When it comes to people, you don't need much writing of DNA to get really interesting stuff. Nick Bostrom (author of Superintelligence: Paths, Dangers, Strategies) has fleshed-out the idea of iterated embryo selection.[1] IES lets you do the equivalent of a millennia-long human breeding experiment in a couple of months in a lab. The result would be phenotypes that have never existed in history. These people would be smarter and healthier than anyone who lived before. It would utterly change the human condition.
The key enabling technology is the ability to (in-vitro) turn embryonic stem cells into gametes. This has been done in mice, but not humans.
I don't think it's quite as easy. With embryo selection you are selecting genotypes, not phenotypes.
The paper you link is pretty handwavy regarding that point; Bostrom seems tho think that if we just had more data we will be able to find a model that links genotypes and phenotypes. However, this is far from certain. There are too many degrees of freedom in our genome, and there are too many factors that contribute to something generic like "cognitive ability". Each individual contribution is so tiny that the effect is lost in noise, even if we sampled all humans.
And even if someone comes up with a novel statistical method and finds a reasonable model, optimising that model would be pretty dangerous. If you just optimise for one trait, the chances you'll even end up with a viable embryo after a dozen iterations are pretty slim.
There's a lot of selective pressure on cognitive abilities; so there must be reasons why we aren't smarter than we are. These reasons will probably kill your experimental embryo.
> Each individual contribution is so tiny that the effect is lost in noise, even if we sampled all humans.
Maybe for some genes, but for thousands of others, the signal is definitely there. Intelligence is highly heritable –as much as height. Just like height, it's spread across thousands of genes. Yet GWASes have found hundreds (soon to be thousands) of genes for height.[1] If researchers had a quarter of a million pairs of IQs and genomes, they'd find a similar number of genes for cognitive abilities.
> If you just optimise[sic] for one trait, the chances you'll even end up with a viable embryo after a dozen iterations are pretty slim.
That seems highly unlikely. We've done far worse with domesticated animals and they've been quite viable. Some breeds of dog are predisposed to genetic diseases such as hip dysplasia, but that's because we didn't have the ability to screen for genetic defects. (Or more cynically: breeders cared more about looks than diseases.)
When domesticating animals, breeders often found rare traits and exaggerated them. But for human cognitive abilities, the best alleles are already prevalent in the population. It's just that nobody has lucked into all of them at once. And unlike height, there's no square-cube law to disadvantage more cognitive horsepower.
> There's a lot of selective pressure on cognitive abilities; so there must be reasons why we aren't smarter than we are. These reasons will probably kill your experimental embryo.
Humans have definitely been selected for intelligence, but we were also selected for other things such as famine resistance. The tradeoffs are different today, and we can do a much better job than nature. We can fix a lot of mutation load. Really, we're not nearly as smart as we could be. In the words of Nick Bostrom:
> Far from being the smartest possible biological species, we are probably better thought of as the stupidest possible biological species capable of starting a technological civilization—a niche we filled because we got there first, not because we are in any sense optimally adapted to it.
The correlations seen in the the genome-wide-association study are impressive, but I don't see how they would help in this case. If you'd use that data for a model, you'd end up with a model that has thousands of parameters, and each of them has a huge margin of error. Using this data, how precise would your model be? Ie. given a genome, how precisely could you estimate height?
Concerning your comparison with breeding: you seem to misunderstand me. I don't think that breeding per se doesn't work. Breeding by looking at phenotypes works wonderful.
But what you suggest is to just iterate to optimise [according to my spellchecker that is how you spell it] according to a model. You are missing the step where those embryos grow (or fail to grow) into adults. Without this step, your optimisation procedure will drift randomly on every axis that your model doesn't measure.
I have a pet theory that while a modest intelligence advantage is likely to be genuinely beneficial, social animals like humans quickly run into trouble if they stray more than a couple of standard deviations from the average. Imagine living all your life surrounded by idiots (parents, teachers and superiors included), having to explain the stupidest thing in painstaking detail (and regularly being scorned as the one who doesn't get it), and of course having no palatable romantic partners. Down that road lies bitterness, voluntary solitude and maybe substance abuse. Something like http://nautil.us/issue/21/information/the-man-who-tried-to-r...
