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Thread: Darwin and the Titanic

  1. #71
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    Quote Originally Posted by UKSmartypants View Post
    Totally incorrect

    The name "NP-complete" is short for "nondeterministic polynomial-time complete". In this name, "nondeterministic" refers to nondeterministic Turing machines, a way of mathematically formalizing the idea of a brute-force search algorithm. Polynomial time refers to an amount of time that is considered "quick" for a deterministic algorithm to check a single solution, or for a nondeterministic Turing machine to perform the whole search. "Complete" refers to the property of being able to simulate everything in the same complexity class.


    In other words, NP-Complete problems are probably solvable only in exponential time. It is believed that if anyone could *ever* solve an "NP-Complete" problem in "P" time, then *all* "NP-complete" problems could also be solved that way by using the same method, and the whole class of "NP-Complete" would cease to exist.


    The classic NP-Complete Problem is The Travelling Salesman problem.


    This problem can be stated as- "Given n number of cities and a travelling salesman has to visit each city. Then we have to find the shortest tour so that the travelling salesman can visit each and every city only once."



    The point is as you increase the number of cities ina linear fashion, the solution time increases exponentially. Its always solvable, but when the number of cities gets very large, the computation time gets longer than the age of the universe....
    Except if you use a neural network, with as many neurons as you have cities, and an extra associative layer, which will arrive at the optimal solution "most" of the time. "Very quickly", if it's appropriately trained.

    TSP is an interesting example. As the number of cities grows (exponentially), you get a whole class of solutions where basically "one solution is as good as another". The shortest distance is asymptotic in the set of paths, and if there are lots of paths they'll tend to cluster around certain distances. Neural networks happen to excel at finding "one of many" solutions. The downside is they get stuck in local minima sometimes, and that's fixable but most people just run the experiment again.
    Last edited by nonsqtr; 12-06-2021 at 10:10 PM.

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    Quote Originally Posted by Authentic View Post
    And is there any wonder why scientists are so distrusted today, when they come across as arrogant and offputting?

    Better work on your messaging, scientist.

    Start taking paying attention to @nonsqtr. I actually read some of his links.
    Excellent, Grasshopper! If you're reading, you're learning.

    When I started in biophysics there were no neural networks, no nonlinear thermodynamics, no fancy microscopes beyond the ordinary electron variety.

    What attracted me to this part of science, was precisely the opportunity to bridge the gap, between what we can see and what we can't.

    The whole idea that the shape of a fern leaf is determined by chemical gradients only came around in maybe late 90's. Before that, all through the 70's and 80's, people were looking for genes that code shape. After 20 years of exhaustive search they finally figured out there's really no such thing. The shape of the human body, for example, is so intricate that 4 million genes wouldn't be enough to code for it.

    What happens instead is there are thousands upon thousands of interacting chemical gradients. What is instructive is to look at the type of molecules that are being used. Glycerol, for instance, is a key component of the phospholipids in cell membranes. Some of the byproducts of phospholipid breakdown include molecules that will physically bend the membrane - so this is "shape", of sorts. Then there is also the cytoskeleton, which is like a scaffolding directly underneath the cell membrane. Part of its function is to anchor molecules to the membrane, for example at a synapse the postsynaptic density is what keeps the receptors in place. (Otherwise they would float around, in the membrane - that's the "fluid mosaic" concept).

    So that's another kind of "shape", the cytoskeleton consists mainly of actin and tubulin (microfilaments and microtubules). There are several of these low-level cellular mechanisms for shape, and in each case the question is, how does the cell "compartmentalize" the concentrations of molecules, and how does it create chemical gradients. But generally speaking, none of these mechanisms extend beyond the cell. In development nerve fibers from the spinal cord will climb all the way up into the brain, but they dont know where they're going or what they're going to connect with, they're just following a chemical gradient. Which is why everyone's spinal cord looks just a tad different.

    This is why embryology is important. Because it's all about the gradients. In 1976 they showed me I could stick a pin in a frog embryo in just the right place at the right time, and it would grow a third front leg. We should have known "right then" that shape is mechanical and therefore chemical.

    So when you look across 'species' your eye sees the differences in shape, yet we share 95+ percent of our DNA with chimpanzees. And it turns out, the SEQUENCE of development is vital, things have to develop in order. A great example is the complex log mapping in the primary visual system, again making use of chemical gradients.

    Generally speaking to build a fractal structure like a fern leaf, three operations are needed: copy, move, and divide. Biological systems easily accomplish all three. Then what happens is, you get interaction between neighboring fractal generators, which is were the beautiful dendritic branching patterns in the brain come from.

