If you have strong CS skills then you should:
1) Focus on bioinformatics. You will immediately be of use as far as making your own product/service or working for a startup if you apply your skills there. Most bio specialists are incredibly weak at data analysis and/or any type of computing. Pretty much all the important problems in bio are computational in nature. The "impressive" bio researchers/scientists have the data science skills of a sub-par / average data scientist / CS grad.
2) Create a home lab or find one / start one locally. Look up the odin project. Work on DIY genetic engineering and you can even take classes from that site. If you just get to this point and stop you will literally have more practical skill and knowledge than the vast majority of graduates with bio degrees.
3) Lots of biohackers experiment with themselves for clout/hype/attention. It never ends well. There are plenty of lab organisms that you can easily source and ethically experiment with.
4) Don't listen to anyone that tells you that you can't do something because you don't have a PhD. Those are the same type of people that missed out on the computing and internet revolutions because they were busy doing trivial academic work.
Get the notion that "biology is a computer that we can fundamentally and totally understand at the level that we understand Church-Turing" out of your head as soon as possible because it is incorrect. Biological systems are complex, they are deeply nonlinear, we do not even come close to understanding the functional behavior of their components (or, indeed, what those components even ARE) the way we can understand transistors or chips or API specs, etc.
The sooner you get used to believing that "I can't prove anything, but we have a pile of mostly not contradictory evidence that suggests that most of the time this idea is a pretty good heuristic and our error bars are reasonable", the better you'll do and the saner you'll remain.
Some recommended worldview reading for you:
The Andy Grove Fallacy: http://blogs.sciencemag.org/pipeline/archives/2007/11/06/and...
Can A Biologist Fix A Radio: https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.p...
Can A Neuroscientist Understand A Microprocessor https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...
And if you like, I can provide a basically endless stream of papers of the form "we thought X did Y and we knew what X was; it turns out that X actually does Q, it also turns out we don't know why X does anything at all, but when we do X we sometimes get Y so we've been confused for the past 50 years"
In all seriousness, you are going to need a PhD if you want to truly understand all of the background of the field. Human biology/biochemistry is just about the most complex thing humans have ever studied. It would surely be easier to just find people with the requisite skill sets.
Even if you just want to start a company, I feel that it would be really hard to pick out a scientific direction/what your company is going to do, without a rigorous scientific background
[0] https://www.ncbi.nlm.nih.gov/pubmed/23287718
[1] https://www.amazon.com/Molecular-Biology-Cell-Bruce-Alberts/...
When a new biotech product team is getting formed get yourself on the project.
Whatever you do, don’t compete with biologists.... there’s just too many of them and the lab skills are valuable but in abundant supply.
I know quite a few bio/chem people working in pharma as well. That industry is booming but mostly for the people that own pharmaceutical companies not their employees. There's a reason that most biologists from good school would rather be bad data scientist that good lab workers in pharma/bio tech. The pay is much worse and pharma/bio tech don't treat there employees nearly as well as big tech companies do.
All of the interesting jobs around that space are still largely tech jobs.
here's my advice: unless you have friends who can set you up with the right VCs and ensure that they will be willing to overlook your lack of experience and IP, don't bother.
you're not going to get up to speed working in the lab on your own in any short amount of time. learning the theoretical stuff that you need to know won't take long, but you probably won't understand how to use the theory to make something novel until you've spent time in the lab. and you won't know how to vet the ideas of people with phds, either.
then there's the elephant in the room: risk. biotech is extremely risky because drug development is difficult even under ideal conditions. making "niche drugs" is even more difficult than making drugs for the mainstream because niche diseases won't have as much of the scientific background already researched when you sit down to try to come up with a useful therapy concept.
if you want to talk in more depth about the skills which are actually needed to get into the field in a scientific or a business capacity, i've advised someone who reached out to me here on HN about that exact topic in the past, and i'm more than happy to discuss it with you via email. check out my profile if you're interested.
If you are really serious about starting a biotech company, you will need to get a PhD in an applied or natural science or work at an immature startup with a team of PhDs thats needs programming help and you can hopefully learn the 'scientific' skills you need along the way. If you restrict the space of companies you want to start to be strictly in the bioinformatics space, then you can probably bypass the PhD route, but you'll need to get a job doing that sort of work at a startup. Job descriptions for those roles should tell you what specific skills you would need for that. Also, I would highly recommend becoming good at stats. Thats fundamental to any path you go down, and can be another way for you to provide immediate value to others.
