There's a couple of things that are slowly changing in the thinking of biologists related to this:
1. Genes are not loci. The "one gene = one protein" dogma of molecular biology (expanded: "one trait selectable in breeding experiments = one compact locus of the chromosome") was a priori wrong, but it's taken us decades to undo the damage. We were led astray because there are traits that do map to a single locus, or single mutations that were found to be able to control a trait, which is not the same thing as the trait.
2. Distributed representation. If I point to a sentence and ask "where is the sarcasm?" (assuming the sentence is sarcastic), there is no answer. It's certainly a trait of the sentence, just as, say, being red headed is a trait of the organism. But a linear model showing each word's contribution to sarcasm isn't helpful.
3. Perhaps a corollary of (2), humans have the concept of abstraction. There are many situations where you can get something like an abstraction from evolution. If a human engineered it, we'd call it a leaky abstraction, but please don't get caught up on that. Evolution doesn't abstract. But there are structures that emerge that can have similar properties, especially from repeated exaptation. Consider the MAP kinase pathways. Lots and lots of cell responses involve them, in all kinds of subtly different ways. We don't try to claim that a MAP kinase is the gene for anything in particular, any more than we would claim that the interrupt for triggering a system call on 32 bit Linux is the cause of certain behavior that a program is supposed to have in a particular domain.
Re: 1: It wasn’t wrong. There are Mendelian traits and complex traits. We shouldn’t forget we’ve basically tracked down most Mendelian diseases since the human genome was published. Still some to go but we will hopefully have all soon. This is a huge accomplishment.
Complex/quantitative traits on the other hand have been known for quite some time. I don’t think it’s anything new, but the landscape is becoming increasingly clear thanks to large scale GWAS and analyses from many groups including Pritchard’s.
I've spent a little time considering what this class of things would be. It's like trying to turn a forgetful functor into a homotopy transformation and having some kind of universal measure of the distance from the end of the transformation.
But we don't actually know if evolution doesn't abstract. It's possible that our genome has been around long enough to develop tendencies to cause mutations that change us in certain ways that might be considered "abstract". For instance, we might be able to mutate longer or shorter arms or legs.
This notion should be supported by the fact that we don't mutate in completely random ways, and the fact that most mutations don't kill us.
I think it's easier to understand if you rephrase it as "evolution doesn't encapsulate." Just because a bunch of smaller proteins fold into a bigger quaternary protein structure doesn't mean that those smaller proteins become "private" members of the new protein "class". Any chemical binding sites that are still exposed on the surface of the complex protein will still be chemically active and affected by evolutionary pressure. Even if you have a protein A made up of B, C, and D that all evolved together to metabolize fructose, an exposed binding site from protein C on A can still effect some completely unrelated biochemistry. At the same time, protein C could also be a critical component of a new protein E.
If the evolutionary pressures on E (or C individually) are greater than on A, C could evolve completely out from under A to the point where A is no longer functional or foldable. As such, evolution can't "rely" on abstractions like "protein C" because their "source code" can be changed nonlocally and their function is dependent on very local conditions like temperature, pH, and surrounding molecules. Abstracting away g(h(i(x))) as f(x) in Mathematica is pointless when your packet sniffer can arbitrarily change the meaning of "h(x)" to "nop".
I'm not a biologist or geneticists but from everything that I've read about cellular biology and genetics I get the strong feeling that there is a lot of encapsulation going on. Sure, it isn't 100% percent perfect but there is still encapsulation going on. Evolution would probably be way harder without it.
One hint is the human body, there are lots of encapsulations: eyes, heart, kidneys, digestive system, etc.
Evolution can repurpose an existing system for a completely different use with just some tweaking.
I think that's a misleading observation though. It is squinting at the macro structure of the body and saying "ah ha, encapsulation!". When you look closer the apparent encapsulation seems less and less like an accurate notion. For example the newer research showing how the gut affects cognition. [0]
This is a bit off-topic but I think paleo-ontologists fall into a similar trap, at least based on popular writing about e.g. sharks. Claims that they haven't evolved in 100M years because they're the same shape as they used to be completely ignore all the other aspects of the shark that could change without affecting the overall shape.
