I'm not the biggest fan of Thiel's thinking, but this line shows that the two founders did their job very well:
>They convinced me there is a surprising amount that has gone wrong with the cancer cell lines people have been studying
This sounds that they managed to explain to him how much is going wrong with HeLa cells (I assume he's talking about HeLa - these are the most widely used).
They're so sturdy that they contaminate everything to the point that some well-known, well-used cancer cell lines turn out to be nothing but HeLa contamination [1]. There's ethical problems as the original cells were taken from Henrietta Lacks without her consent (this is slowly being rectified, have a look at "The Immortal Life Of Henrietta Lacks"). HeLa cells have mutated so much in culture that they have little in common with human cells [2]. I wonder which "human cancer" cells they graft into mice.
All together, this sounds like the founders did their homework on science communication. To scientists this highlights the importance of communication - it's not "just" useful for talking to "the public about the importance of your work", it can make you some serious money!
The fact that they managed to convince Mr. Thiel that they have a unique approach indicates as much about his lack of education in biology as it does their ability to communicate.
The problems with modeling human disease using "found" cancer cell lines (of which HeLa is just one of hundreds) have been widely-understood for well more than a decade. These cells have absurd, fragmented genomes with bizarre and variable chromosomal copy numbers. They have lived in culture for sometimes many decades, under strange evolutionary pressures. They provide models in which we can explore human-like cells and regulatory pathways, but it is well-acknowledged in the field that these are not sufficient to model a generic form of cancer, much less a particular patient's cancer.
Thiel is approaching biotech with the same blindness with which older investors approached investment in datatech during the dot-com bubble. There is no more reason to believe that this startup will corner the market on xenograft-based research into cancer therapeutics than there was to believe that pets.com would become the be-all and end-all of everything pet related online. But this comes back to your point, that the founders have done a fantastic job of communication. I hope they are communicating the truth, and not just their dream. It will be a big fall if it is the latter.
As the article is a bit short on details we can only grasp at what they exactly do different - maybe they're not doing anything unique, maybe he's just impressed with how they're using "known" methods to minimize risks/randomness.
I really can't tell from the text what kind of cells they use and how random these are, or how novel their method is (if it is even novel).
Yeah, Thiel seems quite ignorant in this article unfortunately.
I was aware of the HeLa contamination issues and ultramarathon-culture length, but I haven't heard the bit about the extreme genomic abnormalities in HeLa (I haven't worked with HeLa cells) and it sounds really interesting, can you speak a bit more about the specifics?
I find this a very inspiring line of thought for two reasons. 1) Philosophical. What if we could get rid of randomness in our lives? Can we do 'end-to-end' assembly with our health, owning all stages of the process, like Elon Musk does with Tesla Cars? And in the end, will this give you more happiness or more stress? 2) Business. Getting rid of randomness is important for the growth of our startup. This interview gets me thinking: what randomness can we cut out of our business?
It's basically just thermodynamics: if you're willing to spend enough energy, you can optimize the state an arbitrarily large system as you please.
Of course, the key words there are enough energy. It sounds like Thiel might be forgetting to account for what we want to optimize and how much effort we're willing to spend on it.
"System" -- any bloody thing you care about, any collection of mass and energy.
"Optimize" -- to constrain the system to within a subset of its state-space. Or in other words, to make only some things happen rather than all other possible things.
Generally, as long as something is physically possible, you can spend some amount of energy to make it reasonably probable or unreasonably improbable. This may just be far more energy than you happen to have, or care to spend.
It doesn't. It's really a deliberately vague term referring to any physical action designed to put the system in one state rather than another. A thermodynamically non-reversible action, you could say.
If the discussion is about gaining an increasing measure of understanding and an ability to control biological systems for particular purposes, sure, that certainly makes sense. And, yes, manufacturing when you look at it closely is really all about mitigating variation (randomness) with the understanding that everything is a trade-off. However, to stretch that into the idea of "eliminating randomness" in our lives is absolutely asking to be bitch-slapped by philosophers and scientists--- that's something that money-people like Thiel may need from time-to-time (its not a bad thing).
Even with using xenografts instead of cell lines there's plenty of stuff that can go wrong. Animal models are maybe closer to humans than cell lines, but they're still far from perfect.
