Ask HN: Getting started in biology with a software background
In the past few years the news around biology has been getting more exciting and frequent, between CRISPR and biotech firms working on niche drugs. I'm really interested in learning more about the skills needed to start a biotech company, but I'm lacking the masters degree in biology. What skills are actually needed to get into the field and does anyone know any good resources to learn them?
149 comments
I'm curious who all these sarcastic bio "experts" are that are suggesting getting a PhD or hiring one. I'm a former bio major and researcher/scientist that transitioned to software engineering years ago. I've published papers in bio and worked with many PhDs. Many of them were idiots. Being a PhD doesn't mean anything, it's the independent work that you put in yourself (in an academic lab or by yourself) that determines how skilled you become.
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.
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.
Saying that many PhDs you worked with are idiots says more about you, than it does about the PhDs.
PhD process is only one path, but it has a number of useful attributes, such as being very close to the active state of the art research, feedback from experts in the field, handholding through the paper and grant process, and introduction to a large social network. Those are all very hard to do with home labs and biohacking. Things like journal clubs with other grad students often help people learn how to evaluate the literature with the appropriate context. Independent work is important, but teamwork and learning from others is far more important.
I've worked with some very smart people (famous software engineers with long track records of innovation) that wanted to help with bioinformatics, and they did do some cool things, but their lack of deep context (the sort of thing you can get from a PhD program or working in the field for many years) ultimately led to problems such as premature optimization for the wrong distribution of data.
Nonetheless, I have see independents who came to the field with no background, absorbed the ground knowledge, and made major contributions, but that's absurdly rare compared to PhDs.
PhD process is only one path, but it has a number of useful attributes, such as being very close to the active state of the art research, feedback from experts in the field, handholding through the paper and grant process, and introduction to a large social network. Those are all very hard to do with home labs and biohacking. Things like journal clubs with other grad students often help people learn how to evaluate the literature with the appropriate context. Independent work is important, but teamwork and learning from others is far more important.
I've worked with some very smart people (famous software engineers with long track records of innovation) that wanted to help with bioinformatics, and they did do some cool things, but their lack of deep context (the sort of thing you can get from a PhD program or working in the field for many years) ultimately led to problems such as premature optimization for the wrong distribution of data.
Nonetheless, I have see independents who came to the field with no background, absorbed the ground knowledge, and made major contributions, but that's absurdly rare compared to PhDs.
Saying that most PhDs you worked with are idiots
The person you are replying to did not say that.
The person you are replying to did not say that.
But they did say "many of them are idiots", which is similar and rather arrogant. I don't trust people who claim their colleagues and classmates are idiots. Dunning-Kruger effect and hubris being what they are.
But they did say "many of them are idiots", which is similar
Similar, yes. But, at least to my mind, there's a pretty big jump from "many" to "most". But maybe that's just me. shrug
Similar, yes. But, at least to my mind, there's a pretty big jump from "many" to "most". But maybe that's just me. shrug
Mot really, many still sounds like more than 50%. That's a huge claim
Nope, we have a separate word for that: most.
If the performance of those PhDs were extremely subpar to the point of hindering the research altogether, what sentence would you use to describe this situation?
I think that's what they said, slightly paraphrased:
"I've published papers in bio and worked with many PhDs. Many of them were idiots"
I have no interest in arguing about the interpretation of that sentence.
I have no interest in arguing about the interpretation of that sentence.
I have no interest in arguing about the interpretation of that sentence.
Then don't "interpret" anything. The person who wrote that sentence explicitly said "many" and not "most". Barring some evidence to the contrary, the sensible thing to do is take it literally, no interpretation necessary.
Then don't "interpret" anything. The person who wrote that sentence explicitly said "many" and not "most". Barring some evidence to the contrary, the sensible thing to do is take it literally, no interpretation necessary.
I changed the text from "most" to "many" to more closely match. This seems like a fairly pedantic thing to complain about, not really contributing to the substance of my argument.
Thanks! For others who tried to google "The Odin Project", the correct link is https://www.the-odin.com/ . It's confusing because that's also the name of a website for learning web development (https://www.theodinproject.com/).
1) Not enough for starting a biotech startup as OP would like to do. Having a PhD means that you've spent years on a certain subject. You cannot just read a book and be up to speed on biomedical research and CRISPR.
2) Having hands-on experience in wet labs is useful and relatively easy to learn. People can learn wet lab skills sufficient to carry out experiments (i.e. pipetting stuff together) in under a year. This is not what research is about though.
3) True
4) You don't need a PhD. But to truly succeed in biology, you need to learn things from the ground up, which takes years of studying. If you just read a few books, you will be able to understand certain parts of it, but as a founder of a biotech startup, you will be the equivalent of a tech startup founder blindly following buzzwords such as "blockchain".
2) Having hands-on experience in wet labs is useful and relatively easy to learn. People can learn wet lab skills sufficient to carry out experiments (i.e. pipetting stuff together) in under a year. This is not what research is about though.
3) True
4) You don't need a PhD. But to truly succeed in biology, you need to learn things from the ground up, which takes years of studying. If you just read a few books, you will be able to understand certain parts of it, but as a founder of a biotech startup, you will be the equivalent of a tech startup founder blindly following buzzwords such as "blockchain".
Never recommended just "reading a book" - although books are for knowledge transfer. In fact, I recommended the exact opposite. Creating or joining a lab to do DIY genetic engineering is not pipetting. Anyone can easily obtain the materials to do CRISPR and implement various ideas they get from reading the latest research and literature. It is exactly analogous to the computer and internet revolution. All of those founders learned things from the ground up, often outside of academia, while others were completing academic research. The truth is that the same opportunity is now available to biotech and genetic engineering. The odin project, for example (I am not affiliated in any way whatsoever), offers all the materials to build your own home DIY bio engineering lab. That is certainly enough to do the required research and test / validate various research ideas, and ultimately creating a startup.
Just taking a quick look at the DIY stuff, that only seems to cover the initial genetics experiments. The equipment offered on the Odin project sites doesn't really cover what comes after that.
What do you do after you introduced a plasmid or modified the genome of some bacteria?
What do you do after you introduced a plasmid or modified the genome of some bacteria?
There are plenty of ways to get access to and use the advanced equipment that you may need for research after getting past the beginner and intermediate level. This comes to mind: https://www.biolabs.io/
RE: "Having a PhD means that you've spent years on a certain subject. You cannot just read a book and be up to speed on biomedical research and CRISPR."
Isn't that what books are for? To compile, document and share knowledge some people spent years to figure out?
Isn't that what books are for? To compile, document and share knowledge some people spent years to figure out?
There is a huge gap between having knowledge from books and being able to do original research, let alone solving a biomedical niche problem using CRISPR. This gap is usually filled with an advanced degree, where you spend years in the lab, keep track of the latest research in the field, try to find your niche and solve the actual problem.
I mean, by all means, try it. But life sciences are not computer science. The approach is entirely different and quality of the work you need to do is different. It looks much, much easier than it is. (Which is, on a different note, why I believe the whole pseudoscience crap such as anti-vaxxers is gaining so much traction)
I mean, by all means, try it. But life sciences are not computer science. The approach is entirely different and quality of the work you need to do is different. It looks much, much easier than it is. (Which is, on a different note, why I believe the whole pseudoscience crap such as anti-vaxxers is gaining so much traction)
> But life sciences are not computer science
Exact
1) The use of materials is totally different
Computer programming is a stuf that uses reciclable electricity and "eating your own dog food" kind programs, cheap to produce or free to copy, easily available and in many cases free except by the hardware (Hardware that can be hired, "clouded" or increased gradually).
You don't need to pay a dime for using R, C, Perl or Python and you can obtain the four, ready to use in your computer in less than a half hour. If you need a microscope, there is not an equivalent open source stuff replacement available.
In Biology the matherials will be sold to you only in packages of 10 Kg each pigment, with a caducity date, even when you would need to mix just 10 grams of each one. The spendable one-use only stuff is not free and the price for a single kit is incredible. You will pay it in any case because is indispensable for validating your work and you plan to use 500 of those kits the next year so you are a captive client from this company (that could decide to stop selling you if you try a way to lower the price, and will sue you if you try to copy the formula and make the product by yourself).
2) Documentation is not free and obtaining it is time consuming
In computer programming, you dont need to spent weeks to be delivered to you, or plan a travel to Peru to collect samples of a plant virus. You don't need to spend days just to reach the documentation navigating a miriad of closed or pay per viewed journals, at 50 dollars to peek in each paper.
You can expect to learn something and use it for years. Fortran is still there. You can program automatically your computer to make a hundred of safety copies each working day. In biology you can not clone your amazonian beetles collection, is unique and will be atacked and reduced to dust by real bugs from day one if you do not protect it.
3) There is not an obvious, linear path to success
Errors are random events that you can't always control
In biology you will lose eight months of your research because your samples travelling from Swedden to Madrid end somehow stuck in a Lithuanian airport for ten days and now are defrozen and unusable. You needed this research to assure the new funds and keep running, and now you have three months to obtain new samples, redo and fix it.
You can lose years chasing a dead end or be superseeded by a genius in some part of the planet that discovered the same as you first, or a different and better way to do it.
4) You don't just buy a lab and hire a team to create "something" nice.