Or perhaps it's like code where everything is in therl global scope. New code may add global variables and/or rely on existing ones, or even change existing ones. In code like, changing one line of code could have far reaching and unexpected consequences.
It's not a great example, and I'm sorry for it. Let me try another one. In the study of eukaryotic cell cycle, there's a notion of a checkpoint. For example, if you damage the DNA of a yeast cell severely, it will continue its cell cycle until some point and then arrest instead of arresting directly, and you can find mutants that don't arrest. There's a whole set of mutants that were used to figure out the checkpoints in the cell cycle.
Of course there's no little physical switch. This is a giant mass of molecules bouncing around. A fellow I know did some interesting work on replacing simple checkpoint models with entrained oscillators. But this makes the checkpoints look like an abstraction.
That's our view of it, though. It's not an abstraction. It's something that looks like one.
We decided we had enough so we rewrote it! Well, most of it, there were some abandoned servlets that brought in enough money that we kept the old codebase running as well. :)
Point 1 draws parallels with machine learning's pixel attacks. That one pixel doesn't define the image, but it happens that changing it changes the classification of the whole
>If a human engineered it, we'd call it a leaky abstraction, but please don't get caught up on that. Evolution doesn't abstract.
Why not? Humans are products of evolution too, and they abstract.
I mean, what we consider conscious (e.g. human activity) and what we consider accidental (e.g. the result of evolutionary forces clashing) are not very well defined. Heck, we haven't even solved the determinism vs free will issue yet.
4. Few biologists have dived into automata theory. The divide in background theoretic knowledge and thus language is too great for biology to directly profit from advances in automata theory.
There has been what I think is direct evidence of the possibility of this in the digital world. Adrian Thompson's work[1] in evolving an FPGA circuit to tell the difference between the spoken words "stop" and "go" (yes .. digital circuit design with an analogue task) threw many surprises. The evolved circuit was highly economical with gates and didn't use a clock. It had also started exploiting the specific physics of the FPGA chip and included transmitters and receivers. Digitally "useless" structures were critical to its performance.
If this is true of a simple evolved circuit - i.e. the connection between the circuit specification and the behaviour being complex, with the circuit specification already encoding physics beyond the digital gates - then I'm not sure what hope we should actually place in statistical attempts to connect genes with organism behaviour.
Pushing the link a bit more, just as the circuit representation went beyond the target domain of digital circuits, could what is encoded in our genes explicitly encode exploitations at quantum level? With this, the possibility of brain structures acquiring quantum error correction schemes doesn't seem too far fetched or crazy thought.
> statistical attempts to connect genes with organism behaviour
It reminds me of the halting problem and modern semantic analysis methods for programs. IE some things you can determine without running the code (static analysis of code or dna), and to determine other things you have to run the the code and see (determine if a program will ever end or see if a trait is expressed).
What do you mean with "quantum level"? All of biochemistry is best described by quantum mechanics. Do you mean large scale entanglement and coherence as would be required for quantum computation? That seems a bit unlikely in a warm soup like our bodies. We haven't been able to achieve it on a useful scale even in the controlled environments of physics labs.
> All of biochemistry is best described by quantum mechanics.
.. much the same as all of what the FPGA does is captured by electromagnetism + material physics. Nevertheless, the encoding of the FPGA circuit, while it describes a digital process, can hack into the features of a level below. Similar to that, while gene sequences may encode protein production, I'm wondering whether the resultant system could similarly make explicit use of below-the-protein-level features. Our inability to achieve that yet is not an argument, much the same as a digital circuit designer will be at a loss to make a "stop" versus "go" detector and that too one without a clock.
Sounds like some measurement threshold of what degree a cellular process is reliant on quantum effects. (Maybe something like electron tunneling or photon wave/particle behavior?)
1. The theory of "junk DNA" of which the authors received Nobel Price is increasingly looking like...well...junk.
2. The recent discovery of Epigenetics adds a whole other dimension for understanding traits and genetics. Lots of biological science needs to be rewritten/updated in the years to come.