Imagine living all your life surrounded by idiots (parents, teachers and superiors included), having to explain the stupidest thing in painstaking detail (and regularly being scorned as the one who doesn't get it), and of course having no palatable romantic partners.
I think this is the most accurate description I've ever seen of my pre-university life.
I'd think that as soon as the highly intelligent person becomes more common, that problem will mostly go away. There will be enough geniuses around that they'll be able to form large peer groups in "normal" society, and not just in high-IQ clubs or PhD. circles...
Why would you need an embryo to select a genotype? You don't need to physically embody genomes in embryos just to get iterative selection.... you can do that informatically before you get to the stage of somatic cell nuclear transfer to inject DNA/genome into the cell.
Anders should aspire more towards George Church and less towards Nick Bostrom. (I know he wasn't on the paper, but whatever. It's been on my mind.)
Re: Genotype/phenotype; once we find stable phenotypes, we should make more reliable structures on top of those phenotypes, instead of messing with the genetics. For example, for protein-based molecular nanotechnology, nobody really wants to spend their time doing a trillion different folds in simulation or in the lab just to find their target structure. Instead you will have to skip tweaking genotypes (because of the large costs of exploring the genetic landscape (really, take whatever you can get)) and just use lego brick phenotypes... at least for nano/molecular structures (nanotech). Other changes require exploration of evolutionary/genetic landscape, until heuristics get better if ever, which will continue to be expensive even with $1/genome synthesis costs.
Stable/lego-brick phenotypes: Is this about coming up with something akin to 0 and 1 and then building from the ground up? Or is this about something that's closer to familiar biology but with some fiendishly clever compartmentalization somehow?
Well, given our tremendous struggle with rational design of novel protein nanostructures, my point was that we should avoid that difficulty by designing some ligand-binding proteins only once, then assemble those into the larger structures that we desire.
Of course, knowing all protein (structural) phenotypes for all amino acid sequences up to length=100 would also be nice... but seems unlikely something we can work towards due to constraints of scarce universe.
This strikes me as an extremely bad idea. Putting aside all the issues about whether it is even possible to select genetic traits that will predictably result in higher 'intelligence', how do you even define this outcome? You can only optimise what you can measure, and it seems that this process will just produce people that are good at intelligence tests. At what cost to all other aspects and dimensions of human experience and achievement? How do you prove that this is a good idea for a particular individual?
My opinion: Bostrom has some interesting perspectives, but has always tended to preach pseudoscience to an audience that is entirely non-critical.
Writing genomes, once we can do it, will be the technology that drives this century (sorry, nodejs). Imagine printing a bamboo tree programmed to grow into a bed frame.
i think it would be much easier to just grown the bamboo into the frame, a la the square watermelon. But I digress: we are many, many decades away from being able to do things like that. Possibly centuries. Even programming single-celled organisms to assemble simple hydrocarbons is barely within our reach, at present.
Not sure what you imagine the early 20th century to have been like ... I mean, general relativity just turned 100. Anyways, in terms of our understanding the genetic architecture of even basic traits like height (to say nothing of complicated ones like the morphology of a bamboo shoot) we're practically in the Dark Ages. I know it's a bitter pill to swallow for the "Singularity is near" crowd.
Writing genomes and understanding protein folding will be the gateway to full nanotech. Think about it: proteins are already tiny machines that perform specific functions. If we learn how to make them ourselves the sky's the limit.
Actually... quite possibly. Check out the first slide in [1]. You can basically build genetic logic gates using promoters to conditionally (i.e. depending on the presence of light, a protein, etc.) produce a protein to induce or repress another promoter, thereby increasing or decreasing the expression of another gene.
It's still pretty early stages and there are a lot of factors that make this more complicated (life isn't binary), but the ultimate goal of synthetic biology is to be able to program DNA in the same way you might program a computer.