Unless you are in the bussiness of teaching science you design the lab according of the exact product what you are trying to create. Any machine that you ordered and will not use enough frequently later is a hole in your presupuest. Any timed out kitt that you bought in excess quantity is your money ending in the dumpster bin.
You need to hire somebody familiar with what "hardware" is trendy and works and what machines are outdated since ten years even if you see it in each faculty and in propaganda.
Exact
1) The use of materials is totally different
Computer programming is a stuf that uses reciclable electricity and "eating your own dog food" kind programs, cheap to produce or free to copy, easily available and in many cases free except by the hardware (Hardware that can be hired, "clouded" or increased gradually).
You don't need to pay a dime for using R, C, Perl or Python and you can obtain the four, ready to use in your computer in less than a half hour. If you need a microscope, there is not an equivalent open source stuff replacement available.
In Biology the matherials will be sold to you only in packages of 10 Kg each pigment, with a caducity date, even when you would need to mix just 10 grams of each one. The spendable one-use only stuff is not free and the price for a single kit is incredible. You will pay it in any case because is indispensable for validating your work and you plan to use 500 of those kits the next year so you are a captive client from this company (that could decide to stop selling you if you try a way to lower the price, and will sue you if you try to copy the formula and make the product by yourself).
2) Documentation is not free and obtaining it is time consuming
In computer programming, you dont need to spent weeks to be delivered to you, or plan a travel to Peru to collect samples of a plant virus. You don't need to spend days just to reach the documentation navigating a miriad of closed or pay per viewed journals, at 50 dollars to peek in each paper.
You can expect to learn something and use it for years. Fortran is still there. You can program automatically your computer to make a hundred of safety copies each working day. In biology you can not clone your amazonian beetles collection, is unique and will be atacked and reduced to dust by real bugs from day one if you do not protect it.
3) There is not an obvious, linear path to success
Errors are random events that you can't always control
In biology you will lose eight months of your research because your samples travelling from Swedden to Madrid end somehow stuck in a Lithuanian airport for ten days and now are defrozen and unusable. You needed this research to assure the new funds and keep running, and now you have three months to obtain new samples, redo and fix it.
You can lose years chasing a dead end or be superseeded by a genius in some part of the planet that discovered the same as you first, or a different and better way to do it.
4) You don't just buy a lab and hire a team to create "something" nice.
Unless you are in the bussiness of teaching science you design the lab according of the exact product what you are trying to create. Any machine that you ordered and will not use enough frequently later is a hole in your presupuest. Any timed out kitt that you bought in excess quantity is your money ending in the dumpster bin.
You need to hire somebody familiar with what "hardware" is trendy and works and what machines are outdated since ten years even if you see it in each faculty and in propaganda.
Describing the current state of CS and comparing it to the current state of biotech/genetic engineering is like comparing apples to oranges. In the beginning of the computer revolution the obstacles were extremely similar to the current obstacles (or opportunities) with biotech. The materials were expensive, documentation wasn't abundant, people didn't think personal computers would ever be a thing or that people would interact on the internet using social networks, and server/computing costs were extremely capital intensive before cloud computing.
There is a great opportunity at this moment, similar to the opportunity that Bill Gates and Paul Allen had. Mainframe computers were expensive, yet they found ways to practice and become highly skilled. There wasn't documentation like there is now, yet hacking culture found ways to build cool and effective stuff. Obviously there wasn't a linear path to success - people thought personal computers would never be necessary, yet Apple and Microsoft were huge successes that no one ever expected. There are people that see opportunity in fields like biotech and bioengineering, and there are people that only see the obstacles. The former create amazing things, while years later the latter are envious that they didn't have similar vision and courage.
You made my point.
There is a great opportunity at this moment, similar to the opportunity that Bill Gates and Paul Allen had. Mainframe computers were expensive, yet they found ways to practice and become highly skilled. There wasn't documentation like there is now, yet hacking culture found ways to build cool and effective stuff. Obviously there wasn't a linear path to success - people thought personal computers would never be necessary, yet Apple and Microsoft were huge successes that no one ever expected. There are people that see opportunity in fields like biotech and bioengineering, and there are people that only see the obstacles. The former create amazing things, while years later the latter are envious that they didn't have similar vision and courage.
You made my point.
>There is a great opportunity at this moment
I wouldn't advice to invest in the company of this guy, but is their money, not mine, so... do as you please. Honestly, I would love to hear that he became billionaire.
I wouldn't advice to invest in the company of this guy, but is their money, not mine, so... do as you please. Honestly, I would love to hear that he became billionaire.
[deleted]
You cannot just read a book and be up to speed on biomedical research and CRISPR.
FWIW, the OP didn't specifically say ze wanted to do "biomedical". Biotech is bigger than just medical applications. Biotech could skew more towards materials science, or environmental engineering, or any number of areas besides "treatment for diseases in humans" or whatever.
FWIW, the OP didn't specifically say ze wanted to do "biomedical". Biotech is bigger than just medical applications. Biotech could skew more towards materials science, or environmental engineering, or any number of areas besides "treatment for diseases in humans" or whatever.
Even better than a home lab is finding a friend who works in a research lab, and shadowing them while they do experiments.
Safety is an obvious benefit to this approach, but it also allows you get exposure to more interesting and complex experiments that are more cutting edge and comparable to the kinds of work you'd actually do at a company. These labs have access to equipment and reagents that you cant get anywhere else. From what Ive seen the at home stuff you can do is incredibly limited
This approach also gives you exposure to proper experimental design and technique, which is really hard to learn on your own because biology experiments take so long. Biology experiments take long enough even if you know how to do them
Safety is an obvious benefit to this approach, but it also allows you get exposure to more interesting and complex experiments that are more cutting edge and comparable to the kinds of work you'd actually do at a company. These labs have access to equipment and reagents that you cant get anywhere else. From what Ive seen the at home stuff you can do is incredibly limited
This approach also gives you exposure to proper experimental design and technique, which is really hard to learn on your own because biology experiments take so long. Biology experiments take long enough even if you know how to do them
>I'm curious who all these sarcastic bio "experts" are that are suggesting getting a PhD or hiring one
>Don't listen to anyone that tells you that you can't do something because you don't have a PhD.
I definitely don't think getting a PhD automatically makes you some super genius. I've also worked with a fair number of grad students get their PhD that probably shouldn't have just because they'd been in their program long enough and basically got "pushed" out. That said, if OP is coming at the perspective of starting a business and presumably needing to convince investors/clients that he knows what he's doing, it's kind of silly to suggest he doesn't need any PhD's working for him.
Even if whatever job they're doing doesn't really require "PhD-level expertise", hiring a PhD is a fairly easy way to lend yourself some credibility, particularly towards non-technical people who probably put more weight on having a higher degree.
>Don't listen to anyone that tells you that you can't do something because you don't have a PhD.
I definitely don't think getting a PhD automatically makes you some super genius. I've also worked with a fair number of grad students get their PhD that probably shouldn't have just because they'd been in their program long enough and basically got "pushed" out. That said, if OP is coming at the perspective of starting a business and presumably needing to convince investors/clients that he knows what he's doing, it's kind of silly to suggest he doesn't need any PhD's working for him.
Even if whatever job they're doing doesn't really require "PhD-level expertise", hiring a PhD is a fairly easy way to lend yourself some credibility, particularly towards non-technical people who probably put more weight on having a higher degree.
I think for non scientists looking to work in biotech, it is critical to also understand how the industry works and how value is created. That's the failure point for most biotech startups -- they dont have a useful application of their tech, or the useful application takes costs too much to develop compared to the value it creates
A practical way to do this is to invest in biotech stocks. This will expose you to clinical data, the regulatory process, and how value is created and destroyed. Evaluating clinical data and unmet medical needs is the core skillset in evaluating market potential of a drug, device, diagnostic or patient-facing software
Having skin in the game will help you focus your learning. But only invest as much as you can afford to lose, treat it like tuition.
A practical way to do this is to invest in biotech stocks. This will expose you to clinical data, the regulatory process, and how value is created and destroyed. Evaluating clinical data and unmet medical needs is the core skillset in evaluating market potential of a drug, device, diagnostic or patient-facing software
Having skin in the game will help you focus your learning. But only invest as much as you can afford to lose, treat it like tuition.
> "That's the failure point for most biotech startups -- they dont have a useful application of their tech."
This is precisely the failure of every academic ever. We can examine this through the Theil-lens where they're trying to create a narrative of uniqueness where their research applies + revolutionizes everything ( when it does not ).
But everyone knows this is not the case. This is the central plague of all academic research, that its the pursuit of novel understanding before useful application.
I would argue that this is more a defining characteristic of the current academic pyramid scheme than biotech startups.
Biotech startups dont go out for VC unless they HAVE a market.
Biotech phds go to the NIH regardless of whether theres a market.
This is precisely the failure of every academic ever. We can examine this through the Theil-lens where they're trying to create a narrative of uniqueness where their research applies + revolutionizes everything ( when it does not ).
But everyone knows this is not the case. This is the central plague of all academic research, that its the pursuit of novel understanding before useful application.
I would argue that this is more a defining characteristic of the current academic pyramid scheme than biotech startups.
Biotech startups dont go out for VC unless they HAVE a market.
