Monday 28 February 2022

Rise (and fall?) of the atom.

The truth has fallen.

The dragon as merchant.

Physics can rehabilitate OOL science?

Origin of Life Is Not Reducible to Physics

Evolution News @DiscoveryCSC

Yesterday, we critiqued a proposal by Eugene V. Koonin and three colleagues who presented an expanded theory of evolution as “multilevel learning.” (See, “Evolution Is Not Like Physics.”) The proposal commits the fallacy of equating the properties of biological “laws of evolution” with those of physics, and borders on vitalism, which undermines their goal of naturalizing evolution. The proposal was published in two papers in PNAS last month. This time, we look at the second paper that takes their proposal to the special case of the origin of life. Their attempt to incorporate thermodynamics into a highly negentropic process is sure to provoke interest.

From Vanchurin, Wolf, Koonin, and Katsnelson, “Thermodynamics of evolution and the origin of life”:

We employ the conceptual apparatus of thermodynamics to develop a phenomenological theory of evolution and of the origin of life that incorporates both equilibrium and nonequilibrium evolutionary processes within a mathematical framework of the theory of learning. The threefold correspondence is traced between the fundamental quantities of thermodynamics, the theory of learning, and the theory of evolution. Under this theory, major transitions in evolution, including the origin of life, represent specific types of physical phase transitions. [Emphasis added.]

How Can Nature Learn?

Perceptive readers will want to know how they deal with several well-known issues: (1) probability, (2) entropy increase, and (3) harmful byproducts. The authors have already presented their view of the universe as a “neural network” in which natural selection operates at multiple levels, not just in biology. The only neural networks that any human has observed coming into existence were designed by a mind. How, then, can physical nature learn things?

Under this perspective, all systems that evolve complexity, from atoms to molecules to organisms to galaxies, learn how to predict changes in their environment with increasing accuracy, and those that succeed in such prediction are selected for their stability, ability to persist and, in some cases, to propagate. During this dynamics, learning systems that evolve multiple levels of trainable variables that substantially differ in their rates of change outcompete those without such scale separation.

The vitalistic tendencies in this proposal become evident where they claim that nonliving entities are able to predict, train, and compete. They are further evident when the environment can select them according to specific criteria. How do Koonin and his colleagues know this happens? Just look around: there are atoms, stars, and brains that survived the competition by natural selection. Their existence confirms the theory. This is like the anthropic principle supporter who says, “If the universe weren’t this way, we wouldn’t be here to talk about it.”

To deal with the entropy problem, the authors say that learning decreases entropy. They add a second variable Q to the entropy equation that allows them to overcome the problem. “Q is the learning/generalized force for the trainable/external variables q.”

In the context of evolution, the first term in Eq. 3.1 represents the stochastic aspects of the dynamics, whereas the second term represents adaptation (learning, work). If the state of the entire learning system is such that the learning dynamics is subdominant to the stochastic dynamics, then the total entropy will increase (as is the case in regular, closed physical systems, under the second law of thermodynamics), but if learning dominates, then entropy will decrease as is the case in learning systems, under the second law of learning: The total entropy of a thermodynamic system does not decrease and remains constant in the thermodynamic equilibrium, but the total entropy of a learning system does not increase and remains constant in the learning equilibrium.

Very clever; introduce a magic variable that allows the theory to avoid the consequences of the second law. Entropy increases overall (which must happen) but can stabilize or decrease locally in an evolving system, like a warm little pond.

The maximum entropy principle states that the probability distribution in a large ensemble of variables must be such that the Shannon (or Boltzmann) entropy is maximized subject to the relevant constraints. This principle is applicable to an extremely broad variety of processes, but as shown below is insufficient for an adequate description of learning and evolutionary dynamics and should be combined with the opposite principle of minimization of entropy due to the learning process, or the second law of learning (see Thermodynamics of Learning and ref. 17). Our presentation in this section could appear oversimplified, but we find this approach essential to formulate as explicitly and as generally as possible all the basic assumptions underlying thermodynamics of learning and evolution.

Special Pleading with Handwaving

If this sounds like special pleading with handwaving, watch how they take a wrong turn prior to this by ascribing vitalistic properties to matter:

The crucial step in treating evolution as learning is the separation of variables into trainable and nontrainable ones. The trainable variables are subject to evolution by natural selection and, therefore, should be related, directly or indirectly, to the replication processes, whereas nontrainable variables initially characterize the environment, which determines the criteria of selection.

Assume a replication process. It’s like a can opener. It allows them to visualize endless things most beautiful emerging from the can if they had the opener. Theoretically, trainable variables q overcome the increasing entropy generated by the nontrainable variables x if the probability distribution p(x|q) favors q. “We postulate that a system under consideration obeys the maximum entropy principle but is also learning or evolving by minimizing the average loss function U(q),” they say. Natural selection, or learning, does that. Therefore, life can emerge naturally.

Convinced? They derive their conclusions with some whiz-bang calculus, but clearly if a magic variable q is inserted, the derivation becomes unreliable even if the operations are sound. For instance, if you define q as “a miracle occurs,” then of course you can prove that life is an emergent property of matter. At that point, further sub-definitions of q into different categories of miracles fail to provide convincing models of reality. Watch them define learning as a decrease in entropy:

If the stochastic entropy production and the decrease in entropy due to learning cancel out each other, then the overall entropy of the system remains constant and the system is in the state of learning equilibrium… This second law, when applied to biological processes, specifies and formalizes Schrödinger’s idea of life as a “negentropic” phenomenon. Indeed, learning equilibrium is the fundamental stationary state of biological systems. It should be emphasized that the evolving systems we examine here are open within the context of classical thermodynamics, but they turn into closed systems that reach equilibrium when thermodynamics of learning is incorporated into the model.

