Elon Musk on How to Build the Future

How would one go about meaningfully contributing to solving problems in genetics without having done the work leading to a MD or PhD (or both)?

I'm not sure you would. I mean, I'm sure you could somehow, but at this point so much of what needs to be done is basic research, and really can be done well in that context. There aren't many things that are ready to leave the context of a research lab and into commercialization. We've got some notable disasters with Theranos, and even the YC funded Taxa (glowing plant - that was a farce from the get-go, but they're doing some potentially interesting stuff now).

As far as education, it's not something you can learn by yourself, it just isn't. Most of the methods in a biological wet lab are very far from standardized and need a great deal of troubleshooting. Most post-docs in a new lab spend a couple months just trying to get basic stuff working that they've done dozens of times before. It's hard. You need people around you with experience and perspective, and doctorate programs are likely the only place you're going to get that kind of training.

I think there are a lot of people that want to approach biology with a CS mindset, especially the people interested in synthetic biology, but that rarely bears fruit. It could get to that place eventually, but there's a lot of ground to cover. In that sense I agree with Elon that, despite the huge impact genetic engineering could have, it's not the next thing because we're not ready yet. There's still too much that's fundamental to biological problems that we simply don't understand, and solving things in one species usually doesn't translate very far across taxa.

I'm not sure if you would consider this meaningful, but if you have software development skills, either developing applications that can be used to solve problems in genetics, or that save the time of those working on solving those problems.

This is why Theranos was such an effective scam: the current culture of "innovation" is so heavily based in software, an unconstrained space where a creative wunderkind can make great advances, it thinks all problems can be solved through sheer thinking outside the box, "disruption," and dreaming big. Those are all good things to try, but I don't think it's a coincidence Silicon Valley-based big-dreaming startups aren't doing nearly as well as big, boring research labs with heavy understanding of the science and measured goals.

Consider computational biology. There are lots of problems which hinge on understanding the impact of genetics on populations and variations in genetics and that effect. As there are already sources of genetic data sets and infrastructure to generate those data sets, genetic research becomes more of a data science problem than a medical problem.

You probably couldn't. But if you refer to two things Musk said - a) genetics is important, and b) PhD is not the best way to be useful - I think he didn't mean it to be taken together. He spent some time talking about how being useful means "area under the curve" - do a big thing for small number of people, or do a small thing for a large number of people. Most people can aim for either of the two, and in both cases PhD is probably not the most efficient use of your time.

This is the part I somewhat disagree. I've seen lots of strong computational biologists make the leap into generic data science, but I've seen way too many CS/data science types struggle. They take the data at face value, not recognizing the fact that biological data has flaws. A sound understanding of biology/chemistry helps a lot with identifying those flaws and generally with designing experiments/research.

Admittedly that's a bit of a generalization and I am sure there are a decent number of exceptions but consistent with my experience.

Genetics startups still need engineers and product people.

ex. Counsyl (https://www.counsyl.com)

> Most of the methods in a biological wet lab are very far from standardized and need a great deal of troubleshooting. Most post-docs in a new lab spend a couple months just trying to get basic stuff working that they've done dozens of times before.

Having had experience with syn bio in grad school and trying to reconcile the empirical (biology) and first principles (CS/math) approaches, I've been thinking a lot lately about how to streamline the troubleshooting process for picking up and optimizing wet lab methods. I'd love to chat - my email's in my profile.

>leave the context of a research lab and into commercialization. We've got some notable disasters with Theranos

Did that spring from a research lab or from a happyhour with mba types wanting to jump on "start-up" fortunes

Genetic research uses computational techniques today. However, most academics who understand genetics well are crappy programmers. My source for this is a friend who is a tenure-track professor of evolutionary biology at a major university, with publications based on computational analyses of genomes. In pulling those publications together, he inevitably had to spend a lot of time time reviewing and cleaning up the terrible code of his co-authors, checking for correctness. "And I'm not even good at coding," he said. "That's how bad this stuff was!"