Biotech phds go to the NIH regardless of whether theres a market.
> This is the central plague of all academic research, that its the pursuit of novel understanding before useful application.
Plague of all academic research?! Isn't the point of academic research to understand before application (and quite often application is not the goal at all and that's ok)?
I get that it may seem trivial to apply something, but with high-cost risks of mistakes when they happen in biology/medicine it is not.
The pharma industry learned this the hard way: https://en.wikipedia.org/wiki/Thalidomide
It seems to me that some software engineers/computer scientist think that other fields are slow/full stupid people because the progress is not fast. Well the progress is not fast because cost to start is often huge, wetware can't be moved to cloud, stakes are higher than unhappy customers etc.
Plague of all academic research?! Isn't the point of academic research to understand before application (and quite often application is not the goal at all and that's ok)?
I get that it may seem trivial to apply something, but with high-cost risks of mistakes when they happen in biology/medicine it is not.
The pharma industry learned this the hard way: https://en.wikipedia.org/wiki/Thalidomide
It seems to me that some software engineers/computer scientist think that other fields are slow/full stupid people because the progress is not fast. Well the progress is not fast because cost to start is often huge, wetware can't be moved to cloud, stakes are higher than unhappy customers etc.
> This is the central plague of all academic research, that its the pursuit of novel understanding before useful application.
Uh, not all knowledge is about making products. I hope you meant "all academic research" only in context of biotech startups. And applied science is only part of the scientific endeavor, which is to understand the world, regardless of whether that can be monetized.
Uh, not all knowledge is about making products. I hope you meant "all academic research" only in context of biotech startups. And applied science is only part of the scientific endeavor, which is to understand the world, regardless of whether that can be monetized.
Right. Im not claiming that it is.
However a trait which is more readily attributed to academic research ( not creating a profitable product ) is more applicable to academic research than to startups.
Per the Thiel quote (summarized):
The history of academic research is riddled with scientists making no profit.
However a trait which is more readily attributed to academic research ( not creating a profitable product ) is more applicable to academic research than to startups.
Per the Thiel quote (summarized):
The history of academic research is riddled with scientists making no profit.
In my view, at the very least, a person needs to find out whether they have any hope of functioning in a lab environment. Have you ever fixed a bicycle, or a toilet? I've worked with some really bright people from a variety of fields, and many of them would be downright dangerous to themselves and others if allowed into a lab.
Even if you end up managing a lab rather than working in one, you need to develop a sense for how a lab works and what it's like to do an experiment.
Even if you end up managing a lab rather than working in one, you need to develop a sense for how a lab works and what it's like to do an experiment.
[deleted]
Thank your this. I am also very interested in biology and was looking to find a way to buy used equipment. I was reading biotech books and I was blown away by recombinant DNA and how insulin was invented as a medicine.
I was looking to find a way to buy used equipment
"I'm planning to participate in Olympic games against the best athletes of the planet. I plan to buy a few used sneakers of a similar size than my feet and a second hand tennis racket that is not too worned out. I plan to win against people with brand new and tailor-made equipment".
In short. Don't do it unless you know what you are buying. If you know what you are buying, don't do it if you can afford doing otherwise.
In most cases used equipment will be sold because either the lab had been crushed by better equipped competitors, or is obsolete.
"I'm planning to participate in Olympic games against the best athletes of the planet. I plan to buy a few used sneakers of a similar size than my feet and a second hand tennis racket that is not too worned out. I plan to win against people with brand new and tailor-made equipment".
In short. Don't do it unless you know what you are buying. If you know what you are buying, don't do it if you can afford doing otherwise.
In most cases used equipment will be sold because either the lab had been crushed by better equipped competitors, or is obsolete.
> idiots
really idiots ? I'd be shocked if PhD were really lacking intelligence that much (even if you used the term as an hyperbole)
really idiots ? I'd be shocked if PhD were really lacking intelligence that much (even if you used the term as an hyperbole)
Mostly, my advice as a biomedical researcher and modeler/software user is this, which is not skills based at all and is instead just a reminder that you're going to need a really fundamental shift in thinking.
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"
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"
So much this. I think the most difficult thing about transition from hard sciences such as cs/physics/math to wet science such as biology/sociology/psychology is to get used to the fact that sometimes things don't work and behave as expected and it takes enormous amount of work to get it right.
Therefore you can't really go and design things form the first principles, since first principles are not really that well known. Of course you can try, and get some successes but that is rare. Add extremely complicated AND nonlinear dependences between the systems you are working with and you may get idea why we still don't have cure for most cancers yet.
Therefore you can't really go and design things form the first principles, since first principles are not really that well known. Of course you can try, and get some successes but that is rare. Add extremely complicated AND nonlinear dependences between the systems you are working with and you may get idea why we still don't have cure for most cancers yet.
Thirding this. For all the people outside of science who seem to hold it up as this shining example of logical purity, in practice things are far more ambiguous than we're making it out to be.
When I did lab work, results pretty much always had to be viewed through the lense of "Did my technique screw up the data?" before you can even start thinking about the research implications.
When I did lab work, results pretty much always had to be viewed through the lense of "Did my technique screw up the data?" before you can even start thinking about the research implications.
"sometimes things don't work and behave as expected"
I think it's a great misconception to think that people who work with computers work things out from first principles. Dealing with complex systems that they don't understand, and repeatedly plunging into new areas, is exactly why computer people think they can handle things like biology.
It also seems illogical to say "nobody understands biology, therefore you cannot hope to". If nobody understands much, that makes it more likely an outsider can contribute.
The attitude of many responses in this thread reminds me of the culture/class gap I've seen between lawyers and legal IT analysts (one person I worked with had a degree in biology as it happened).
I think it's a great misconception to think that people who work with computers work things out from first principles. Dealing with complex systems that they don't understand, and repeatedly plunging into new areas, is exactly why computer people think they can handle things like biology.
It also seems illogical to say "nobody understands biology, therefore you cannot hope to". If nobody understands much, that makes it more likely an outsider can contribute.
The attitude of many responses in this thread reminds me of the culture/class gap I've seen between lawyers and legal IT analysts (one person I worked with had a degree in biology as it happened).
By that I mean that in biology things are quite... Undeterministic. Working with computer is (except some very rare cases) a work when non-deterministic behaviour is most likely a bug. It's more or less the same in other hard sciences.
In biology (and other non-hard sciences) the systems quite often behave in non-deterministic way. To be clear I don't claim that this non-deterministic behaviour is inherent to biological systems (although it is when we take the quantum limit), but rather that they are so complex that untangling this complexity to get nice casuality is virtually impossible. Add the complexity and lack of understanding _on top of that_.
> It also seems illogical to say "nobody understands biology, therefore you cannot hope to".
I didn't mean to imply that "nobody understands biology, therefore you cannot hope to". I rather meant to express that (at the current level of our species and tools we have) "biology cannot be understood the same way computers are". IMO of course. Therefore applying the same methodology that you apply to hard sciences (math, phys, cs [, chem?]) may not yield as good results and often doesn't.
> If nobody understands much, that makes it more likely an outsider can contribute.
I can agree that it's easier for outsider to contribute something to biology than physics, mathematics or CS. However, to contribute meaningfully good grasp of concepts and techniques is necessary. Contrarily to math, theoretical or CS in biology it has to be acquired at the front lines of the battles i.e. in the lab. I agree that bioinformatics has made huge leaps in the recent years, but AFAIK nearly all significant new contributions in biology come from the laboratory work not theoretical considerations.
In biology (and other non-hard sciences) the systems quite often behave in non-deterministic way. To be clear I don't claim that this non-deterministic behaviour is inherent to biological systems (although it is when we take the quantum limit), but rather that they are so complex that untangling this complexity to get nice casuality is virtually impossible. Add the complexity and lack of understanding _on top of that_.
> It also seems illogical to say "nobody understands biology, therefore you cannot hope to".
I didn't mean to imply that "nobody understands biology, therefore you cannot hope to". I rather meant to express that (at the current level of our species and tools we have) "biology cannot be understood the same way computers are". IMO of course. Therefore applying the same methodology that you apply to hard sciences (math, phys, cs [, chem?]) may not yield as good results and often doesn't.
> If nobody understands much, that makes it more likely an outsider can contribute.
I can agree that it's easier for outsider to contribute something to biology than physics, mathematics or CS. However, to contribute meaningfully good grasp of concepts and techniques is necessary. Contrarily to math, theoretical or CS in biology it has to be acquired at the front lines of the battles i.e. in the lab. I agree that bioinformatics has made huge leaps in the recent years, but AFAIK nearly all significant new contributions in biology come from the laboratory work not theoretical considerations.
I'm in my 5th year of a chemical biology PhD and I'm looking to potentially move into computer science/data science (been programming for 15 years) after I graduate. Maybe we can just switch? I work with CRISPR for my project.