#2 is perhaps one of the most confounding recent areas of research that really starts complicating the study of DNA and how changes to it map to an organism's outcome in the real world over time and in different environments.
As new studies emerge on identical twins/triplets/etc., it has become clear that even identical DNA does not at all have 100% clone of the person, even if they do in fact share the same DNA. Ranging from things like height differences, leg lengths, intelligence levels, and a host of other characteristics, "identical" DNA does not mean physically identical creatures that look and behave exactly the same, even if they are far more similar than different.
Eh I disagree. It's been known for a while that you need something other than just DNA to get the heterogeneity of cellular phenotypes. Remember that your Iris and toenails have the same DNA, yet wildly different gene expression profiles. Without cellular memory that would be impossible and we would all still be single celled organisms :)
> As new studies emerge on identical twins/triplets/etc., it has become clear that even identical DNA does not at all have 100% clone of the person, even if they do in fact share the same DNA. Ranging from things like height differences, leg lengths, intelligence levels, and a host of other characteristics
New studies weren't needed; this has been known forever. From The Blank Slate:
> Even genetically homogeneous strains of flies, mice, and worms, raised in monotonously controlled laboratories, can differ from one another. A fruit fly may have more or fewer bristles under one wing than its bottlemates. One mouse may have three times as many oocytes (cells destined to become eggs) as her genetically identical sister reared in the same lab. One roundworm may live three times as long as its virtual clone in the next dish. The biologist Steven Austad commented on the roundworms' lifespans: "Astonishingly, the degree of variability they exhibit in longevity is not much less than that of a genetically mixed population of humans, who eat a variety of diets, attend to or abuse their health, and are subject to all the vagaries of circumstance -- car crashes, tainted beef, enraged postal workers -- of modern industrialized life." And a roundworm is composed of only 959 cells!
The article above has nothing to do with Junk DNA. "All genes" is still about 1.5% of the genome. I think Junk DNA is a bad term for the parts of the genome that describe it. In human-engineered physical systems, we are used to all or most of the parts having a purpose of some kind, even if it is an aesthetic purpose.
The genome is more like source code that evolved over millions of commits, and has code from way back when, for different use cases. It also has "injected code" ie ancient viral DNA, and other weird artifacts that's not relevant to the functioning code base of today. Sure, it is useful from a meta-evolutionary perspective, those parts can be refashioned for another use... but so can real-world "junk" :)
Quote:
Male mice grow ovaries instead of testes if they are missing a small region of DNA that doesn't contain any genes, finds a new paper published in Science.
The press release is misleading - no one is claiming 98% of the DNA is "junk", contrary to what the press release claims with no evidence. Regulatory regions around genes are understood be crucial to gene function and taught in undergraduate education. Usually people claim that mobile elements + repetitive regions constitute the "junk" region, which is about 40% of the genome. Also, there are dozens of studies showing you can survive with large chunks of it missing. Each human in fact has thousands of such structural variations.
Not sure what you mean. Dawkins in 2009:
“Leaving pseudogenes aside, it is a remarkable fact that the greater part (95 percent in the case of humans) of the genome might as well not be there, for all the difference it makes.”
Honestly, I have a PhD, doing human genomics, and to me Dawkins is a popular science writer. I don't know anyone in the field of genomics who is an actual working scientist who believes in the 95% number.
Note that the Encode Project defines functional as being transcribed, which is not a widely accepted definition of functional. The whole idea of "function" itself brings a lot of subjective notions into play.
As I said in the parent, I also think "junk dna" is a terrible term. What I prefer stating is, in agreement with Ohno/Kimura's theory of neutral selection, the vast majority of our genome is not under positive or negative selection - i.e. mutations occurring there are neither detrimental, nor helpful, for fitness.
If you look at allele ratios of variants across the genome, the vast majority show evidence of neutral drift.
For fairness sake, quote from the same post (Dawkins changed his mind by 2012):
"So you can think of the protein-coding genes as being sort of the toolbox of subroutines which is pretty much common to all mammals -- mice and men have the same number, roughly speaking, of protein-coding genes and that's always been a bit of a blow to self-esteem of humanity. But the point is that that was just the subroutines that are called into being; the program that's calling them into action is the rest [of the genome] which had previously been written off as junk."