Derek Lowe posted about this a few days ago on his In The Pipeline blog:
But their rationale is that trying to culture CSCs in vitro runs the risk of having them change character too much, making any assays using them unreliable. My guess is that they’re right about that, but my worry is that xenograft tumors themselves are already unreliable enough to cause trouble (and I have no idea of what happens to them after you “passage” them through multiple animals). Xenograft models are, of course, well known in oncology drug discovery, but one of the things that’s well known about them is that they’re the pure example of “necessary but nowhere near sufficient”.
> technology is trying to overcome the randomness that is nature
This is a major reason why consumer electronics and information technology have enabled so many more rapidly scalable businesses in recent decades than other industries (hence "We wanted flying cars, instead we got 140 characters").
The technology industry is uniquely founded on physical phenomena that are deterministic, even at high speed or recursive complexity (CPUs, storage, networking, displays, peripherals, etc). Therefore, if you can think of something in a mathematical fashion, you know that it can be built. This essentially eliminates technical feasibility risk - rather than worrying about whether a product can be built, you can focus on what should be built and why.
Well, CPUs don't grow on trees. We got to the point of predictability through the same rocky, unpredictable process of eliminating randomness (a.k.a. science).
I don't mean the fabrication of those components, I mean their operation.
In living organisms, eliminating randomness in their operation would change their intended behavior in several ways - randomness is a feature, not a bug, in living organisms.
There is another incredibly interesting meta-discussion implicit in that article. How does someone very intelligent like Peter Thiel make investment decisions in an area where he lacks a huge amount of background knowledge?
He can obviously evaluate the team, he can ask experts he trusts to give him their opinion about the value of the approach, but... biotech start ups, far more than software start ups, live or die by the strength of their technology. Transplants have far fewer false positives than working with cell lines (especially HeLa!) - but they are also several orders of magnitude more expensive. Is the trade off between decreasing false positives versus increase in experiment costs worth it? That company obviously think so, but it's not a no brainer, and it's very difficult to seriously evaluate. Mice models are very expensive - and they are better than HeLa, but they aren't foolproof. In cancer biology, there's a continuum between how expensive a technique is, and how much it recapitulates the actual biology of the disease. The sweet spot also varies hugely from disease to disease.
Another point which I find fascinating, is that more or less everyone who has worked on cancer biology knows that HeLa are not really a realistic model... yet people keep using them, why? Because a lot of researchers are interested in publishing papers in prestigious journals, not discovering robust findings. A pharma company instead has a lot more incentive in getting things right - because a splashy result that gets falsified at phase 1 is an incredibly expensive failure, while it's still a Nature paper for a professor. I don't think that academia should exist to serve pharma companies, but the problem of false positives is very real, and all the incentives around the publishing/grant system make it much worse. We should be rewarding researchers that get moderate but robust results that hold up under scrutiny, not people who make incredibly splashy discoveries that later turn out to be incredibly narrow and difficult to reproduce.
There are no completely accurate tumor models. The best a tumor model can do for a group is provide a standardized disease progression to test against.
This is why in mouse tumor models there are a lot of highly aggressive tumor models that are meant to rapidly kill the host-- if you can show even a 5% reduction in tumor size or 5% longer life of the mouse, it's impressive.
You look at the technology from first principles. The idea of isolating cancer stem cells is not new. Identifying them, culturing them (sequencing them) and using them to graft tumor into a xenohost are all interesting technologies themselves. These new models themselves seem to be very valuable and better than what exists currently.
Uhh, I think Thiel doesn't understand what is going on here. I am going to respond to a few excerpts.
"The question is, can you change those probabilities into different numbers? The reason we invested in Stemcentrx at a valuation that would have been higher than many other biotechs we looked at is that we felt the whole company was designed to get these probabilities as close to one as possible at every step, to get rid of as much of this randomness or contingency as possible. That is something that we found deeply reassuring."
Broadly speaking there are two strategies for drug research: shotgun, and sniper rifle. If we're being honest, shotgun is the dominant method specifically because 10 chances is far safer than 1 "really good" shot that may or may not produce a really good drug. The one shot wonder shops usually fail early stage when they can't produce a chain of positive data from one funding round to the next. Biotech startups are longshots-- I would say that they are probably even longer shots than a software shop specifically because there can be unknown physical constraints of biology that can silently ruin an otherwise great idea, leaving other people to make the exact same mistake repeatedly after you're gone. Reducing inconsistency of your results is a core of the scientific method, practiced everywhere, but doing so effectively is hard when you don't have full understanding of any one component you work with. It isn't random behavior, it's just not fully understood behavior.