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
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
No one can truly understand all of the background for the field. Doing so would require like 10 PhDs
It is important to have a fundamental understanding of biology and chemistry, and ability to critically evaluate data, but once you have that, its more important to be able to find people with the right domain expertise and have productive interviews with them about data. If you are a neuroscience PhD you should not be evaluating a cancer assay or a GLP tox study on your own, you should read enough to have a productive interview with someone with more expertise, then go have that interview
You need a rigorous science background to start a company but that is not sufficient. Many projects are just not fundable. For non scientists, id recommend learning how the industry works and how value is created, because that complements the expertise of scientists who know the science but dont know the right clinical applications
It is important to have a fundamental understanding of biology and chemistry, and ability to critically evaluate data, but once you have that, its more important to be able to find people with the right domain expertise and have productive interviews with them about data. If you are a neuroscience PhD you should not be evaluating a cancer assay or a GLP tox study on your own, you should read enough to have a productive interview with someone with more expertise, then go have that interview
You need a rigorous science background to start a company but that is not sufficient. Many projects are just not fundable. For non scientists, id recommend learning how the industry works and how value is created, because that complements the expertise of scientists who know the science but dont know the right clinical applications
+1 for this is a PhD level thing. The science and technology is not at the sort of commodity level where you do a masters and you're done really. A good approach is to just get in the face of a lot of lab people working on new tech/IP who could really use software support for what they are doing in an area of trial and error that could otherwise scale. But if you are thinking biotech with lab, you need huge seed investment to begin with for the lab, unless you are spinning out of academia.
> you are going to need a PhD if you want to truly understand all of the background of the field
Is that the need, though? Few SaaS CEOs truly understand all the background of computer science. They do understand the problem space, but they leave it to the experts in the organization to get into the details of applying computer science to the problem.
Is biology different, where the level of expertise needs to be at a more detailed level in order to lead an organization?
Is that the need, though? Few SaaS CEOs truly understand all the background of computer science. They do understand the problem space, but they leave it to the experts in the organization to get into the details of applying computer science to the problem.
Is biology different, where the level of expertise needs to be at a more detailed level in order to lead an organization?
The key distinction here is starting a company vs leading a company. To just walk in and lead an established company - you'd be fine with a better than average understanding, as long as you trust the experts in your company to do what is right.
To found a company? How do you know where to start? If you are hiring PhDs to come up with the idea for you, how do you if their ideas are novel or worthwhile?
Not to disparage SaaS founders, but I think it's somewhat easier to look about and say "there is no app for sending texts via REST API" and do that, than it is to look at say "no one has found a way to selectively target cancer cells for CRISPR" and then go do that.
To found a company? How do you know where to start? If you are hiring PhDs to come up with the idea for you, how do you if their ideas are novel or worthwhile?
Not to disparage SaaS founders, but I think it's somewhat easier to look about and say "there is no app for sending texts via REST API" and do that, than it is to look at say "no one has found a way to selectively target cancer cells for CRISPR" and then go do that.
I think the key distinction is that there is a well-established infrastructure around software, such that the average SaaS founder (even 10 years ago) is/was not doing fundamentally new things with computers. If they were, they would probably need to understand computer science well. Take any moonshot ML company- they need quite deep technical depth and usually have a founder that has spent time in academia.
We are still at a point in Biotech where companies have to do fundamentally new things with their tech to succeed. If you were starting a company that applies CRISPR to X genetic disease, you might not need a Ph D. to start the company, because you can apply existing technology to solve that problem. But that begs the question: Why has no one else done it yet?
At the end of the day, you need a unique insight to start a large company. That means experience with either the problem space or the technology space is helpful. Not discouraging OP either, if you have a vision for what the future should look like, and can acquire talent/capital... anyone can start a massive company. So OP should try to acquire that vision.
We are still at a point in Biotech where companies have to do fundamentally new things with their tech to succeed. If you were starting a company that applies CRISPR to X genetic disease, you might not need a Ph D. to start the company, because you can apply existing technology to solve that problem. But that begs the question: Why has no one else done it yet?
At the end of the day, you need a unique insight to start a large company. That means experience with either the problem space or the technology space is helpful. Not discouraging OP either, if you have a vision for what the future should look like, and can acquire talent/capital... anyone can start a massive company. So OP should try to acquire that vision.
A big issue is its actually expensive to start a company that involves lab. Software can genuinely be one person in their bedroom with some AWS credit. I think the "why has no one else done this?" question is one of the worst anyone in the history of business or science can ask themselves. Any opportunity looks like that, it's why its an opportunity! The very best ones look like an obvious gaping chasm when you see them.
I'm in agreement with that sentiment. For any given opportunity, even if it's "obvious" that someone should have done it, doesn't mean it's been done before. If something seems like an opportunity you should probably try to validate it, instead of immediately discounting it. That's how I started my last company!
But there's no doubting that some sort of counter-intuitive or hidden insight is necessary. Even if that insight is: "people think this is obvious and not worth doing, but it actually IS worth doing." RE: Zero to One.
But there's no doubting that some sort of counter-intuitive or hidden insight is necessary. Even if that insight is: "people think this is obvious and not worth doing, but it actually IS worth doing." RE: Zero to One.
Founders generally don't start companies in vacuums. They are the result of communication with other people on the viability of ideas. They meet a subject matter expert in a field, they get to talking, they come up with an idea. The idea gets floated around to other subject matter experts. If something seems viable then prototypes are made to gauge market fit.
If what you're saying is true, there should be no reason why many of the most successful biotech management consultants do not come from a PhD background.
In biology, the problem space is extremely vast and very hard to understand. I have a PhD in biology and find computer science relatively logical and simple. You can just look things up. Looking things up for a specific topic is not even easy in biology.
Funny how you find comp science is simple and logic. Of course on a if else method level yes. But how to have a running system, dependencies, fail safe operations all coupled with tousand / millions lines of code is easily more complicated than a simply thesis about a mode of action of a protein.
I think we need to stop this useless debate as clearly one side has often no clue about the other. This behavior has led to massive failure in my career to successfully launch IVD devices or have a successfully bioinfo software for physicians - talking from experience here.
I think we need to stop this useless debate as clearly one side has often no clue about the other. This behavior has led to massive failure in my career to successfully launch IVD devices or have a successfully bioinfo software for physicians - talking from experience here.
The point is that a computer system can be entirely understood from the chips on up. Sure, it gets complex with millions of LOC. But we humans designed computers and wrote the code. Biology is not like that, and there is lack of a completed understanding form the proteins on up. So you get the complexity amplified on all fronts.
Biological signals are very noisy. By contrast, CS signals are much cleaner. Only when CS delves into large volumes of social data do signals complicate. But even then, the cooked events of digital data is much cleaner than the raw signals inherent in bio-data. The complexity, interdependency, nonlinearity, and unknowns in bio variables are frequently overwhelming even for experts.
Success in biological research is driven by the scientist's ability to sort wheat from all this chaff, and it's acquired by gathering the data themselves hands-on in wet labs. A master biologist has learned how to navigate that space experimentally and analytically using techniques they've mastered just well enough to see over the noise.
As someone with degrees in both bio and CS and 15 years of work that crosses the boundary between them, I'm decidedly more in awe of those who have mastered biology.
Success in biological research is driven by the scientist's ability to sort wheat from all this chaff, and it's acquired by gathering the data themselves hands-on in wet labs. A master biologist has learned how to navigate that space experimentally and analytically using techniques they've mastered just well enough to see over the noise.
As someone with degrees in both bio and CS and 15 years of work that crosses the boundary between them, I'm decidedly more in awe of those who have mastered biology.
What do you mean "can"? Nobody ever does, so I think it is valid to say that it "cannot". Otherwise, you could just as well say that a biological system can be understood from the atoms on up, since quantum theory provides a complete understanding.
I think that you and a lot of people in this thread don't understand the impossibility of understanding millions of lines of code. Do you really think "computer geeks" just hold them all in their heads, and then assume that since they can do that, it must not be difficult?
I think that you and a lot of people in this thread don't understand the impossibility of understanding millions of lines of code. Do you really think "computer geeks" just hold them all in their heads, and then assume that since they can do that, it must not be difficult?
> Do you really think "computer geeks" just hold them all in their heads, and then assume that since they can do that, it must not be difficult?
No, but I think a group of geeks could. However a group of biologists cannot explain a biological system in full. When I say a computer system can be understood in full, I mean that human knowledge encompasses the working and programming of the machine, not that one person could know every detail. This is not the case for biology, as there's plenty we don't know.
> Otherwise, you could just as well say that a biological system can be understood from the atoms on up, since quantum theory provides a complete understanding.
It really doesn't provide a complete understanding for biology anymore than it does for sociology. It's not possible for humans to reductively explain such fields in terms of physics. No one can even prove this is possible. But it's not a complete understanding for physics either, since there's Relativity and questions about quantum gravity and dark energy.
No, but I think a group of geeks could. However a group of biologists cannot explain a biological system in full. When I say a computer system can be understood in full, I mean that human knowledge encompasses the working and programming of the machine, not that one person could know every detail. This is not the case for biology, as there's plenty we don't know.
> Otherwise, you could just as well say that a biological system can be understood from the atoms on up, since quantum theory provides a complete understanding.
It really doesn't provide a complete understanding for biology anymore than it does for sociology. It's not possible for humans to reductively explain such fields in terms of physics. No one can even prove this is possible. But it's not a complete understanding for physics either, since there's Relativity and questions about quantum gravity and dark energy.
"You can just look things up"
Not only is documentation pretty scarce these days, but good information on the interactions between pieces of software is much scarcer, due to the exponential number of ways that it can interact.