So, I think there must be a role for strong developers to partner with strong genetic researchers to make the best use of computers for research. That role might not exist now--you might have the opportunity to go create it. But it does seem sorely needed.

The easiest way that probably anyone on HN (who can fizzbuzz) can help is with data management. So much stuff is still done by hand that could be easily scripted.

Researchers in our institute were amazed how easy it is to use e.g. google forms to gather data in a reasonable format. Once you get data in a reasonable format you can help them with transforming it/joining it with other sources/cleaning them up. ETLs and data integration are often completely foreign concepts to them.

And that's researchers, you might still start calling them quite computer-competent after you talk to the people in the clinic. All the research is for nothing if it's not brought to "bedside" to benefit the patients in a clinical setting, outside all trials. For that you need to make sure genomics pipelines are automated and reproducible and only clinically relevant information gets to the oncologists (or other doctors) deciding on treatment. This is still not quite there even in the best places.

I think most of the really world-changing stuff will just be hard work on relatively easy problems. It's hard to get excited about these (compared to the latest neural networks or distributed high performance systems) but they need to get done

You can definitely contribute. If you're a software engineer, then you could join a research lab as a scientific programmer. Good labs are well-funded in these areas and will have funding to cover salary for a programmer to implement data analysis pipelines, polish research software and make it publicly available etc.

Alternatively if you're a software engineer or a product designer, or many other roles, then you could join a company working on commercializing genetic medicine. They're are lots and those companies are definitely not just looking for people with PhDs.

Once in a place like that, you'd be able to chat further with people about your career direction.

So, I can't answer about solving big problems, but I did genetic engineering research in grad school on bacteria. One could very easily conduct serious genetic engineering in one's bedroom for less that $500 or so. Of course this is fairly basic stuff, but still, you'd be amazed what is possible with very little equipment.

For example, there is a yearly competition called iGEM, which is synthetic biology competition for undergrads. Some of the stuff they do with limited resources is quite impressive.

http://igem.org/Main_Page

Moreover.. Musk said he didn't anticipate being involved in all 5 things he thought about in college, including genetics. What is he working on that's genetics related?? Did he just misspeak?

That is mostly because those "types" as you call them didn't take math courses or slept through them, or don't use the math tools in everyday work - because it's not needed.

In other words, they're not Computer Scientists. They are Computer Programmers instead. (or maybe Computer System Engineers)

I believe that the comment you are responding to was speaking to the asymmetry that people with experience in computational biology have an easier time moving to general data science problems than do people with experience in general data science working on computational biology problems.

I agree that the asymmetry exists: there is a tremendous baseline of scientific knowledge and experience that is needed to make significant contributions to the field. I personally have worked with people with backgrounds in programming or CS on medical problems, and it has been frustrating because they lack what I would term "scientific common sense". I would personally prefer, and would be able to make more progress with, working with (for example) anyone who has completed a sequence of education sufficient for pre-med requirements and has some programming experience over a "full stack data engineer". Even if someone with a programming or CS background were inclined to pick up the textbooks and amass the baseline scientific knowledge (I'm sure they exist, although I haven't met them yet), they'd still lack the years of laboratory work and experience of applying this knowledge.

My original comment was apparently poorly worded because it was interpreted by the responders differently than I intended, but delightfully, it resulted in very thoughtful comments. I am very skeptical that one can make even small contributions to genetics without the experience of years of specialized work. There are ancillary problems that could be done by someone with a programming or CS background, e.g., a better LIMS system, or perhaps protocol management, but I don't see those tasks as leading to later making meaningful contributions to the field of genetics. The MD or PhD isn't required, but all the work done leading up to it is, and so as I see it those prepared to make the contributions are most likely going to have gotten the degree on the way.

Indeed, the main problem in genetics are not related to handling data, but require major experimentation, even at cellular level, not to mention higher ones.

Not much CS can help with right now - the most useful tools (mass fuzzy searches and molecular simulations) are already there.