"One of the very unusual things they do is graft human cancer into the mice. It’s a somewhat more expensive way to do this than studying cancer in cell culture. It’s a somewhat harder structure to build. But drugs tested this way are much more likely to work in humans. They convinced me there is a surprising amount that has gone wrong with the cancer cell lines people have been studying. "
Literally everyone who is serious about making oncology drugs does this technique. It's a staple. I guess it's "unusual" if you aren't accustomed to the methods of biotechnology, but rest assured, this trick has been well-known for quite a while-- it is not a magic bullet, but it's better than alternatives. Cancer cell lines are known to be poor models in a number of dimensions, which is great because we can anticipate their shortcomings. Mouse models also have shortcomings that are somewhat well understood, but don't confuse this for a slam dunk. There are still many (most?) cases in which drugs developed from mice won't transfer to humans.
"But if biotech companies tend to invest money in ways that are pseudo random, then a lot of it must get wasted. You end up doing things where you say, “I am not sure it’s going to work.” Well, that sounds like a wasteful thing to do. The standard excuse that biotech companies have is that, “We don’t know if it’s going to work, so we have to do it this way.” That has to be inefficient."
Is this guy serious? You're never sure if it's going to work in the laboratory! Research is mostly "try something new, then try another new thing when that fails." It's "wasteful" because it's guided trial and error, and you don't have full totalitarian control over any of the variables. If you think you are investing in a new biotech company (or old, really) that has escaped this paradigm, I have a series of pyramids to sell you.
"This idea was very much in my mind by the time we invested in Stemcentrx in 2012. They had an annoyingly complicated problem, all these pieces you have to bring together, and they said, “We are just going to do it ourselves.” That is a mind-set that I very deeply share. I don’t want to name names, but there are other companies where, in some ways, this was the key thing that failed."
All biotech problems are annoyingly complicated, and the prerequisites for trying to solve problems are also ann...
> You're never sure if it's going to work in the laboratory! Research is mostly "try something new, then try another new thing when that fails." It's "wasteful" because it's guided trial and error, and you don't have full totalitarian control over any of the variables. If you think you are investing in a new biotech company (or old, really) that has escaped this paradigm, I have a series of pyramids to sell you.
This is an application of Thiel's philosophy of making contrarian bets. From his perspective, if you weren't incredulous about Stemcentrx in the way you are, then he would have made the mistake of investing in something incremental. Whether his contrarian bet turns out to be correct is something that remains to be seen.
> Nature isn't random! Nature is complex, cryptic, self-referential, and chaotic, but it's deterministic at the level of biology.
There are certainly many seemingly random aspects of biology that are actually just a lack of understanding at the moment, but that doesn't mean there is no fundamentally random behavior in biological systems.
The concept you're looking for is Stocastic [1]. The distinction between being 'stocastic' and 'random' is one of 'unpredictable & determined' vs ' unpredictable & undetermined'. Cell biology deals with (and utilizes) a few stocastic processes, but that doesn't make those processes random. An interesting test to differentiate the two (at least in biology) is what happens when you run the simulation backwards - if the occurrence of an event is still unpredictable in reverse, then it's 'random', if every causal incident can be traced, it would generally be considered stochastic.
Sorry, that was not a well-written wording. The intention was to demonstrate the concept of 'predictability' not whether it was actually predictable. Edited and fixed now.
Huh. It seems different scientific fields use the word 'stochastic' in very different ways.
The commenter, 'bgins' makes the point, "Random has many connotations (like entropy), not at all equivalent, and is a more generic term usable outside mathematics. Stochastic means nondeterministic or unpredictable. Random generally means unrecognizable, not adhering to a pattern. A random variable is also called a stochastic variable."
And user, 'Einnocent,' "Likewise, I've noticed that network theory tends to refer to traffic as being stochastic. Such traffic, from the point of view of a router, would be considered random, but of course each packet was deterministically produced."