Not only is documentation pretty scarce these days, but good information on the interactions between pieces of software is much scarcer, due to the exponential number of ways that it can interact.
> Looking things up for a specific topic is not even easy in biology
How do you find info on bio topics right now?
How do you find info on bio topics right now?
Finding the actual info is not terribly difficult: Google Scholar and Pubmed, maybe with some of the arxivs.
The trick is interpreting it and putting it into context. A paper will report the results of one specific experiment, and it’s very rarely exactly what you want. Understanding how a result will generalize to other conditions is tricky, even for experts: there are tons of weird feedback loops, unusual dynamics, and other traps for the unwary (plus badly designed experiments and the occasional legit Type I error). For example, doubling the amount of a substance almost never doubles its effect, and in some cases, the effects aren’t even monotonic: ~75% alcohol, for example, is a much better disinfectant than 50 or 100%.
With time—-and lots of paper-reading, you do eventually develop a sense for what factors might matter and how you could check.
The trick is interpreting it and putting it into context. A paper will report the results of one specific experiment, and it’s very rarely exactly what you want. Understanding how a result will generalize to other conditions is tricky, even for experts: there are tons of weird feedback loops, unusual dynamics, and other traps for the unwary (plus badly designed experiments and the occasional legit Type I error). For example, doubling the amount of a substance almost never doubles its effect, and in some cases, the effects aren’t even monotonic: ~75% alcohol, for example, is a much better disinfectant than 50 or 100%.
With time—-and lots of paper-reading, you do eventually develop a sense for what factors might matter and how you could check.
Depends on how deep you want to go. You need to find out what people are working on, and you do this by searching on PubMed and talking to people working in that field.
This sounds similar to what my friends in academia describe while exploring their fields. They build an impressive repository of knowledge regarding who's working on different topics/subfields over time. This may be one advantage of pursuing a PhD. Would be cool to see someone make the process of acclimating to a new field more accessible.
There are plenty of executives running biotech companies who have barely any biology expertise. Pretty much everything they know is contained to their product and its competitors. There are PHD's and MD's in SV who basically specialize in working as consultants for these types of products and back up their potential. You see it all the time with health startups when they have a medical advisor or something to that effect.
Doesn't give me much confidence in their product.
This is the central argument for pursuing a phd.
But its rubbish.
Speed + onus behind learning > everything.
Also note the self-serving bias where Phd's swear:
"You need a phd"
But its rubbish.
Speed + onus behind learning > everything.
Also note the self-serving bias where Phd's swear:
"You need a phd"
Can you explain your reasoning behind need a PhD? It makes sense to me why you should learn a lot to understand a complex field, but I fail to see why you should also spend a few years researching a very narrow subfield... wouldn't it be better to be a generalist and be able to apply your knowledge widely, as opposed to only understanding a tiny sliver of the whole field?
You don’t need a PhD, but it helps.
If you want to have a decent shot at getting funding from biotech VCs, the team overall needs experience in biotech AND startups, and the less experience the founder has in biotech the more the team will require to compensate for the deficiency. And vice versa for business experience, a freshly minted PhD is going to need team members with significant biotech BD experience.
A PhD or MD counts towards experience in biotech. MS + years of industry experience also counts, as would BS + many years of industry experience.
A PhD is nice because because it demonstrates deep experience in a narrow field plus an understanding of context. The context part is key. Autodidacts tend to miss out on the context, and their learned knowledge tends to be more fragile as a result, so there is a slight bias towards advanced degrees.
Insufficient experience is a hard no for biotech. Nobody wants to see another Theranos, and notably biotech VCs didn’t put any money into Theranos. We’d like to keep it that way.
If you want to have a decent shot at getting funding from biotech VCs, the team overall needs experience in biotech AND startups, and the less experience the founder has in biotech the more the team will require to compensate for the deficiency. And vice versa for business experience, a freshly minted PhD is going to need team members with significant biotech BD experience.
A PhD or MD counts towards experience in biotech. MS + years of industry experience also counts, as would BS + many years of industry experience.
A PhD is nice because because it demonstrates deep experience in a narrow field plus an understanding of context. The context part is key. Autodidacts tend to miss out on the context, and their learned knowledge tends to be more fragile as a result, so there is a slight bias towards advanced degrees.
Insufficient experience is a hard no for biotech. Nobody wants to see another Theranos, and notably biotech VCs didn’t put any money into Theranos. We’d like to keep it that way.
> "You don’t need a PhD, but it helps."
> "A PhD is nice because because it demonstrates ..."
An employees willingness + investment to singularly do just one thing for the rest of their life.
It works as really good signaling for a CEO / hiring manager to assess someone as a really consistent employee.
> "A PhD is nice because because it demonstrates ..."
An employees willingness + investment to singularly do just one thing for the rest of their life.
It works as really good signaling for a CEO / hiring manager to assess someone as a really consistent employee.
Depends what sort of company you wish to produce. 23andMe you could definitely have done without a PhD as its much more traditional market fit "lets do logistics". But Illumina to produce the micro array in the first place or have the software to optimise probes etc. that takes a certain level of understanding have noticed or seen the value of that thing laying around in academia before it was mainstream. All the interesting new ideas/IP are sat within an academic network more than a company one at the moment so accessing that network is a big reason for a PhD.
Presumably, this hypothetical biotech startup is going to be focused on some narrow niche of CRISPR technology, so being a specialist is exactly what's required in my mind. If the OP just wants to learn about CRISPR he could probably do a masters or even a year or two of self study. I find it very unlikely that they would be able to come up with something novel and marketable without even more hands on time.
But if PhD is basically learning followed by research, you could also just do a masters / learning followed by research at a company (directly relevant to what the company is doing)?
There is a lot of professional development during PhD too. Especially if you're at a well funded institution with enough prestige to have a lot of international collaboration. The resources are typically a lot more experimental and RnD focussed than you will find in many companies. Having done a masters I'd say the "learning" there is rote and the sort of thing you do in school. PhD what you learn is how to actually see through BS you otherwise took as "truth" on a masters, see the landscape of research at play, which ideas have merit, which don't, what's interesting and likely has large implications etc. The actual skills and exposure is something quite tricky to explain but unless you do research in a really big company, it might be a lot harder to develop those. Especially if you are an entry level technician, as most of the more senior positions are likely to be those with PhDs already. At least in biotech, software and hardware it's way more about industry career paths and RnD.
Yeah I'm not sure I agree you need a full-blown PhD just to understand this stuff. Some-hands on work, definitely. I would say that you need a PhD to be taken seriously as a leader on the science-side of a biotech company though.
You don't need a PhD to find market fits for these types of products. They are self aggrandizing.
Market fit isn’t the only thing that matters.
Theranos had an excellent market fit—-people hate having blood drawn—-but the technology did not work and fundamentally could not work. The whole point of being a subject matter expert is to learn about things like that.
It’s true that there are other routes to becoming an expert in something (and proving it). A masters and a stint at a well-known company certainly works for many people; bootstrapping your own company might be an option too, but you’d need to be very successful to establish your bonafides. The advantage of a PhD is that it’s a fairly well-known quantity.
Theranos had an excellent market fit—-people hate having blood drawn—-but the technology did not work and fundamentally could not work. The whole point of being a subject matter expert is to learn about things like that.
It’s true that there are other routes to becoming an expert in something (and proving it). A masters and a stint at a well-known company certainly works for many people; bootstrapping your own company might be an option too, but you’d need to be very successful to establish your bonafides. The advantage of a PhD is that it’s a fairly well-known quantity.
Ignore this.
I studied Genetics and CS. What you can do instead is reach out to the PhDs and gather information from them to start getting a general sense of the direction.
This is easy some schools allow you to sit in on lectures / come to events. Most PhDs are usually off working independently though. Make friends with the competent PhDs gather info. Hack the system you don’t need a PhD you can hire one later.
This is all assuming you have the raw talent of product and can lead well, and can convince people exceptionally well, otherwise disregard.
I studied Genetics and CS. What you can do instead is reach out to the PhDs and gather information from them to start getting a general sense of the direction.
This is easy some schools allow you to sit in on lectures / come to events. Most PhDs are usually off working independently though. Make friends with the competent PhDs gather info. Hack the system you don’t need a PhD you can hire one later.
This is all assuming you have the raw talent of product and can lead well, and can convince people exceptionally well, otherwise disregard.
Hire the PHD.
God knows they'll work for 3rd-world labor rates.
God knows they'll work for 3rd-world labor rates.
> just about the most complex thing humans have ever studied
The question is if the methods are complex. AFAIK computational biology involves statistics, statistics and statistics. The systems themselves are incredibly complex and interconnected , but the abstraction levels of the math involved are not incredibly high.
The question is if the methods are complex. AFAIK computational biology involves statistics, statistics and statistics. The systems themselves are incredibly complex and interconnected , but the abstraction levels of the math involved are not incredibly high.