I must warn you about a survivorship bias. All the exciting news you are hearing are the tip of a giant iceberg that rests on the mounts of a most dull and repetitive labor.
Here are some realities that nobody tells you:
1. There are only 3 operations done in experimental biology: liquid pipetting, opening/closing tubes and moving tubes between machines. 90% of protocols may be reduced to those operations.
2. Just to reproduce an already published work, you need 6 month of 10 hour/day work, performing those 3 operations from point 1. It took 5 years to reproduce just 18 molecular biology papers [https://www.the-scientist.com/news-opinion/effort-to-reprodu...], while 1 out of 4 computer science papers can be reproduced in less than 30 minutes [http://reproducibility.cs.arizona.edu/].
3. It takes days, if not weeks, to perform an experiment and make an observation. That is, if you manage to make a mistake in your experiment at day 2, you will only learn that something went wrong at day 6. Often, it is even impossible to tell what went wrong.
3a. Imagine, that after days of coding, you hit "compile", and the compiler would find a typo you made somewhere in your code, and instead of pointing at the error, it would simply delete all your code, and never tell you what was the reason, so you have to start over writing the code from scratch. This is how day-to-day biology work looks like.
4. CRISPR technology is one of the most heavy on 3 operations from point 1.
5. If you ask biologists about how do they feel about doing experiments, 97 out of 100 will say they hate it the most and curse the day they decided to do biology.
Regarding starting a company, I would first try to talk to as many as possible industry people, trying to understand what they do day to day and what they find most problematic, difficult or annoying to do, and see what you need to do to solve their problem.
In fact, I so much hate the fact, that I spent 15 years studying, then 5 years doing PhD, just so I can spent the rest of my days pipetting liquid. I decided to try to automate away all manual work by creating a universal robotic framework. If you are interested, please check https://cartesianrobotics.xyz/
We are hiring in SF and Singapore for informatics roles.
So bring it on I say! Just apply to any mayor classical bio-company. I mean biologists need to become data-scientists and computer scientists more and more, and they can use a lot of help. For example: During my internship in 2003 I did DNA sequencing, I spend all day making a gel and loading it and I read 200 basepairs of DNA of a printed paper to check my results. Today we have an Illumina sequencer in the lab, it produces 60 billion basepairs every couple of days. We are nowhere anymore without computers and computer scientists.
Alternatively there’s a lot of algorithm/classifier development you could jump into.
It’s usually easier going the other way though. Biology is a complex beast.
Biology is a vast field, and there is simply an enormous amount to learn. And you also need to understand the methods and techniques used to perform experiments, which can get pretty deep into physics at times, and requires a lot of very specialized knowledge for many of the more complex methods. Statistics is also important for many types of experiments.
It's really not easy and fast to gain the necessary knowledge, people in the field generally have a bachelor/master and a PhD, and that is the start of your scientific career.
The utility, in my opinion, of a phd is that you had 4+ years where you were immersed in this environment and hopefully everything sunk in.
Biology is also not as black and white, cause and effect, strictly mechanistic as CS/engineering. Always think about exceptions and grey areas.
So don’t compete with bio phds, the money is not worth it. I have studied molec bio and comp science separately and can work in both industries. We talk about 150k vs 100k salary differences. Find an area that interests you like data analytics software in healthcare or something like this and go to a tech company you will thank me.
https://www.amazon.com/Gene-Intimate-History-Siddhartha-Mukh...
Not all of the places on that list will have biology labs, but some will! It's a good way to get some experience at the bench without going the degree route.
Add some evolution and phylogenetic, which with a mathematical background should be straightforward to grasp.
1. An over abundance of Bio Phd's. 2. Medicine in particular is tricky 3. Academics can be a$$Holes 4. They don't pay as well.
Work in the industry for a few years.
To be honest, the scientists just need big data + data science + workflows/ pipelines.
You will need two basic skills
1) The skill to understand that trying to manipulate by yourself a delicate and exquisitely calibrated machine without knowing what you do, is a bad idea.