In the biochemical field (of which I'm familiar, though ignorant of the others), I doubt anyone would say that gene expression is 'random', rather it is 'stochastic', and their intention in choosing those words distinctly would indicate that there are not just probabilistic, but physical limits to the expression of a gene that constrain its probability space. And further, there are generally deterministic (if unpredictable (though not entirely independent) from to state) mechanisms which ultimately lead to gene expression. Were I to say that the expression of gene A is 'random,' then it would seem to imply that there is some minute chance that the result is a billion copies of the gene, when physically, it might be impossible for the cell to exist with that many proteins in it - or that the state of gene expression at time t0 is entirely independent of expression at time t1, which is also untrue. Both of these interpretations of biological processes are misleading and wrong, though they are what is conjured when it is said that, "gene expression is random." All the while, the number of copies is actually dependent on knowable, measurable, if highly-noisy processes - what I've know to be termed 'stochastic'.
> Were I to say that the expression of gene A is 'random,' then it would seem to imply that there is some minute chance that the result is a billion copies of the gene
Not at all (in mathematical terms, anyway). You can certainly define the probability distribution of a random variable such that only certain values are possible. This is known as its support: https://en.wikipedia.org/wiki/Support_(measure_theory)
If Thiel were being contrarian, he's invest in a biotech company that WAS merely trying to iterate incrementally-- it will be very hard for him to find such a company that actually needs funding, as only big pharma is interested in that kind of iteration. Biotech startups are almost exclusively (to my knowledge, exclusively) long shots with potential large upswings. Nobody is working on incremental improvements. Emphasizing a company that claims it won't have any failures or waste is probably indicative of ignorance rather than some grand enlightened-investment stratagem.
> Biotech startups are almost exclusively (to my knowledge, exclusively) long shots with potential large upswings.
And it's these sorts of long shots that he wants to avoid. He thinks that Stemcentrx has more certainty in its outcome than other biotechs, despite also having a potentially large upswing.
Like you, I was not particularly impressed by Stemcentrx from the information in the article. But neither of us have any other visibility into what they're actually doing. Perhaps it will end up as another Halcyon Molecular, or perhaps it will live up to its hype - only time will tell. Given that Thiel has the money (and hopefully the humility) to hire experts who know far more about this topic than him (or either of us, for that matter), I hope that things work out - patients and investors alike will benefit.
>the randomness that I think of as the evil part of nature.
This is a perfect example of why Thiel's thinking is so unappealing to me. First of all, nature is not random, but there is a lack of understanding of the true implications of cause and effect of what goes on at a biological (and ecological) level that it may appear that way to people so captivated by the binary control of the programming world.
But, even if it were random, I don't think that's evil in the least. It's unpredictable, yes, but so are the implications of many of the technologies that Thiel embraces. That does not make those technologies inherently evil, just as "randomness" is not an evil part of nature.
I am afraid of the sterile world that Thiel seems to want.
Additionally, I'd like to point out this bit from a below comment where the poster responded to several article excerpts, including: "I think of aging and maybe just mortality as random things that go wrong. The older you get, the more random things happen, the more breaks. If it’s not cancer, you could get hit by an asteroid. So on some level, technology is trying to overcome the randomness that is nature. That is a question on the level of a company. Can you get rid of randomness in building a company? But the philosophical version of the question is whether we can get rid of randomness in its entirety and overcome the randomness that I think of as the evil part of nature."
Nature isn't random! Nature is complex, cryptic, self-referential, and chaotic, but it's deterministic at the level of biology. "Random things happening" is not a proper understanding of age-related decline; aging is the long-term compounding interest of metabolic byproducts. It really stuns me that Thiel's understanding of these things is so apathetic to reality.
Biotech companies don't risk so much randomness because they like it that way. It's because without most of the randomness there is no possibility of breakthroughs.
Still, optimizing experiment design through a balancing of bayesian statistics, simulation and cost optimization may result in savings or less capital risk.
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[ 4.0 ms ] story [ 81.8 ms ] thread>They convinced me there is a surprising amount that has gone wrong with the cancer cell lines people have been studying
This sounds that they managed to explain to him how much is going wrong with HeLa cells (I assume he's talking about HeLa - these are the most widely used).