There is statistics and then there is statistics. For example this paper is statistics based https://www.nature.com/articles/ng.3487 with some novel data too. But its stats over graphs, which is way less straight forward. You don't just fire up R and do model <- lme(data, x ~ y + z) That paper spawned a fairly amazing startup too one that I really really really doubt anyone could possibly have come up with without a PhD and a lot of insight https://mogrify.co.uk/
Sure, I guess my wording wasn't clear. It is the system itself that is complex, not the equations or statistics we use
Your best bet is probably to look for a co-founder with an MS or PHD in molecular biology rather than trying to get yourself to that level from the ground up. If you have some biology background and just want to understand the specifics of Crispr, you can start with this paper[0] and just google every term you aren't familiar with until you make it all the way through. If you don't know biology at all, this[1] is a common text book in undergrad programs for molecular/cellular biology.
[0] https://www.ncbi.nlm.nih.gov/pubmed/23287718
[1] https://www.amazon.com/Molecular-Biology-Cell-Bruce-Alberts/...
[0] https://www.ncbi.nlm.nih.gov/pubmed/23287718
[1] https://www.amazon.com/Molecular-Biology-Cell-Bruce-Alberts/...
Go find a biotech company hiring software engineers where the biochemists are in the same room/ building. Get a job there. Get to know biochemists and ask them about their work and work problems, listen to their talks, read a lot. Get mentored by a PhD in the field at the company.
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.
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.
Right now I know a ridiculous amount of people with bio phds from top tier schools that are trying to make it as data scientists. I don't think there is a bio revolution like the software revolution that is happening quite yet.
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.
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.
>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.
yep, that's why i left the industry. the companies know that the science staff are willing to work for less, so long as they have the opportunity to do what they're interested in. so they end up working for much less.
yep, that's why i left the industry. the companies know that the science staff are willing to work for less, so long as they have the opportunity to do what they're interested in. so they end up working for much less.
Same thing I've witnessed. I was in the industry for almost a decade and worked on the computational side for half that time, then left for a DS role in an unrelated industry.
Everyone I knew that had halfway decent programming/math skills ended up leaving eventually. The pay differential for what your skills can bring you in other industries is massive.
There's so many interesting things going on in biotech, but they're always on the horizon and are likely decades from any commercial product (synthetic bio, DNA as data storage, etc). On top of that, no one gets super rich from equity at a start-up unless you are an exec. If you're lucky and pick the right pony when it's under 30 employees and spend a decade there until a massive IPO, you'll at most get a couple 100k's. Employees in biotech are not valuable enough to get big paydays.
Also, you're likely working on products, that if successful, will help people live longer. So when you're already not paid enough to buy the cheapest home in your city, you're at best ensuring those old folks who already have homes will live longer and make your home ownership dream less likely.
Everyone I knew that had halfway decent programming/math skills ended up leaving eventually. The pay differential for what your skills can bring you in other industries is massive.
There's so many interesting things going on in biotech, but they're always on the horizon and are likely decades from any commercial product (synthetic bio, DNA as data storage, etc). On top of that, no one gets super rich from equity at a start-up unless you are an exec. If you're lucky and pick the right pony when it's under 30 employees and spend a decade there until a massive IPO, you'll at most get a couple 100k's. Employees in biotech are not valuable enough to get big paydays.
Also, you're likely working on products, that if successful, will help people live longer. So when you're already not paid enough to buy the cheapest home in your city, you're at best ensuring those old folks who already have homes will live longer and make your home ownership dream less likely.
my background is in biotech and i've worked at a small handful of biotech startups in a scientific capacity.
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.
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.
But here's the question: maybe you can become a provider of highly optimized big data processing or data visualization software, without deep knowledge in biology?
you probably can, but it's hard to know what will be in demand without doing a lot of customer interviews with the people who would need your service.
Where is the best place to meet the right people? I talked to my friend (PhD) and she explained the process of conversion from Illumina file formats all the way to numpy , but I would love to know more.
boston is the best place to meet people in biotech. more specifically, it depends on the kind of data product you're trying to make. there are a plethora of networking events for biotech in the area, and a lot of the local nonprofit institutions will have valuable people for you to meet as well.
Why are Phds so cheap to hire if their skills are so valueable?
> Why are Phds so cheap to hire if their skills are so valuable?
Is a hamster wheel. Your skills are valuable only while you are running. If you stop, your huge time and money investment loses their value gradually, and if you stop for too much time (getting pregnant, suffering an accident or having small kids) you are out of the game. Thus, for many researchers cheap work is better than none.
And there are the stupid artificial constrains, a damocles sword in the shape of a clock. Scientist work is related with Universities schedule. They can expect to be hired mainly at the beginning of the year or in summer, hired for the next year. If you miss the train you will need to wait for another year.
Scientists are expected also by society to produce X discoveries at the interval of age Y and have a limited time for that. This is really idiot. Would be like expecting Leonardo painting the Monna lisa in three years maximum (and exactly between 27 and 29 yo), or stop painting.
And there is also a huge vanity factor. To be associated with an university or big brand even if you just make the coffee there, is good for the ego and help constructing your identity and selling you better later.
None of those have any relationship with what science really is, a method to solve problems, of course
Is a hamster wheel. Your skills are valuable only while you are running. If you stop, your huge time and money investment loses their value gradually, and if you stop for too much time (getting pregnant, suffering an accident or having small kids) you are out of the game. Thus, for many researchers cheap work is better than none.
And there are the stupid artificial constrains, a damocles sword in the shape of a clock. Scientist work is related with Universities schedule. They can expect to be hired mainly at the beginning of the year or in summer, hired for the next year. If you miss the train you will need to wait for another year.
Scientists are expected also by society to produce X discoveries at the interval of age Y and have a limited time for that. This is really idiot. Would be like expecting Leonardo painting the Monna lisa in three years maximum (and exactly between 27 and 29 yo), or stop painting.
And there is also a huge vanity factor. To be associated with an university or big brand even if you just make the coffee there, is good for the ego and help constructing your identity and selling you better later.
None of those have any relationship with what science really is, a method to solve problems, of course
There is not enough demand (and relatively large amount of supply). Biotech isn't like software where it adds immediate value. There are hoops to jump through (unless you are only doing it to cure a disease affecting yourself). The liability is high if things fail. People have accepted bugs as part of computing, they are not so generous when it comes to civil engineering, and even less so when it comes to the life sciences. And most importantly, too many wannabe researchers and not enough money to go around.
To use an analogy, game developers. Plenty of supply because of everyone wants to be game developers. The work is difficult, pay is bad, work-life balance non-existent. Not to mention the economics are brutal. Are individual game developers valuable? Certainly. Are they cheap too? Yes. The problem is structural (big AAA firms as gatekeepers on the high end, tremendous amount of competition on the indie end).
The problem with Bio/Med/Pharma is also structural. Med school supply is capped by forces like professional associations (fancier terms for doctor unions/lobbies), hospital supply is also capped by the similar forces, with perhaps some contributions by the pharma industry. As for pharma, I think enough people has complained about it that it's not worth elaborating here. There has never been a shortage in bio talent or consumer demand. The bottleneck is due regulatory and policy reasons.
HN treats computing and software like how certain groups treats guns. This is the exception not the norm. Most other industries have tremendous oversight/interference by authorities that the move fast break things method is difficult to apply. In practically any other field, the barrier of entry is artificially high and information is locked-in, not open and shared.
Imagine if you create a new Kubernetes load balancer and to get it deployed you need to have it "approved and certified". You pay perhaps 4 figures to the Cloud Native Computing Foundation who is "accredited" by the Association for Computing Machinery to certify software. Sounds ridiculous? This is the way of life for practically everything that is not generic software (embedded automobile/medical/aerospace software is the exception)
Need a leftpad library? Be prepared to discuss licensing for "intellectual property". No fixed pricing on page, a salesperson will contact you and negotiate.
To use an analogy, game developers. Plenty of supply because of everyone wants to be game developers. The work is difficult, pay is bad, work-life balance non-existent. Not to mention the economics are brutal. Are individual game developers valuable? Certainly. Are they cheap too? Yes. The problem is structural (big AAA firms as gatekeepers on the high end, tremendous amount of competition on the indie end).
The problem with Bio/Med/Pharma is also structural. Med school supply is capped by forces like professional associations (fancier terms for doctor unions/lobbies), hospital supply is also capped by the similar forces, with perhaps some contributions by the pharma industry. As for pharma, I think enough people has complained about it that it's not worth elaborating here. There has never been a shortage in bio talent or consumer demand. The bottleneck is due regulatory and policy reasons.
HN treats computing and software like how certain groups treats guns. This is the exception not the norm. Most other industries have tremendous oversight/interference by authorities that the move fast break things method is difficult to apply. In practically any other field, the barrier of entry is artificially high and information is locked-in, not open and shared.
Imagine if you create a new Kubernetes load balancer and to get it deployed you need to have it "approved and certified". You pay perhaps 4 figures to the Cloud Native Computing Foundation who is "accredited" by the Association for Computing Machinery to certify software. Sounds ridiculous? This is the way of life for practically everything that is not generic software (embedded automobile/medical/aerospace software is the exception)
Need a leftpad library? Be prepared to discuss licensing for "intellectual property". No fixed pricing on page, a salesperson will contact you and negotiate.
Disclaimer: I have a PhD. I think one problem is that the love of the subject matter motivates people to go far beyond the bare minimum training required to get a job.
Other comment mentioned game developers; I think another obvious comparison is pilots. Anything people will do because it's fulfilling or prestigious or a popular mission in life will produce an excessive number of applicants for positions.
they're used to working at slavery wages (sub 30k) to get their phd, so even a low salary will feel like a big bump to them.