    So, this is why I say, to understand biophysical evolution (which is the level at which it occurs), one must have some dynamics, some embryology, and a good solid understanding of (bio)chemistry. And math never hurts. You can have with this stuff, it's not all drab. There's a lot of free fractal generators you can use on your computer.

    But I digress. Halomonas binds copper and other ions, because it's kind of a salt-water organism. In Halomonas membrane we find bidirectional transporters that shuttle ions in opposite directions. Halomonas proteins often require ionic cofactors, and they stop working in fresh water and in low pH environments. It can withstand severe temperature changes though, which we can't, our internal temperature has to stay pretty much the same.

  3. #73
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    Quote Originally Posted by UKSmartypants View Post
    Totally incorrect

    The name "NP-complete" is short for "nondeterministic polynomial-time complete". In this name, "nondeterministic" refers to nondeterministic Turing machines, a way of mathematically formalizing the idea of a brute-force search algorithm. Polynomial time refers to an amount of time that is considered "quick" for a deterministic algorithm to check a single solution, or for a nondeterministic Turing machine to perform the whole search. "Complete" refers to the property of being able to simulate everything in the same complexity class.


    In other words, NP-Complete problems are probably solvable only in exponential time. It is believed that if anyone could *ever* solve an "NP-Complete" problem in "P" time, then *all* "NP-complete" problems could also be solved that way by using the same method, and the whole class of "NP-Complete" would cease to exist.


    The classic NP-Complete Problem is The Travelling Salesman problem.


    This problem can be stated as- "Given n number of cities and a travelling salesman has to visit each city. Then we have to find the shortest tour so that the travelling salesman can visit each and every city only once."



    The point is as you increase the number of cities ina linear fashion, the solution time increases exponentially. Its always solvable, but when the number of cities gets very large, the computation time gets longer than the age of the universe....
    Wellll......

    Not totally incorrect:

    A: you did not critique the other 4 examples.
    B: NP Complete problems are ones that we watch in the computing world, because invariably someone will suggest some cockeyed snakeoil method to slice the Gordian knot...
    Scientist, Evangelical Christian - reformed, father, entrepreneur, hunter, outdoorsman, motorcyclist, Constitutional Conservative.

  4. #74
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    Quote Originally Posted by Physics Hunter View Post
    Wellll......

    Not totally incorrect:

    A: you did not critique the other 4 examples.
    B: NP Complete problems are ones that we watch in the computing world, because invariably someone will suggest some cockeyed snakeoil method to slice the Gordian knot...
    Oh, you mean like Grover's algorithm?

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    https://en.m.wikipedia.org/wiki/The_..._Morphogenesis

    Prigogine got the Nobel Prize for actually doing this.

    He was one of the early students of nonlinear thermodynamics.

    That work was mostly in the late 60's and early 70's.
    Last edited by nonsqtr; 12-07-2021 at 07:26 AM.

  6. #76
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    Let's talk about evolution.

    Let's talk about a specific example.

    I said evolution was biophysics, well here's an example.

    In a previous post I mentioned that there's a cloud of negative charge around cells, because of the phosphates sticking up out of the membrane.

    This charge cloud is interesting, physically. It will attract positive ions (sodium, calcium) electrostatically, but the ions can not go through (across) the membrane, because of the lipids.

    And, that's a problem for the cell, because cells need sodium and calcium.

    Lipid micelles are easy, they form spontaneously. However they can't get ions because the charge cloud is too dense, hydrogen is a lot smaller than calcium.

    The first step in the evolution of a trans-membrane ion channel, is simply the ability for a molecule to stick it's head up out of the membrane. Because that's the only way it's going to get an ion. The binding site must be "taller than" the charge cloud.

    So, when we look around in nature, we observe a fair number of reactive molecules that have bound with lipids and phospholipids. Sugars, are ubiquitous. Choline, same way. These molecules are floating around freely in the ocean and they're in every form of life, they're easy to get. (Amino acids are easy to get too, halobacter eats them and uses them to build proteins).

    If you have a bunch of lipid and a bunch of sugars floating around in a solution, you'll get some glycolipids. And, primitive life forms eat by invaginating (endocytosis), so they'll just drink up these various molecules.

    And, if a dumb cell suddenly gets a glycolipid it can begin shuttling ions. Ion channels are glycolipids, they usually exist as trimers or tetramers.