This makes sense to me (as a programmer :). But I wonder if someone managed to figure out the language in which the "caller" code is written. How does it say: call subroutine at position X, then if (condition), call subroutine at position Y, etc... I was not able to find any info on this.
That's a great question! As you know, DNA -> RNA -> Protein where the first arrow is transcription (polymerase) & the second one translation.
Genes are transcribed by a polymerase, and a couple of factors determine if the polymerase can be "recruited" there. Here are a couple of those factors:
1) Is the site accessible? That part of the genome needs to be "open chromatin" for it to be transcribed. Next question of course is what makes some chromatin open some closed..
2) Histone signals: DNA spools around wheel-like things called histones. Histones get marked in a variety of ways to indicate "status" of that part of the genome. Some of them are pro-transcription.
3) Polymerase is usually recruited there by DNA-binding proteins called "transcription factors". These proteins bind to the regulatory / "promoter" region upstream of a protein-coding gene. There are over a thousand different transcription factors, each with a specific "motif" they recognize.
The global state is detected in various ways (receptors on the cellular mebrane etc.), which triggers these transcription factors, which then go bind DNA near the genes,
to activate a new subroutine.
Here's a paper:
http://www.pnas.org/content/107/20/9186
Title: "Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks"
Thank you, the article is fascinating indeed. But your explanation is not very accessible for a layman. I will tell you the questions a typical programmer will be asking:
Main question: suppose we have to build a heart from scratch. It has certain shape, at every point inside it, there's a specific type of cell (they are all certainly different). E.g., there's a complicated network of blood vessels inside, and what not. There's a process that builds the heart. Question: who tells this process that at point (x,y,z), it should add a cell of type T, and connect it to neighbors in a certain way? Is it something like cellular automation, where the property of cell at (x,y) is determined by properties of neighbors only? All neighbors? And how this function is calculated? And what is the CODE for calculation? Where is it stored? And where something that was formerly called "JUNK", and now looks like a master program, fits into this picture?
To explain it to a programmer, it's better to avoid the terminology and specifics, but focus only on functional relations - e.g. type of cell at point (x,y) is a FUNCTION of types (or something) of cells (x-1,y), (x+1, y) and maybe something else. I can't find it anywhere.
BTW, I am not a believer in cellular automation stuff a la Wolfram, just trying to frame the question in a way I could understand.
(just in case you don't want to reply here, my fake email
address is in my profile)
While this is not my specialty, the field of "evo-devo" and developmental biology is the field trying to answer those questions. Honestly, we still know very little about how the genome results in morphology.
It is a lot more like a cellular automaton than anything else. From minute gradient differences inside the fertilized egg, recursively structure spreads out in very much a cellular automata like fashion. Cells respond to the bio-mechanical stress around them and to the signals they bathe in, to decide cellular fate, and in turn release new signals..
It's a very different paradigm of computation than the one we think of. Each cell is like a docker instance, containing the full source code. Perhaps these links explain a bit:
Analog computer switching to digital mode, and back to analog, and back to digital, etc... ? Something is extremely fishy here. Looks like highly intelligent series of operations, much beyond whatever we can imagine.
The coding part of the genes are 1.5% of the genomes.it is called the Exome.(32 millions)
But when looking at all genes (including UTR, pseudogenes) from a basepair prespective It's closer to 40% of the genome.(1.2 billion bases)
This is a paper that I'm glad was published (maybe not Cell, but somewhere), because it presents an interesting and pretty clear theory.
At the same time, it seems like it's fairly clearly falsifiable almost on its face, because there are many genetic disorders involving clear, substantial mutations (in a chromosomal topography sense) that are circumscribed in their effects, at least to one extent or another.
I think some variant of this might be true, where traits are influenced by a very very large number of genes (like thousands or hundreds of thousands or more), possibly interacting or in more complex chaotic effects. But this is basically a variant of the polygenic hypothesis, which has been a major paradigm for years. This model seems like the dominant paradigm pushed to its limit, rather than something fundamentally new.
