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Comment by rsheridan6 | Hacker News Reader

I don't find these criticisms convincing. We haven't solved the protein folding problem? So solve it. Is there some reason to believe it won't ever be solved? If you had a sufficiently accurate simulation of a cell or organism (which we don't, but we might someday), you could do experiments on it much cheaper and faster than in a lab.

I do agree that there's no freakin' way this will be done in ten years, or in the 62-year-old Ray Kurzweil's lifetime, or mine.

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There is Nobel-prize winning theory that shows that you won't ever truly solve this problem by studying the underlying atomic physics:

http://en.wikipedia.org/wiki/Ilya_Prigogine#Dissipative_stru...

I'm ignorant of this theory, but reading the link I don't see that it shows that protein folding can't be modeled.

You'd have to read the papers cited below to get the meat of the theory but the gist of it is that you can't model proteins by looking one level down, i.e. by studying only the properties of the atoms that make them up. Proteins have emergent behaviors not entirely predictable from the behaviors of their constituent atoms.

Many still believe in the reductionist idea that a perfect understanding of physics would lead to a perfect understanding of chemistry and then biology. This is not the case.

Proteins have emergent behaviors not entirely predictable from the behaviors of their constituent atoms.

Define "emergent". I can believe that a protein's behavior is extremely sensitive to the initial configuration of its atoms and that as a practical matter we can't (currently?) get detailed enough measurements to predict exactly what's going to happen. But without exceptionally compelling evidence I'm not going to believe that there are different physical laws for proteins than for their atoms.

I suggest you read Prigogine's book if you're really interested but his work demonstrates that the problem isn't having detailed enough information about state, it's the irreversibility of time, which makes physics fundamentally non-deterministic.

What has irreversibility got to do with it? There are cellular automata that are irreversible but obviously deterministic.

It has to do with entropy and thermodynamics and the fact that living systems are so far removed from thermodynamic equilibrium that they generate unpredictable, emergent behaviors.

The math behind is pretty gnarly but if you want to understand it I recommend his book: http://www.amazon.com/End-Certainty-Ilya-Prigogine/dp/068483... .

A CA is not a good model for this.

I'm out of my depth here, but if protein folding is intractable like the halting problem or the traveling salesman problem, as you're making it seem, I find it odd that I haven't read that elsewhere.

The physics can be modeled, but according to this article (http://techglimpse.com/index.php/ibms-blue-gene-exploring-pr...) a 1 petaflops machine is estimated to take 3 years to crunch through 100 microseconds of simulated time. And that's just for a single protein interacting with itself and a bit of water.

That might just mean they haven't found the right algorithm yet.

Most people suspect it to be NP-hard or NP-complete.

I fail to see the connection.

No, a computer simulation can never predict exactly what a physical system will do, even in the case of a single particle, due to quantum uncertainty. But so what? If I took out one of your neurons and replaced it with an identical neuron, that new neuron wouldn't do exactly the same thing, again due to quantum uncertainty; nonetheless, its long term behavior would be essentially identical and you, as a person, would be no different.

That is, neurons and brains are classical objects, essentially immune to the underlying uncertainty they're built on.

That is, neurons and brains are classical objects, essentially immune to the underlying uncertainty they're built on.

Absolutely not. Prigogine's work demonstrates that systems far from thermodynamic equilibrium (of which all living systems are an example) are intractably non-deterministic. The issue isn't the underlying quantum uncertainties, it's the macro-uncertainties of the higher-level system.

In other words, you won't predict the behavior a neuron by modeling the underlying physics. You have to learn to model the macro behavior in a statistical way.

I didn't mean to suggest that quantum effects were the only source of uncertainty.

If I've got a cubic meter of pure water, I can slosh it around and observe all sorts of interesting effects. I can then model that cubic meter of water with another cubic meter. That second cube won't behave identically. A cubic meter of water has a very high Reynolds number and can have considerable chaotic turbulence (chaotic in the classical sense, not quantum). The exact motion of the water simply won't be the same, no matter how precisely you mimic the 'input' into the system (forced motion of the cube, for instance).