They're so sturdy that they contaminate everything to the point that some well-known, well-used cancer cell lines turn out to be nothing but HeLa contamination [1]. There's ethical problems as the original cells were taken from Henrietta Lacks without her consent (this is slowly being rectified, have a look at "The Immortal Life Of Henrietta Lacks"). HeLa cells have mutated so much in culture that they have little in common with human cells [2]. I wonder which "human cancer" cells they graft into mice.
All together, this sounds like the founders did their homework on science communication. To scientists this highlights the importance of communication - it's not "just" useful for talking to "the public about the importance of your work", it can make you some serious money!
[1] http://retractionwatch.com/2013/03/11/more-hela-problems-for...
[2] http://blogs.biomedcentral.com/on-biology/2013/09/17/debatin...
The problems with modeling human disease using "found" cancer cell lines (of which HeLa is just one of hundreds) have been widely-understood for well more than a decade. These cells have absurd, fragmented genomes with bizarre and variable chromosomal copy numbers. They have lived in culture for sometimes many decades, under strange evolutionary pressures. They provide models in which we can explore human-like cells and regulatory pathways, but it is well-acknowledged in the field that these are not sufficient to model a generic form of cancer, much less a particular patient's cancer.
Thiel is approaching biotech with the same blindness with which older investors approached investment in datatech during the dot-com bubble. There is no more reason to believe that this startup will corner the market on xenograft-based research into cancer therapeutics than there was to believe that pets.com would become the be-all and end-all of everything pet related online. But this comes back to your point, that the founders have done a fantastic job of communication. I hope they are communicating the truth, and not just their dream. It will be a big fall if it is the latter.
I really can't tell from the text what kind of cells they use and how random these are, or how novel their method is (if it is even novel).
I was aware of the HeLa contamination issues and ultramarathon-culture length, but I haven't heard the bit about the extreme genomic abnormalities in HeLa (I haven't worked with HeLa cells) and it sounds really interesting, can you speak a bit more about the specifics?
Does it make sense to pursue impossible goals? If not, does it make sense to philosophize about pursuing impossible goals?
Of course, the key words there are enough energy. It sounds like Thiel might be forgetting to account for what we want to optimize and how much effort we're willing to spend on it.
Define "optimize" and "system"?
"Optimize" -- to constrain the system to within a subset of its state-space. Or in other words, to make only some things happen rather than all other possible things.
Generally, as long as something is physically possible, you can spend some amount of energy to make it reasonably probable or unreasonably improbable. This may just be far more energy than you happen to have, or care to spend.
I have no clue about physics, so I'm just curious.
Derek Lowe posted about this a few days ago on his In The Pipeline blog:
But their rationale is that trying to culture CSCs in vitro runs the risk of having them change character too much, making any assays using them unreliable. My guess is that they’re right about that, but my worry is that xenograft tumors themselves are already unreliable enough to cause trouble (and I have no idea of what happens to them after you “passage” them through multiple animals). Xenograft models are, of course, well known in oncology drug discovery, but one of the things that’s well known about them is that they’re the pure example of “necessary but nowhere near sufficient”.
http://blogs.sciencemag.org/pipeline/archives/2015/09/09/cha...
This is a major reason why consumer electronics and information technology have enabled so many more rapidly scalable businesses in recent decades than other industries (hence "We wanted flying cars, instead we got 140 characters").
The technology industry is uniquely founded on physical phenomena that are deterministic, even at high speed or recursive complexity (CPUs, storage, networking, displays, peripherals, etc). Therefore, if you can think of something in a mathematical fashion, you know that it can be built. This essentially eliminates technical feasibility risk - rather than worrying about whether a product can be built, you can focus on what should be built and why.
In living organisms, eliminating randomness in their operation would change their intended behavior in several ways - randomness is a feature, not a bug, in living organisms.
He can obviously evaluate the team, he can ask experts he trusts to give him their opinion about the value of the approach, but... biotech start ups, far more than software start ups, live or die by the strength of their technology. Transplants have far fewer false positives than working with cell lines (especially HeLa!) - but they are also several orders of magnitude more expensive. Is the trade off between decreasing false positives versus increase in experiment costs worth it? That company obviously think so, but it's not a no brainer, and it's very difficult to seriously evaluate. Mice models are very expensive - and they are better than HeLa, but they aren't foolproof. In cancer biology, there's a continuum between how expensive a technique is, and how much it recapitulates the actual biology of the disease. The sweet spot also varies hugely from disease to disease.