Supply > demand
Biotech is a giant field, so it really depends what you want to do. I did my undergrad in CS but spent the past ~4 years working at a molecular diagnostics startup, where I do software engineering/stats/machine learning work. Along the way, I learned a fair deal of biology, the fundamentals of the molecular assays we work with, but more importantly the skills fundamental to science (inquiry, designing experiments, generating new knowledge, skepticism, etc.). Honestly, the biggest transition from CS -> sciences is not the specific biology or understanding of experimental techniques, but rather learning how to think like a scientist. These are skills you probably will not have if you come from a strictly programming background.
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.
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 have spent 11 years in molecular biology research.
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/
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/
I was coming into this thread to recommend looking for entry level part time mcb/bio lab work precisely because of point #1.
It's a wax on, wax off thing. The repetition gives you a chance to reflect on the rationale for your technique and bigger picture of what you're doing, in addition to getting hands-on experience in the field.
It's a wax on, wax off thing. The repetition gives you a chance to reflect on the rationale for your technique and bigger picture of what you're doing, in addition to getting hands-on experience in the field.
I think a part-time job would be an overkill
I have trained many students to do biology, and I can teach anybody, without any education, to perform molecular biology experiments in 2 weeks.
I 2 months, they are usually able to generate scientifically valid ideas for research (although, usually not the best ideas).
Biology is extremely simple as a field, and a computer scientist will have no problem getting into the field (while opposite is usually hard)
I have trained many students to do biology, and I can teach anybody, without any education, to perform molecular biology experiments in 2 weeks.
I 2 months, they are usually able to generate scientifically valid ideas for research (although, usually not the best ideas).
Biology is extremely simple as a field, and a computer scientist will have no problem getting into the field (while opposite is usually hard)
I'd say there's no subtitute for hands on experience. In the same way people on here recommend writing your own project to learn a language over simply reading about it, actually doing lab work gives you an understanding that no biology textbook can provide.
I vehemently disagree with the claim that biology is a simple field, and I'm especially surprised to hear this from someone who did work in this field for 11 years. Anyone who has taken an intro biochem class would back me up on this: life is complicated!
I vehemently disagree with the claim that biology is a simple field, and I'm especially surprised to hear this from someone who did work in this field for 11 years. Anyone who has taken an intro biochem class would back me up on this: life is complicated!
I completely agree about hands-on experience: yes, it is extremely useful to practically do a biology engineering project. What I claim, is that about 2 weeks to 2 months of that is enough.
Regarding your second paragraph: I was fortunate to work in 3 fields in my scientific career: organic chemistry, materials science/organic semiconductors and molecular (synthetic) biology. This gave me ability to compare. What I found out, is that biology has the most interesting problems nowadays, and biology day-to-day work is extremely, outrageously, ridiculously stupid. I can not even start describing how amazingly debilitating molecular biology methods are. Right now, in the middle of the night I am stuck in the lab doing a day-and-half long protocol, all consisting on steps like "put liquid in", "pipette liquid out", "incubate for 5 minutes" and "shake for half hour".
Now here I am, after 15 years of school, 5 years of PhD and 11 years after that, doing work which is less intellectual than flipping burgers in McDonalds.
Fortunately, the fact that biology is so simple, gives me hope that it can be automated fairly easily, which I am planning to concentrate on for the next 5 years.
Please don't mix the plain complexity of life, which appears like "too many things to memorize", and complexity of mathematics and computer science, which is "understanding deep layers of abstractions". While I haven't taken intro bio classes, I read books and tons of papers. They are just plainly simple, you don't need brain to read them, you only need to memorize.
Regarding your second paragraph: I was fortunate to work in 3 fields in my scientific career: organic chemistry, materials science/organic semiconductors and molecular (synthetic) biology. This gave me ability to compare. What I found out, is that biology has the most interesting problems nowadays, and biology day-to-day work is extremely, outrageously, ridiculously stupid. I can not even start describing how amazingly debilitating molecular biology methods are. Right now, in the middle of the night I am stuck in the lab doing a day-and-half long protocol, all consisting on steps like "put liquid in", "pipette liquid out", "incubate for 5 minutes" and "shake for half hour".
Now here I am, after 15 years of school, 5 years of PhD and 11 years after that, doing work which is less intellectual than flipping burgers in McDonalds.
Fortunately, the fact that biology is so simple, gives me hope that it can be automated fairly easily, which I am planning to concentrate on for the next 5 years.
Please don't mix the plain complexity of life, which appears like "too many things to memorize", and complexity of mathematics and computer science, which is "understanding deep layers of abstractions". While I haven't taken intro bio classes, I read books and tons of papers. They are just plainly simple, you don't need brain to read them, you only need to memorize.
Genetics and evolutionary ecology falls into the category of "layers of abstraction." Mechanisms for up and down-regulation, self-catalysis, epigenetic.
You're convoluting the simplicity of each step of an experiment with the level of complexity of the field itself. Memorization is a crucial component, but if you believe that's all there then you are severely underestimating the study of life. It would be like claiming that since CS can be boiled down to 0's and 1's that it is therefore a simple field of study.
You're convoluting the simplicity of each step of an experiment with the level of complexity of the field itself. Memorization is a crucial component, but if you believe that's all there then you are severely underestimating the study of life. It would be like claiming that since CS can be boiled down to 0's and 1's that it is therefore a simple field of study.
Good luck with the robot. I wonder, if the individual steps are really that simple, why hasn´t it already be automated already?
And if you don´t mind me asking, what´s the best way to start a little home experiment to see if one may actually be interested in biotech?
And if you don´t mind me asking, what´s the best way to start a little home experiment to see if one may actually be interested in biotech?
Thanks! The reason it was not automated yet, is that non-biologists (including CS engineers) don't know that individual steps are that simple; while biologists do not have experience in thinking in abstractions, and so they simply not seeing that they are doing effectively same operations, just with different reagents.
In general, biologists are very busy pipetting faster than their peers, so they can secure scarce professorship positions. They simply have no time taking step back and looking at more general picture.
People who have skills necessary to build a robot and understand biology, have opportunity to work for companies that pay salaries 5 times higher than academia pays, so they don't bother.
I would not recommend doing any home experiments, especially without professional laboratory experience. Most of the experiments contain dangerous, poisonous or potentially explosive chemicals, and as for a newcomer, the risk is too high.
Instead, I would recommend finding a local biotech interest group, join them and go from there. Alternatively, you can find a local biotech lab and volunteer to help a grad student or postdoc with their project. This is more common than anyone may think. This way, there are no obligations, and you can leave if you don't like it; at the same time, you will get exposure to the state of the art biology.
Again, don't be afraid that you will not be able to bring anything good; biology as a science is ridiculously simple, and if you already have CS or electrical engineering background, you will have no trouble catching up within a month or two (that is how long it takes to our grad students who come from non-biology background to start bringing in new valid scientific ideas).
In general, biologists are very busy pipetting faster than their peers, so they can secure scarce professorship positions. They simply have no time taking step back and looking at more general picture.
People who have skills necessary to build a robot and understand biology, have opportunity to work for companies that pay salaries 5 times higher than academia pays, so they don't bother.
I would not recommend doing any home experiments, especially without professional laboratory experience. Most of the experiments contain dangerous, poisonous or potentially explosive chemicals, and as for a newcomer, the risk is too high.
Instead, I would recommend finding a local biotech interest group, join them and go from there. Alternatively, you can find a local biotech lab and volunteer to help a grad student or postdoc with their project. This is more common than anyone may think. This way, there are no obligations, and you can leave if you don't like it; at the same time, you will get exposure to the state of the art biology.
Again, don't be afraid that you will not be able to bring anything good; biology as a science is ridiculously simple, and if you already have CS or electrical engineering background, you will have no trouble catching up within a month or two (that is how long it takes to our grad students who come from non-biology background to start bringing in new valid scientific ideas).
There are no wet lab communities out here (Switzerland), at least I couldn't find any. But asking a postdoc to help out for free sounds promising.
Btw, I work 50 hours a week plus travel, I don't think it's going to be that fast to get up to speed.
Cheers
Btw, I work 50 hours a week plus travel, I don't think it's going to be that fast to get up to speed.
Cheers
Interesting.
It seems automation would greatly speed up this field.
I wonder if progress is being made on this front.
I wonder if progress is being made on this front.
You are absolutely right.
After those 11 years in molecular biology, I am absolutely convinced that the bottleneck of the field is not some fancy method, but a total lack of automation.
Modern "CRISPR" cool methods are done with essentially the same tools as the research from 50 years ago.
I would claim, that computer scientists are lucky ones: they have skills to create their own tools. To make a better, more automated programming language, you would need same skills as to use that language.
Biologists on the other hands are different. The skills to do biology are totally different from skills needed to automate biology. And so the gap appears: biologists don't even think of automating, and mechanical/electrical/computer engineers are not even aware of the problem
After those 11 years in molecular biology, I am absolutely convinced that the bottleneck of the field is not some fancy method, but a total lack of automation.
Modern "CRISPR" cool methods are done with essentially the same tools as the research from 50 years ago.