Yes, DNA (with a combination of some enzymes) is capable of universal computation. So far, this has not turned out to be anything particularly interesting from an engineering standpoint,
Yes, even without enzymes and cells. I have DNA crystallization based tiling systems that can implement essentially arbitrary 1d cellular automata and could implement Turing machines directly. People have implemented neural networks with branch-migration-based systems, and DNA gates can be used to implement systems of logic gates and a wide class of chemical reaction networks. There's a whole field of DNA and molecular computing.
Phenotype = genotype + environment + reproductive phenotype (that is, the seed shell for a plant, egg for a bird, or the uterus for a mammal). Being able to rewrite the genotype doesn't give you total customization of the resulting phenotype.
DNA seems to be strongly compressed code that's also used as data. But we're not even sure if it encodes everything there is about an organism. Keep in mind that organisms aren't made in a single factory, they build child organisms, which then build further organisms, etc., creating a chain. You can imagine it as being a single runtime, accumulating state. It is possible that, e.g. replication machinery gets altered in such a way that it builds altered copies - the information about this alteration may had disappeared from DNA or never be there in the first place, and yet the alteration propagates.
TL;DR: read "Reflections on Trusting Trust", the same phenomena applies to biology.
Simplistic, but a good way to think of the difference between genetic engineering and synthetic biology. Genetic engineering is like reverse engineering: poking around in an executable to change bits of the assembler and see what happens. Synthetic biology is attempting to build a top-down approach for genetic designs: specification, design, implementation, testing - and in that model, the DNA is the machine code and we are indeed trying to make a compiler.
If only it were that simple. We're very far away from understanding how a particular DNA string relates to phenotypes. All we've found to date is a bunch of configuration bits and maybe a general idea of how things might be related to each other but not much more than that.
The gap in understanding between 'here are a bunch of bits that appear to do 'x'' and 'x' is vast, far larger than the gap between say being able to build a compiler to have an executable perform in a certain way.
If anywhere that is where the real breakthrough will be, in being able to effectively map the chain of binary bits in a genome to a certain phenotype in a mature organism without any 'leftover bits'.
Once you have that chain of causality mapped out in enough detail to clearly link all causes with their effects you can start engineering something to act in a way that is far outside what we normally find in nature.
I'm not sure if I'm expressing myself clearly about all this, maybe someone more versed in the matter can chime in but the path between DNA and organism is a lot longer and less well understood than the path between a low level language program and the resulting output of that program. Just having a 'compiler' will not change much about that understanding, though once we have that compiler we may be able to advance our understanding by a more direct approach in modifying pieces of an existing genome to observe the effects in the organism. For sure it will speed things up.
edit: if we did have that understanding it would be trivial to create a digital representation of a (simple) organism from it's sequenced DNA. The fact that we are nowhere near capable of doing this is a simple proof that merely being able to create a certain sequence of bits will not give us the capability to create organisms to order.
Depends on what type of organisms are we talking about. We do have enough understanding to target bacteria with a "compiler" approach. That's the gist of synthetic biology and the stuff that's being done around iGEM competition.
Bacteria are "simple" enough that we can just append some DNA sequences and have them develop stuff we want. It's predictable enough that people already created "standard library" of DNA blocks - so people can work with bacteria in a kind of dataflow environment, with underlying DNA representation hidden. You connect a "molecule sensor" block with an "amplifier" block, an "inverter" block and a "light up" block, and you get bacteria that start to glow in presence of some metal, etc.
But of course, once we move to the level of multicellular organisms, the complexity explodes. Here, as you say, we basically know that something in an unaltered system sorta correlates with a particular phenotype and if we change it then maybe it won't blow up.
Sorry, yes, I was assuming 'multi-cellular organisms' as the context because of the bamboo rake comment at the beginning, but you're absolutely right that for single cells the situation will be different. Even so, if we're going to expand the scope downward we might as well go all the way, viruses, arguably even more simple than single celled organisms are still full of surprises.
>Just having a 'compiler' will not change much about that understanding
The compiler is called "genetic engineering" and it's a 30 years old field. In particular genetic engineering allows you to knockout specific genes and observe the phenotype. This technique is commonly used on bacteria, plants and animals. There is an ongoing mouse knockout project, an attempt to study every mouse gene by turning it off. It is 50% complete so far. By the way, the experiments are performed mostly by hand.