Nonetheless, the second cube is a fantastic way to understand the first cube, and in some way is qualitatively identical, even when the specific motions aren't replicated exactly. This is exactly the same for computational simulations of the fluid. Of course they can't predict chaotic behavior, but for all intents and purposes they can be just as useful as having that second cube of water.

Likewise, a computer simulation of a neuron may never exactly predict what a real neuron will do. Just like one neuron can never exactly predict what another neuron will do. Just like one bucket of water can never exactly mimic another. But who cares?

You're comparing two very different things here though. One bucket of water is substantially like another just as one brain is like another. A brain and a computer model of a brain are two very, very different things, with completely different meta-properties.

Yes, but what I'm pointing out is that what you are complaining computers fail to have, other physical systems also fail to have! Even a second bucket of water can't predict the first bucket of water! Thus, the fact that a computer can't either is sort of insubstantial to the question of a simulation's utility, no?

The essential point is that you can't model the behavior of a non-equilibrium system by modeling its constituent elements, which is what Kurzweil claims.

That no two non-equilibrium systems are exactly alike seems to me a different question.

I do computational fluid dynamics for a living. I assure you, we model non-equilibrium systems all the time.

You should have the math chops to read the papers then, which make the point better than I can.

Protein folding is suspected to be an NP-complete problem <http://www.liebertonline.com/doi/abs/10.1089/cmb.1998.5.27 >, so it seems to me that having reason to believe it will someday be solved equals having reason to believe that someday NP-complete problems can be computed in polynomial time. Alternatively, you might hope for a domain-specific approximative algorithm, but this seems tricky, too, since mis-folded proteins can end up as really bad stuff, e.g. prions <http://en.wikipedia.org/wiki/Prion >.

Saying it's NP-complete doesn't prove we can't solve it well enough for simulation. As a physical problem become "pathologically NP", Nature becomes non-deterministic too; it seems unlikely that Nature will make extensive use of a protein that doesn't reliably fold in a certain way, or some small constrained set of ways.

See http://www.scottaaronson.com/papers/npcomplete.pdf

Not that I wouldn't like to be as optimistic, too, but Nature does seem to have made enough use of unreliably folding proteins to have come up with BSE, Parkinson's disease, and some others <http://en.wikipedia.org/wiki/Protein_folding#Incorrect_prote... >.

I'm a big fan of Scott Aaronson, too, btw :-) Here's a pic of him demoing the soap bubbles experiment he refers to in that paper you linked to <http://www.scottaaronson.com/soapbubble.jpg >.

I thought of mentioning that, actually, but figured it would be extraneous. However, if they are all indeed pathological then perhaps we can do our simulated humans a favor and not simulate the pathological path.

Still, as discussed in another comment I made, I seriously, seriously doubt that we will ever simulate any sort of intelligence by raw physical simulation. It just isn't feasible with any realistic computational technique.

The article says that the HP model is NP-complete. But water folds protein very quickly. I may be misunderstanding things here, but if protein folding (as opposed to a particular model of protein folding) is NP-complete, shouldn't it be slow in real life too?

I recall reading an interview with someone who founded a company that builds computers for simulating biological systems in silico who thought that there were much better algorithms waiting to be discovered because nature can do it quickly. I can't find it now.

>We haven't solved the protein folding problem? So solve it.

It's not a trivial problem. There are lots of bright minds working on this problem. If you understand the difficulty behind it, you wouldn't make such ignorant statements.

Where did I say it was a trivial problem? I was assuming (perhaps incorrectly according to other commenters) that it's a tractable problem that may someday be solved, like putting a man on the moon was.

> We haven't solved the protein folding problem? So solve it.

You sir, are a genius. To make up for my previous lack of initiative, I will do so immediately. Please arrange for the world to be ready for my announcement of the solution at noon tomorrow.

I'm not a genius, and you probably aren't either, but somebody probably is.

As far as I know there is no fundamental reason to assume that this problem won't be solved. It's not the halting problem.

Don't even solve it. Just put it up for bid on elance, along with the P-NP problem.

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