Maybe I'm misrepresenting my sense of the field, but I doubt that many people doing GWASes believe in a relatively limited number of genes affecting traits. I think this has been true for several years now at least. I've been wrong in my assumptions about the field before though.
The top comment on this post addresses the one gene, one trait evidence. In essence, just because changing one gene mutation fixes the problem, that doesn't mean that only one gene was responsible for the problem in the first place.
"The "one gene = one protein" dogma of molecular biology (expanded: "one trait selectable in breeding experiments = one compact locus of the chromosome") was a priori wrong, but it's taken us decades to undo the damage. We were led astray because there are traits that do map to a single locus, or single mutations that were found to be able to control a trait, which is not the same thing as the trait."
It seems to me that this is an example of a common pattern in biological research. A simple explanation is developed for a phenomena, but then as time goes on, it turns out that in at least some important ways things are much more complicated than originally thought.
Also, the article talks about the controversy between the view that all genes make tiny causal contributions versus some are core and other peripheral.
But perhaps it is, at least in some cases, a matter of what I would call complex conditionality. So for instance maybe disease X happens if there are mutations in genes A, B and C, or C, D and E, or G, H and I, but not any other combination. Certainly we find things like that in other areas of reality.
And that pattern in science is not a bad thing. Find a general pattern and use it to make predictions. Eventually you identify the conditions in which the general pattern does not hold and you formulate a less simple explanation.
Having studied biology and genetics this is common sense to me. Genetic systems are probabilistic chemical systems where everything influences everything to varying degrees in parallel. They are nothing like the linear assemblies of modules that humans prefer to engineer.
Which explains why gene regulatory network inference by expression patterns is garbage. My first time in graduate school I worked on it, and the model worked just as well using random genes instead of annotated transcription factors.
There was no science in it, so I took a master’s, worked in industry, and am now doing real science in a CS PhD program.
>Having studied biology and genetics this is common sense to me. Genetic systems are probabilistic chemical systems where everything influences everything to varying degrees in parallel. They are nothing like the linear assemblies of modules that humans prefer to engineer.
I'm really glad that this mental model I've built up seems to reflect reality. The idea of neatly editing specific genes to produce a desired result always sat with me as the pinnacle of human hubris. The more I learn about Biology the more I just throw up my hands and say "magic".
Is it really magic or is the genetic code just defining something much more like a CPU or OS than an application program?
If you took a bug report and did a statistical analysis of what transistors contributed to the bug, in most cases it would tell you "pretty much all of them". In very rare cases, it would tell you there were specific broken transistors. Similar for OS instructions. Because it's just the wrong level to be analyzing problems with the ultimate behavior of a system that's mostly functional. 99.9999% of the time, you don't have a compiler bug or an OS bug or a hardware bug, because if you did, everything would be dead.
My kneejerk reaction* is that genes are like transistors or OS instructions and not like application program instructions, and it really shouldn't be surprising or mysterious if they don't correspond to specific behaviors. It's just a different level of abstraction.
*having not studied biology and genetics, and just assuming living organisms have to be like computers.
The closest analogy in technology would be the Internet, IMO.
A CPU or a single computer is still a fairly homogeneous, consistent creation. Sure, it has a lot of transistors, but it's just the same pattern over and over.
Think about the Internet: many of the billions of things on the internet are similar enough to be called the same, but many of them are ... a little not. This computer is Intel, this is AMD. This one runs Microsoft, this one Ubuntu. This one Ubuntu 15.07 with a 5.3.2.2 Linux kernel, that one with a 5.3.7.1 kernel. This part uses IPv4, that IPv6. This HTML on this site is a little nonstandard, that one relies on an expired SSL certificate.
The body, and modifying it, is a lot like the Internet. You set up 1.1.1.1 as a DNS server and accidentally partition the Internet.
There are diseases for which editing a single gene in the right set of cells would absolutely cure a disease. There might be other genes or regulatory elements involved, but their contribution is neglible.
To me, the pinnacle of human hubris is thinking you can correct the more challenging ones (like the ubergenetic phenotypes described here) without having some sort of powerful technology that helps work with the irreducible complexity of probabilistic chemical systems.