>We're very far away from understanding how a particular DNA string relates to phenotypes.
This problem can be solved much faster with a large scale effort. Thousands of automated experiments can run in parallel while recording the phenotype of the animals (including results of behavioral tests) and storing it for later analysis.
On this genotype->phenotype dataset one can train an ML model. Actually there is a company that is trying this approach (just ML part, without automated experiments) http://www.deepgenomics.com/
I doubt that one company will be enough, though. With serious funding there could be much more progress in this area. We could have a full knockout map of mouse genome in less than a decade if we really wanted.
I think right now, your best approach to growing bamboo bed frames would be to a) first make a frame-shaped template, and b) make bamboo cells grow according to the template. Encoding it in just cell DNA is beyond our current state of knowledge and probably insanely tricky - Fourier-transforming a square and encoding it into chemical feedback loops and encoding that into DNA kind of tricky.
bamboo is already a fine construction material, it's much more efficient to just grow long straight pieces and join them after the fact. That way you don't have to do any genome engineering, or have special growth facilities for oddly shaped bamboo (for one, bamboo that doesn't grow straight up is going to take a lot more space on the ground).
Synthesis is still quite expensive at a penny a base, much less a dime a base. To give a comparison to sequencing, costs are about a penny a megabase (~7 orders of magnitude cheaper). We have a long way to go.
Even for a small bacterial genome (1Mbp) that's $10,000 at a penny a base. To have any real power, I'd want to build at least thousands of them which is a little out of my price range.
1) In practice you're not synthesizing an entire bacterial genome; you're only synthesizing the small bit you're patching along with whatever the retrovirus needs. The bacteria are already pretty great at copying their base DNA on their own. : P
2) DNA synthesis has been tracking Moore's law for a number of years, and doesn't show the signs of slowing down anytime soon unlike transistors.
Sorry, it's short for basepair (bp) of DNA (eg a, c, t, or g). Currently it costs about ten cents a bp to synthesize a particular stretch of sequence from one of the companies in the article (dime a base). An average protein can be encoded by 1 kilo basepair, a small bacterial genome by 1 mega basepair, and a human genome by 6 giga basepair.
Maybe the confusion here is that prices are sometimes spoken of as 'a [currency unit] a [item]' which can be understood as 'one [currency unit] per [item]'.
DNA has been proposed as a long term digital storage medium. Magnetic and optical digital storage generally last less than a decade. You need to copy annually to be sure.
As others pointed out, writing DNA efficienty is the bottleneck. Reading is getting cheap.
I'm a bit scared, so far I've never read anything leading me to believe we can understand organic complexity. To my eyes writing DNA will only be a spectacular show of nopes.
so far I've never read anything leading me to believe we can understand organic complexity
That's a bit hyperbolic, isn't it? We understand the basics many organic systems and our knowledge and tool sets are improving at an accelerating rate. Look at the recent gene therapy used to cure leukemia:
http://www.techtimes.com/articles/104545/20151109/babys-leuk...
To my eyes writing DNA will only be a spectacular show of nopes
But we'll be able to learn so much from the failures. We'll finally have the coding and debugging tools to make a serious stab at reverse engineering life.
Right, hyperbole it is. I understand that research is making very large steps these days. But too often when things get too cheap, people use it without deep enough thoughts, with something as large as an organism.... Cheap rant from my side but that's my belief.
The sooner we get real insights and are able to find new cure the better. We'll see.
Sequencing will only be truly "here" when companies like 23andme offer both sequencing and interpretation of human genome directly to consumers for $1k or less. Preferably much less.
Great question! I'm assuming that if AI and genetic engineering hit their stride at the same time, copyright is the least of our concerns.
We engineer machine intelligence just enough for it to spark (tipping point, runaway, whatever you want to call it), and then it'll engineer us "better". Is "better" what we consider better? Or what it considers better? Interesting times.
Or, rather, how does copyright work for genetic designs? Syn Bio papers are published all the time, and their designs are copyright like any other invention.
The key enabling technology is the ability to (in-vitro) turn embryonic stem cells into gametes. This has been done in mice, but not humans.
1. http://www.nickbostrom.com/papers/embryo.pdf