Another point which I find fascinating, is that more or less everyone who has worked on cancer biology knows that HeLa are not really a realistic model... yet people keep using them, why? Because a lot of researchers are interested in publishing papers in prestigious journals, not discovering robust findings. A pharma company instead has a lot more incentive in getting things right - because a splashy result that gets falsified at phase 1 is an incredibly expensive failure, while it's still a Nature paper for a professor. I don't think that academia should exist to serve pharma companies, but the problem of false positives is very real, and all the incentives around the publishing/grant system make it much worse. We should be rewarding researchers that get moderate but robust results that hold up under scrutiny, not people who make incredibly splashy discoveries that later turn out to be incredibly narrow and difficult to reproduce.
This is why in mouse tumor models there are a lot of highly aggressive tumor models that are meant to rapidly kill the host-- if you can show even a 5% reduction in tumor size or 5% longer life of the mouse, it's impressive.
"The question is, can you change those probabilities into different numbers? The reason we invested in Stemcentrx at a valuation that would have been higher than many other biotechs we looked at is that we felt the whole company was designed to get these probabilities as close to one as possible at every step, to get rid of as much of this randomness or contingency as possible. That is something that we found deeply reassuring."
Broadly speaking there are two strategies for drug research: shotgun, and sniper rifle. If we're being honest, shotgun is the dominant method specifically because 10 chances is far safer than 1 "really good" shot that may or may not produce a really good drug. The one shot wonder shops usually fail early stage when they can't produce a chain of positive data from one funding round to the next. Biotech startups are longshots-- I would say that they are probably even longer shots than a software shop specifically because there can be unknown physical constraints of biology that can silently ruin an otherwise great idea, leaving other people to make the exact same mistake repeatedly after you're gone. Reducing inconsistency of your results is a core of the scientific method, practiced everywhere, but doing so effectively is hard when you don't have full understanding of any one component you work with. It isn't random behavior, it's just not fully understood behavior.
"One of the very unusual things they do is graft human cancer into the mice. It’s a somewhat more expensive way to do this than studying cancer in cell culture. It’s a somewhat harder structure to build. But drugs tested this way are much more likely to work in humans. They convinced me there is a surprising amount that has gone wrong with the cancer cell lines people have been studying. "
Literally everyone who is serious about making oncology drugs does this technique. It's a staple. I guess it's "unusual" if you aren't accustomed to the methods of biotechnology, but rest assured, this trick has been well-known for quite a while-- it is not a magic bullet, but it's better than alternatives. Cancer cell lines are known to be poor models in a number of dimensions, which is great because we can anticipate their shortcomings. Mouse models also have shortcomings that are somewhat well understood, but don't confuse this for a slam dunk. There are still many (most?) cases in which drugs developed from mice won't transfer to humans.
"But if biotech companies tend to invest money in ways that are pseudo random, then a lot of it must get wasted. You end up doing things where you say, “I am not sure it’s going to work.” Well, that sounds like a wasteful thing to do. The standard excuse that biotech companies have is that, “We don’t know if it’s going to work, so we have to do it this way.” That has to be inefficient."
Is this guy serious? You're never sure if it's going to work in the laboratory! Research is mostly "try something new, then try another new thing when that fails." It's "wasteful" because it's guided trial and error, and you don't have full totalitarian control over any of the variables. If you think you are investing in a new biotech company (or old, really) that has escaped this paradigm, I have a series of pyramids to sell you.
"This idea was very much in my mind by the time we invested in Stemcentrx in 2012. They had an annoyingly complicated problem, all these pieces you have to bring together, and they said, “We are just going to do it ourselves.” That is a mind-set that I very deeply share. I don’t want to name names, but there are other companies where, in some ways, this was the key thing that failed."
All biotech problems are annoyingly complicated, and the prerequisites for trying to solve problems are also ann...
This is an application of Thiel's philosophy of making contrarian bets. From his perspective, if you weren't incredulous about Stemcentrx in the way you are, then he would have made the mistake of investing in something incremental. Whether his contrarian bet turns out to be correct is something that remains to be seen.
> Nature isn't random! Nature is complex, cryptic, self-referential, and chaotic, but it's deterministic at the level of biology.
