly constrain and design versus humility in accepting that the natural world simply doesn't care.

[natechols]

I think what bugs me the most is the common assumption that we just need better modeling tools to make a lot of the messy lab work go away. Everyone who raises this idea seems to think it's original and revolutionary and a no-brainer, but there's at least 40 years of bitter experience in biotech and pharma development saying otherwise. Even genuinely impressive feats like AlphaFold get inflated wildly in importance (and used to retroactively bash experimentalists for all the time they wasted by not listening to software people). Actually speeding up the entire process of biomedical research requires improvements across the board in many different fields, and it's not something that is magically going to be solved by computer science wizardry alone.