I would claim, that computer scientists are lucky ones: they have skills to create their own tools. To make a better, more automated programming language, you would need same skills as to use that language.
Biologists on the other hands are different. The skills to do biology are totally different from skills needed to automate biology. And so the gap appears: biologists don't even think of automating, and mechanical/electrical/computer engineers are not even aware of the problem
Check out Benchling! YC company (S12), founded by a CS undergrad from MIT. [1] Learn more about their story. Biotech is growing so fast. There’s tons of room
for scientists and non-scientists alike. Programmers have a lot to add here, even with zero bio background, the power of a fresh mind. Talk with scientists and learn what problems they have.
[1] https://www.crunchbase.com/person/sajith-wickramasekara
[1] https://www.crunchbase.com/person/sajith-wickramasekara
You certainly should check the Biostar Handbook[1] then and their forum[2]. BioJulia[3] is worth checking as well (I have really high hopes for Julia language).
[1] https://www.biostarhandbook.com/
[2] https://www.biostars.org/
[3] https://biojulia.net/
[1] https://www.biostarhandbook.com/
[2] https://www.biostars.org/
[3] https://biojulia.net/
Does this seem really incongruous to anyone else? It's like a biologist hears something about Linux, and asks about how he can start writing kernel patches having never coded anything in his life. It seems like there's an underlying assumption that computer science >> all other sciences, as if the abstract logic of computer programming is a substitute for basic domain knowledge in other fields, often built over hundreds of years.
You see this often when programmers comment on physics, neuroscience or even philosophy. Everything is understood in terms of computer science and the successes of the computer industry, because obviously reality is fundamentally what programmers spend most of their time on. Which is something Jaron Lanier pointed out a while back.
Of course I only mean some programmers, and I'm sure mathematicians and physicists aren't immune to the same temptation. It's natural to understand the world through the lens of what you understand best, but there are other big fields of human knowledge out there.
Of course I only mean some programmers, and I'm sure mathematicians and physicists aren't immune to the same temptation. It's natural to understand the world through the lens of what you understand best, but there are other big fields of human knowledge out there.
I think also they see that the gap between bootcamp grads or smart college dropouts and a masters in CS is not necessarily that large, and assume the same is true for other fields when that is pretty rarely the case.
Join Lucence. Our dry lab and wet lab work are world class and you will learn in the context of real problems for real patients with real samples.
We are hiring in SF and Singapore for informatics roles.
We are hiring in SF and Singapore for informatics roles.
contact info?
Email your CV and a paragraph on your interest to:
hr at lucencedx.com
Bonus points for citing our active jobs page: https://www.lucencedx.com/careers/
Bonus points for citing our active jobs page: https://www.lucencedx.com/careers/
Generic HR inbox?
I don't think you're actually concerned about this.
I don't think you're actually concerned about this.
I'm a self-taught bio-informatician (working at large healthcare company, doing Next Generation Sequencing related things). although I am actively seeking to better myself, My code is horrible, we have one "library" (a file really) full of functions and since 2 months a couple of classes that I already feel like completely re-writing. A lot of code is in Jupyter Notebooks somewhere because we just got the hang of Git last year, let alone proper branching. Pull requests? Yes I've heard of them. Transferring code to partners is always troublesome. Man I wish we would have had a proper code-writer on board from the very start, bonus points if that someone excited about biology!
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.
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.
I love git. Literally the only way to be productive when you've got more than 4 people touching code.
Do you guys allow remote work?
Do you guys allow remote work?
I work from home sometimes, and 1 of our team members is in the US (but still at the same company). We have no people completely isolated that never come into the office.
I know for programmers / coders its a weird question but what about part time?
Why is this a weird question? I work 90% (36 hours) many of my colleagues work 32. In the Netherlands (in my bubble) it seems almost standard for both parents to go to 32 (4x8) and both have one non-weekend day with the kid(s).
There are two areas where you can jump in more easily with a CS/software background. Many companies are building informatics/analysis pipelines. That’s likely closer to what you know and you can pick up enough biology along the way.
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.
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.
[deleted]
To be more specific, the first company I worked at had three co-founders; an algorithms person, a functional biology expert, and someone with experience in building software teams. The first two came from academia.
For most of the things you actually do in practice as a scientist in that field you generally need to be trained by someone that knows the particular area of biology and the specific methods you're using. It's not just about the general background knowledge, which is certainly important, but on top of that you need specific experience for each specialized area you work in.
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.
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.
Not mine but a useful link. Bioinformatics may not be exactly what you are looking for but its a nice bridge between software and biology.
https://github.com/ossu/bioinformatics
https://github.com/ossu/bioinformatics
I have a genetics phd and work as a bioinformatician. It takes time for concepts to fully "sink in" in biology. Learning one independent theory/fact/concept is great, but it is when you hook it up to all your other knowledge that it becomes powerful. I would read as much as you can, wait a month, then go back and re-read it. I promise you'll see and think about things you haven't before.
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.
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.
I lead projects with software and bioinformaticians in one of the worlds top bioinfo/genetics company.
If you want my opinion:
Salary sucks in such companies as there are way too many phds out there. Big egos and never built a company... And bios have no clue about good engineering, TDD or CI. It is all copy paste scripts.
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.
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.
If you're interested in genetic engineering/CRISPR, check out the following book: Gene by Siddhartha Mukherjee. He does a fantastic job of layman's writing on science, his other book on Cancer is fascinating and extremely well written, that one is called "Emperor of Maladies". I'd highly recommend both, but don't expect ANY other writing on bio to be that engaging :)
https://www.amazon.com/Gene-Intimate-History-Siddhartha-Mukh...
https://www.amazon.com/Gene-Intimate-History-Siddhartha-Mukh...
I transitioned into cancer immunotherapy research after Computer Science grad school. Biology is a mess and it will take several years to get up to speed on any one research area. The best options are either grad school or finding a job which doesn't initially require a lot of bio background but provides the opportunity to learn. It's also worth checking out local biohacker spaces, they can teach some basic techniques but you'll hit a ceiling quickly. To really "start a biotech company" you'll really need to spend a long time outside your comfort zone.
Where are you located? A lot of places now have community biology labs in makerspaces that are doing really fun things. Start here: https://wiki.hackerspaces.org/Hackerspaces
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.
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.
The hackerspaces.org link is good, but to add to that, here is a list that is specifically "biohacker" groups/spaces.
https://diybio.org/local/
For anybody in/near the Research Triangle Park area of NC, there is Tri-DIY-Bio, a pretty active DIY bio group. http://www.tridiybio.org/
https://diybio.org/local/
For anybody in/near the Research Triangle Park area of NC, there is Tri-DIY-Bio, a pretty active DIY bio group. http://www.tridiybio.org/
"Getting started in biology" and "starting a biotech company" to me are opposite ends of a continuum, akin to "learning how to walk" vs "training for a marathon". I made the transition from web dev to bioinformatics five years ago and while I have a good job in academia, I wouldn't dream of starting a bioX company without a bioX cofounder.
[deleted]
I would start with genetics and genomics, then add some protein knowledge and focus on the techniques that rely mostly on informatics/bioinformatics, like Next-Generation Sequencing, chromatography and similar.
Add some evolution and phylogenetic, which with a mathematical background should be straightforward to grasp.
Add some evolution and phylogenetic, which with a mathematical background should be straightforward to grasp.
The Bio tech seen is very credential focused. It is not like standard tech that often welcomes people with other experience. There are several likely reasons for this.
1. An over abundance of Bio Phd's. 2. Medicine in particular is tricky 3. Academics can be a$$Holes 4. They don't pay as well.
1. An over abundance of Bio Phd's. 2. Medicine in particular is tricky 3. Academics can be a$$Holes 4. They don't pay as well.
Zack Booth Simpson might be interesting to talk to:
https://www.linkedin.com/in/zack-booth-simpson-1084b3/
He transitioned from computers to biology.
Read "Genomics & Personalized Medicine; What Everyone Needs to Know"... twice, paired w YouTube videos.
Work in the industry for a few years.
To be honest, the scientists just need big data + data science + workflows/ pipelines.
Work in the industry for a few years.
To be honest, the scientists just need big data + data science + workflows/ pipelines.
I find this resource very useful for learning bioinformatics:
https://github.com/ossu/bioinformatics
Biotech startup school: https://www.baybridgebio.com/biotech-startup-school.html
You may way to look into biotech incubators such as IndioBio[1] to get an idea of what it takes.
[1] https://indiebio.co/
[1] https://indiebio.co/
> I'm interested in learning more about the skills needed to start a biotech company, but I'm lacking the masters degree in biology. What skills are actually needed to get into the field
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
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
I'm in a similar boat. I have a bachelors in computer science, 13 years in industry, and I'm fascinated by biology.
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...
[2] https://freeradical13.github.io/
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...
[2] https://freeradical13.github.io/
I'm not a scientist but have worked in biotech in VC, started a venture backed therapeutics company, and worked with the life sci group of a FAANG.
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
[3] https://www.baybridgebio.com/drug_valuation.html
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
[3] https://www.baybridgebio.com/drug_valuation.html
I appreciate the plug to your blog -- subscribed! I really like your latest post about Genentech. I never knew how related they were to Apple.
Thanks! I didn't realize the connection until recently either. Bob Swanson is a very underrated founder