"We describe our first patient treated with lentiviral vector–mediated addition of an antisickling β-globin gene into autologous hematopoietic stem cells."
"We describe our first patient treated with <a technique that injected> <DNA to counteract the disease> into <stem cells>. <which were then injected into the patient>."
Its not magic. Its just something we don't have good mental models for understanding since it doesn't resemble the things we usually engineer. I always give the following rough analogy:
"Imagine a programming language where every single instruction simultaneously executes and simultaneously alters the inputs of all other instructions to varying degrees."
Complex artificial neural networks and data flow models are closer to gene regulatory networks and the genotype->ribotype->phenotype causal flow but are still oversimplifications.
It is sometimes possible to hack individual genes to fix really glaring problems reducible to one or a few key mutations like sickle cell anemia. Those things are the low hanging fruit of genetic engineering. Beyond that it gets harder. Trying to do things like boost life span or median IQ will be exponentially harder and will require new cognitive paradigms IMHO.
Its too bad nobody studies this stuff anymore. There was a ton of really great research into complex systems and emergent behaviors in the 90s and 2000s and then it went out of vogue for some reason.
Common sense from the machine learning side too! (that said, the ex-physicist in me would like to remind everyone that science and reality are not required to match your common sense)
If true, this means that evolution could look more like stochastic gradient descent, and less like brute force luck plus survival of the fittest.
You misunderstand my point... larger ratio of base pairs to behavior means that the behavior is likely more robust to noise, and the original evolution of the behavior would have been smoother (i.e. proceeded in less of a step-wise manner, because each step would be a much smaller fraction of the required change than previously thought).
Traits embedded into a dense vector space encoded as DNA base pairs is a pretty radical deviation from the one-hot vector approach we were taught in school. Wouldn't it be lovely if we found that vector addition worked similarly in this domain?
This reminds me of systems theory; anyone else? It makes intuitive sense if I think of traits as one outcome of a system. Rather than converging to a single particle-like "cause," such as a gene, it seems that the wider system is the basis for the trait--the objective appearance of the trait is an expression of the system. It seems it should follow that you can continue tracing the trait from the individual level up though additional systems, like social group, atmosphere/environment, etc.
This is basically what it is : basic systems theory applied to basic genetic knowledge. Having worked in this field during my studies, I can say that most of the leading figures were not impressed by the Omnigenics paper. It was simply the right claim at the right time, it made a splash, but it will probably not change at all how research is conducted.
Some say that the view it is pushing (core vs peripheral genes) is simply wrong as evidence of it cannot be found in the data (and there is a lot of data), see the end of the article about Naomi Wray's article. Also see the many mentions of the paper's critics! I like the phrase "provocatively phrased extension of earlier ideas".
There are bigger issues that need addressing but all of them are much much harder to get right and they would probably not make it to Quanta magazine ;)
For example, phenotype heterogeneity and patient stratification are two big thorns in precision medicine's side.
> "It makes intuitive sense if I think of traits as one outcome of a system."
Put another way, if you were building something and you wanted it to survive, would you want a flexible multifaceted system, and not a dogma-esque checklist of X, Y and Z.
That's a great way to put it. You'd want a system that would instinctively re-implement patterns as a way of protecting itself, as well. I've noticed lately that if you take business process development, personal productivity, value-creation philosophy, socio-political framework-building (e.g. development of democracy) and combine that sort of thing with the traditional "software OR firmware OR hardware" definition of technology, the human system is always building "technology" toward what might be called survival+. In this human systems/outside-systems interaction there is a periodicity aspect where at times (e.g. giant meteor approaching) human "survival" is definitely the question, and other times we surge way past just putting meat on the table, so to speak.
Also, a trait, e.g. a psychological trait, or a group of them ("values-centered, empathetic, philosophical" vs. "productivity-centered, logical, process-oriented") could be seen as a technology orientation that helps the system build technology of a specific kind.
The humans sub-systems compliment each other and at the same time feels like backups of each other. The hardware of genes get adjusted by the software of epigenetics.
And who knows what we have yet to find. But it's making more and more sense in a why didn't we see this sooner sorta way.