There is a fair possibility that you end with either bad products like a method for pursuing suspects based in DNA of a chunk of cut hair (made entirely of cheratin that does not have any DNA). Incorrect biological explanations or a very expensive machine broken are also probable results
2) The skill to hire a trained biologist that will do the job
The other comments are very good, so I'll add things I didn't see mentioned:
* In my opinion, you have to get beyond the wow factor and figure out what you want to work on and why, rather than the how (e.g. CRISPR). I spent half a year researching this and my result was an interest in heart disease [1].
* Credentialism in biology is high, as mentioned. I'm currently in an M.S. in General Biology program. I was able to get my work to shift me to part time (20 hours/week) and I found a non-thesis M.S. program which is geared towards part time professionals (a rare program format) which is perfect for me. However, another comment mentioned biochemistry rather than general biology and there is some merit to that point, although I've appreciated the broader perspective of a general program.
* If you take the Biology credential path of M.S. or PhD, unless you go the Bioinformatics route, you'll likely need to take the GRE Biology specialty test, which was quite difficult for me with almost no biology background. It took me about 6 months to teach myself [2]. I liked the textbook "Campbell Biology" by Urry et al. as well as others, although I find the lack of citations in textbooks really annoying for the way I learn (following rabbit holes).
* Personally, I'm trying to avoid the bioinformatics route because I don't want to just be a tool of some other scientist, but I want to be a scientist myself. This is a hard path and there is some merit to others' comments about joining a biotech startup as a programmer and then transitioning to deeper biology.
* In my networking, I see a lot of people going the other way: from biology to computer/data science. Anecdotally, this seems to be largely due to points others raised: lower salary, a glut of PhDs, slow growth and conservatism of the field due to regulation, etc.
I don't know where I'll end up exactly, but I've thoroughly enjoyed the ride and I think you should scratch your itch if you can. Feel free to email me (email in profile) with questions.
[1] https://github.com/freeradical13/ValueBasedPrioritization/ra...
Having a PhD is not essential -- I know many successful VCs, founders and operators without PhDs -- but you absolutely need an appreciation of science. People like to hire PhDs because they have a fundamental knowledge of biology and or chemistry and they have deep experimental experience in a specific domain. Without that experience doing experiments it's hard to really understand the challenges of science and rigor required. That said not all PhDs give you that, and academic science tends to be less rigorous than industry science
Also, developing a drug requires PhD level experience in many domains and no one person can do it all. A neuroscience PhD won't necessarily qualify you to review tox data. You need people who can quickly get up to speed on various technical fields, find experts and have productive conversations with them
You need to understand the drug development process at a basic level. There are lots of articles about this online, here's one I wrote [0]
You need to understand how to evaluate and critique scientific and clinical data. It is easier to start by looking at clinical data. You can learn to analyze this data without a PhD if you spend time with it and ideally have a mentor. Here is a case study I wrote on basic concepts in evaluating clinical data [1]
Evaluating preclinical or scientific data is much harder. In nonhuman studies you are measuring more endpoints with less direct relationship to human disease in more contrived systems. The experiments also tend to be less rigorously designed, executed and documented (at least in academia) so there are all kinds of pitfalls to avoid that you can't really know without experience. This is where a PhD really helps
To start learning this just struggle through papers. Find a paper that interests you and read it in depth. Learn what each experiment and instrument does. Learn what each molecule does. Here's an example I wrote translating a synthetic biology paper into layman studies terms to give you a sense of what goes into reading a paper [2]
If you have a scientist friend who can walk you through papers that speeds up the process by orders of magnitude and you can learn so much
At first read for comprehension. Then read with a critical eye. Why did they do this experiment instead of this other one? Are they missing an important control here? Is this model robust? Are the conclusions they draw stronger that what the data suggest?
Learn how drugs are valued. Valuation is different in biotech than any other sector. Drugs don't have revenue or users for years. A drug can. A worth $10B before FDA approval. Value in biopharma comes from reducing technical risk. I wrote a post about that here [3]. Most biotech founders lack either the science or business knowledge needed. It is easier to learn the business stuff so that can let you add value to a company while learning more about the science
[0] https://www.baybridgebio.com/blog/drug_dev_process.html
[1] https://www.baybridgebio.com/blog/aducanumab-analysis
[2] https://www.baybridgebio.com/blog/synbio-laymans-terms.html