I don't think this is exactly true. For example: https://en.wikipedia.org/wiki/Cellular_noise
There are certainly many seemingly random aspects of biology that are actually just a lack of understanding at the moment, but that doesn't mean there is no fundamentally random behavior in biological systems.
[1] https://en.wikipedia.org/wiki/Stochastic
(edit to fix wording)
The first sentence on the page you linked to suggests otherwise:
> The term stochastic occurs...to describe events or systems that are unpredictable
Also from that page you linked:
> a purely stochastic system is one whose state is randomly determined...it can be classified as non-deterministic (i.e., "random")
Also: http://math.stackexchange.com/questions/114373/whats-the-dif...
> A variable is 'random'. A process is 'stochastic'. Apart from this difference the two words are synonyms
The commenter, 'bgins' makes the point, "Random has many connotations (like entropy), not at all equivalent, and is a more generic term usable outside mathematics. Stochastic means nondeterministic or unpredictable. Random generally means unrecognizable, not adhering to a pattern. A random variable is also called a stochastic variable."
And user, 'Einnocent,' "Likewise, I've noticed that network theory tends to refer to traffic as being stochastic. Such traffic, from the point of view of a router, would be considered random, but of course each packet was deterministically produced."
In the biochemical field (of which I'm familiar, though ignorant of the others), I doubt anyone would say that gene expression is 'random', rather it is 'stochastic', and their intention in choosing those words distinctly would indicate that there are not just probabilistic, but physical limits to the expression of a gene that constrain its probability space. And further, there are generally deterministic (if unpredictable (though not entirely independent) from to state) mechanisms which ultimately lead to gene expression. Were I to say that the expression of gene A is 'random,' then it would seem to imply that there is some minute chance that the result is a billion copies of the gene, when physically, it might be impossible for the cell to exist with that many proteins in it - or that the state of gene expression at time t0 is entirely independent of expression at time t1, which is also untrue. Both of these interpretations of biological processes are misleading and wrong, though they are what is conjured when it is said that, "gene expression is random." All the while, the number of copies is actually dependent on knowable, measurable, if highly-noisy processes - what I've know to be termed 'stochastic'.
Not at all (in mathematical terms, anyway). You can certainly define the probability distribution of a random variable such that only certain values are possible. This is known as its support: https://en.wikipedia.org/wiki/Support_(measure_theory)
And it's these sorts of long shots that he wants to avoid. He thinks that Stemcentrx has more certainty in its outcome than other biotechs, despite also having a potentially large upswing.
Like you, I was not particularly impressed by Stemcentrx from the information in the article. But neither of us have any other visibility into what they're actually doing. Perhaps it will end up as another Halcyon Molecular, or perhaps it will live up to its hype - only time will tell. Given that Thiel has the money (and hopefully the humility) to hire experts who know far more about this topic than him (or either of us, for that matter), I hope that things work out - patients and investors alike will benefit.
This is a perfect example of why Thiel's thinking is so unappealing to me. First of all, nature is not random, but there is a lack of understanding of the true implications of cause and effect of what goes on at a biological (and ecological) level that it may appear that way to people so captivated by the binary control of the programming world.
But, even if it were random, I don't think that's evil in the least. It's unpredictable, yes, but so are the implications of many of the technologies that Thiel embraces. That does not make those technologies inherently evil, just as "randomness" is not an evil part of nature.
I am afraid of the sterile world that Thiel seems to want.
Additionally, I'd like to point out this bit from a below comment where the poster responded to several article excerpts, including: "I think of aging and maybe just mortality as random things that go wrong. The older you get, the more random things happen, the more breaks. If it’s not cancer, you could get hit by an asteroid. So on some level, technology is trying to overcome the randomness that is nature. That is a question on the level of a company. Can you get rid of randomness in building a company? But the philosophical version of the question is whether we can get rid of randomness in its entirety and overcome the randomness that I think of as the evil part of nature." Nature isn't random! Nature is complex, cryptic, self-referential, and chaotic, but it's deterministic at the level of biology. "Random things happening" is not a proper understanding of age-related decline; aging is the long-term compounding interest of metabolic byproducts. It really stuns me that Thiel's understanding of these things is so apathetic to reality.
Still, optimizing experiment design through a balancing of bayesian statistics, simulation and cost optimization may result in savings or less capital risk.