Of course, to me this is obvious: "everything correlates with everything else".
Wait until we start seeing the results of the hyped up "gene editing" studies where they follow up for a few years and look at more than one/few outcomes at a time...
Don't even need that. Later this year BGI will allow you to submit zygotes and they'll provide a 23andme compatible report. And you can plug that in to dnaland and find out which zygote you should select for the most intelligent offspring.
Well that is almost surely fake. This is like a list of companies whose stocks should be shorted in (at what age is intelligence determined? 5 years?) as the lawsuits come rolling in.
If you look at how random projections work in machine learning or error correcting codes this makes perfect sense. The information is distributed across all bits.
A similar debate goes on in Cognitive Neuroscience and/or neuropsychology/neurophysiology: can cognitive phenomena--like emotion, learning, memory, affect, etc--be localized in the brain? can fMRI solve this localization-distribution issue?
Whatever we have known so far, early phases of the input system (sensory) and later phases of the output system (motor) can be localized. Even there is an overlap between the input and the output system.
Then there’s the problem that fMRI is hardly providing us with a complete view of everything going on in the brain. The same goes for the other instruments we have. Who knows what parts of the puzzle we’re not yet even seeing?
Think of thermometer, which everyone of us uses. It doesn't tell much about what is going on inside, but it tells us that something is NOT normal esp out of normal body temp range. Does fMRI play the role of thermometer? Yes when there are injuries; but injury itself plays the role of the thermometer.
fMRI is not useful in settling the disputes about how brain makes the mind--esp higher cognitive processes.
I don't think this significantly changes expectations. CRISPR and similar systems will still be immensely beneficial for simple traits (e.g. traits determined by a very small number of genes). Some diseases can be eliminated by changing a single gene.
CRISPR and similar systems could someday be helpful for dealing with complex traits, but we first need to understand exactly what genes contribute to them and how, and for many traits, that will take a very long time. In some sense, CRISPR is just a dumb cutting tool. Knowing where to cut was always the hard part, and it always will be.
Even if the cause of a particular disorder is spread across the genome it could be the case that changing one particular gene is enough to cure it in some cases.
No it just means more than one gene would be needed to be modified. Don't know who was ever thinking that traits weren't polygenic. If they were monogenic then there would be no variance in traits.
I imagine CRISPR/Cas9 still has great potential for addressing specific genetic disorders (which are usually localized to a single, known gene), but the more transhumanist stuff about making smarter, longer-lived people is still a ways off.
> Even an average idiot must now realize, that GM food may be riskier than you think.
Yes and no. It may lead to dangerous expressions, but plain chemical testing and analysis prior to commercial release should ensure nothing toxic makes it into people's food.
If your apples suddenly start producing magnitudes more cyanide after modifying something, just don't sell them.
These kinds of strange findings are going to keep occurring because these researchers are too far invested into a mode of research (genetics) that won't actually find the determinism they desire. It's the environment not the genes.
1. Genes are not loci. The "one gene = one protein" dogma of molecular biology (expanded: "one trait selectable in breeding experiments = one compact locus of the chromosome") was a priori wrong, but it's taken us decades to undo the damage. We were led astray because there are traits that do map to a single locus, or single mutations that were found to be able to control a trait, which is not the same thing as the trait.
2. Distributed representation. If I point to a sentence and ask "where is the sarcasm?" (assuming the sentence is sarcastic), there is no answer. It's certainly a trait of the sentence, just as, say, being red headed is a trait of the organism. But a linear model showing each word's contribution to sarcasm isn't helpful.
3. Perhaps a corollary of (2), humans have the concept of abstraction. There are many situations where you can get something like an abstraction from evolution. If a human engineered it, we'd call it a leaky abstraction, but please don't get caught up on that. Evolution doesn't abstract. But there are structures that emerge that can have similar properties, especially from repeated exaptation. Consider the MAP kinase pathways. Lots and lots of cell responses involve them, in all kinds of subtly different ways. We don't try to claim that a MAP kinase is the gene for anything in particular, any more than we would claim that the interrupt for triggering a system call on 32 bit Linux is the cause of certain behavior that a program is supposed to have in a particular domain.