    Right now, there is plenty of experimentation going on, to see how cells handle unknown molecules. Primitive cells just seem to eat them willy-nilly, and getting something useful is the luck of the draw. (However if you're a billion bacteria in a sea of trillions of molecules, the odds are in your favor).

    So, how do we get from there, to actually making these molecules from DNA? Well... there's something that comes before that, there is the maintenance of concentrations of a molecule, "without" DNA. Glycerol is a byproduct of glucose metabolism, via the glycolytic pathway, and it's also recycled via lipase enzymes. The cell can still function even without synthesizing glycerol.

    The requirement is that molecular concentrations be maintained, for biochemical activity to continue. Cells use a variety of strategies to maintain concentrations, not just synthesis and not just random invagination.

    One can begin to understand biophysical evolution by looking at primitive organisms. Cyanobacter makes AEG (amino ethyl glycine), which forms a polymer backbone much like DNA. In this way, the cells can use proteins to encode genetic information. Before DNA there was RNA, and before that there was protein.

    The first micelle to be able to channel an ion, will survive.

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    Quote Originally Posted by nonsqtr View Post
    Let's talk about evolution.

    Let's talk about a specific example.

    I said evolution was biophysics, well here's an example.

    In a previous post I mentioned that there's a cloud of negative charge around cells, because of the phosphates sticking up out of the membrane.

    This charge cloud is interesting, physically. It will attract positive ions (sodium, calcium) electrostatically, but the ions can not go through (across) the membrane, because of the lipids.

    And, that's a problem for the cell, because cells need sodium and calcium.

    Lipid micelles are easy, they form spontaneously. However they can't get ions because the charge cloud is too dense, hydrogen is a lot smaller than calcium.

    The first step in the evolution of a trans-membrane ion channel, is simply the ability for a molecule to stick it's head up out of the membrane. Because that's the only way it's going to get an ion. The binding site must be "taller than" the charge cloud.

    So, when we look around in nature, we observe a fair number of reactive molecules that have bound with lipids and phospholipids. Sugars, are ubiquitous. Choline, same way. These molecules are floating around freely in the ocean and they're in every form of life, they're easy to get. (Amino acids are easy to get too, halobacter eats them and uses them to build proteins).

    If you have a bunch of lipid and a bunch of sugars floating around in a solution, you'll get some glycolipids. And, primitive life forms eat by invaginating (endocytosis), so they'll just drink up these various molecules.

    And, if a dumb cell suddenly gets a glycolipid it can begin shuttling ions. Ion channels are glycolipids, they usually exist as trimers or tetramers.

    Right now, there is plenty of experimentation going on, to see how cells handle unknown molecules. Primitive cells just seem to eat them willy-nilly, and getting something useful is the luck of the draw. (However if you're a billion bacteria in a sea of trillions of molecules, the odds are in your favor).

    So, how do we get from there, to actually making these molecules from DNA? Well... there's something that comes before that, there is the maintenance of concentrations of a molecule, "without" DNA. Glycerol is a byproduct of glucose metabolism, via the glycolytic pathway, and it's also recycled via lipase enzymes. The cell can still function even without synthesizing glycerol.

    The requirement is that molecular concentrations be maintained, for biochemical activity to continue. Cells use a variety of strategies to maintain concentrations, not just synthesis and not just random invagination.

    One can begin to understand biophysical evolution by looking at primitive organisms. Cyanobacter makes AEG (amino ethyl glycine), which forms a polymer backbone much like DNA. In this way, the cells can use proteins to encode genetic information. Before DNA there was RNA, and before that there was protein.

    The first micelle to be able to channel an ion, will survive.
    I am way too busy talking about opposing Global Godless Communism to trifle with the ground that they smugly assume that they own.

    Be well.
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    Quote Originally Posted by Physics Hunter View Post
    I am way too busy talking about opposing Global Godless Communism to trifle with the ground that they smugly assume that they own.

    Be well.
    Yeah, because some gigantic dude with a long white flowing beard twinkled his fingers and it all magically appeared as-is.


    Last edited by Oceander; 12-08-2021 at 09:56 AM.
    So let us stop talkin' falsely now
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    Quote Originally Posted by Physics Hunter View Post
    I am way too busy talking about opposing Global Godless Communism to trifle with the ground that they smugly assume that they own.

    Be well.
    The Chinese will kick our butts if we don't understand this stuff.

    Their physicists are very good, and there's millions of them.

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    Quote Originally Posted by UKSmartypants View Post
    your response is garbage. [...]Now eff off.
    Thread closed.

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