[jdeaton]

> people unacquainted with biology have a false perception of how low-throughput biology experimentation is. In many ways, it can be. But the underlying physics of microbiology lends itself very well to experiments that could allow one to collect tens-of-thousands, if not millions, of measurements in a singular experiment. It just needs to be cleverly set up.

I think this passage gets to the fundamental rift of disagreement in perspective between those focused purely on computational advances versus innovating in wet lab techniques.

Why? Because years of peoples' careers have been wasted waiting on promises from molecular biologists claiming they will make these "clever" high-throughput experiments work. In my experience, they'll spend months to years concocting a Rube Goldberg machine of chained molecular biology steps, each of which has (at best) a 90% success rate. You don't have to chain many of these together before your "clever" setup has a ~0% probability of successfully gathering data.

[bartread]

You have just very eloquently expressed why I left a career in biochemistry behind after undergrad. Realistically I had no business doing that degree in the first place: I simply don't have the patience for the lab work grind.

[dexwiz]

Same reason I left Chemistry. Gotta grind for 80 hours a week for a decade for a chance at a good position. And hope you don’t get stuck under a PI that steals all the credit.

[esel2k]

Seems like I am not alone - I still tried for 4 years to make it work in pharma but realised that spending 3 days to grow primary cells to just get irreproducible results was not going go be satisfying.

[throwawaymaths]

The old joke by Sydney Brenner: "low input, high throughput, no output" science.

[theGnuMe]

NGS is the counter example here. We will also have single cell proteomics in the near future. Robots can do lots of things like vary the initial conditions across a spectrum of variables... something no grad student would want to do.