Further handwaving is seen in their definition of “evolutionary temperature” as “stochasticity in the evolutionary process” and “evolutionary potential” as “a measure of adaptability.” Does anyone really want to proceed hearing them compare a population of organisms to an ideal gas?

The origin of life can be identified with a phase transition from an ideal gas of molecules that is often considered in the analysis of physical systems to an ideal gas of organisms that is discussed in the previous section.

A Cameo by Malthus

Reality left the station long ago. Malthus makes a cameo appearance: “Under the statistical description of evolution, Malthusian fitness is naturally defined as the negative exponent of the average loss function, establishing the direct connection between the processes of evolution and learning.” Learning solves every problem in evolution: even thermodynamics! Tweaking Dobzhansky, they say, “[n]othing in the world is comprehensible except in the light of learning.”

The key idea of our theoretical construction is the interplay between the entropy increase in the environment dictated by the second law of thermodynamics and the entropy decrease in evolving systems (such as organisms or populations) dictated by the second law of learning.

What is this “second law of learning”? It’s Vanchurin’s idea that variables can be defined as ones that “adjust their values to minimize entropy.” A miracle happens! Minds can do this; but matter? Sure. It’s bound to happen.

The origin of life scenario within the encompassing framework of the present evolution theory, even if formulated in most general terms, implies that emergence of complexity commensurate with life is a general trend in the evolution of complex systems. At face value, this conclusion might seem to be at odds with the magnitude of complexification involved in the origin of life [suffice it to consider the complexity of the translation system] and the uniqueness of this event, at least on Earth and probably, on a much greater cosmic scale.Nevertheless, the origin of life appears to be an expected outcome of learning subject to the relevant constraints, such as the presence of the required chemicals in sufficient concentrations. Such constraints would make life a rare phenomenon but likely far from unique on the scale of the universe. The universe is sometimes claimed to be fine-tuned for the existence of life. What we posit here is that the universe is self-tuned for life emergence.

We’re Here, Aren’t We?

Koonin’s colleagues never get around to solving the extreme improbabilities for getting the simplest building blocks of life by chance. They never discuss harmful cross-reactions, which are certain to occur due to known chemical laws. And they wave the entropy problem away by inserting magic variables that they define as systems that “adjust their values to minimize entropy.” These systems also magically possess memories! How do they know that? Well, neural networks have them, and life has them. Genes must have evolved to be the carriers of long-term memory. After all, we’re here, aren’t we?

Evidently, the analysis presented here and in the accompanying paper is only an outline of a theory of evolution as learning. The details and implications, including directly testable ones, remain to be worked out.

Indeed.

On mapping the boundaries of evolutions.

How Much Can Evolution Really Accomplish?

Eric H. Anderson

Editor’s note: In 2020, Michael Behe published A Mousetrap for Darwin, a collection of his essays and responses to critics. Professor of biochemistry Laurence Moran argued that Behe had misinterpreted evidence and had misunderstood the significance of chloroquine resistance. This is the first in a two-part response.

In 2007, biochemist Michael Behe had the temerity to ask a question — a question that should have been asked with repeated and urgent sincerity by all biologists since the ink from Darwin’s quill first dried on his manuscript: What can evolution actually accomplish?

The question is at once reasonable and utterly crucial to the evolutionary story. Yet, for the most part it has been ignored in the history of evolutionary thought. The deeply held assumption of nearly all evolutionists is that evolution can do everything. After all, we’re here aren’t we! So there is little point in even asking the question. To be sure, occasional lip service has been paid to this inquiry over the decades, but such efforts typically descend into a question-begging exercise that simply assumes evolution must have this great creative power. Again, we’re here, and so even if we don’t understand the precise mechanisms of evolution, even if we’re still trying to fill in the details, even if there is some as-yet-undiscovered evolutionary mechanism, evolution simply must have this great creative power.

Paleontologist Stephen Jay Gould famously used this tactic, arguing that even if we don’t understand exactly how evolution works, we must still regard evolution as a fact, because, well, things have evolved. Phillip Johnson rightly called out Gould for this self-serving circular attempt to prop up evolution, with Johnson’s careful analysis revealing that Gould’s “fact” of evolution turned out to mean nothing more than the theory.

Unsatisfied with circular evolutionary arguments and lazy reasoning, Behe decided to pose his question to the real-world data. What does the actual evidence show about what evolution can do? Behe approached the problem from a number of angles, the most well-known being his analysis of the appearance of chloroquine resistance in the unicellular malaria parasite Plasmodium falciparum.

Lots and Lots of Cells

In brief, Behe noted that the anti-malarial drug chloroquine had been far more successful against the parasite than many other drugs, with resistance to chloroquine arising only in one out of approximately 10^20 parasite cells, as estimated by immunologist Nicholas White, a well-known expert in malaria research. It’s hard for us to grasp such a number, but for comparison’s sake, astronomers estimate there are only between 10^11 and 10^12 stars in our Milky Way galaxy.

Although the molecular details of chloroquine resistance remained fuzzy at the time of Behe’s 2007 book, The Edge of Evolution, based on the malaria data then available Behe suggested that chloroquine resistance might well require two coordinated mutations. A single point mutation (as had been seen with some other drugs) or a series of individually beneficial mutations should have arisen much more frequently than White’s 10^20 estimate. The data, Behe noted, simply did not fit with such approaches, so a more parsimonious explanation was that two coordinated mutations were required.

Evolutionists, predictably, were upset. Jerry Coyne and Sean Carroll asserted that Behe had to be wrong, just on the principle of the thing. In essence, they argued that oh, yes, chloroquine resistance can too come about by a series of single beneficial step-by-step point mutations. That such a claim flatly contradicted the data was beside the point.

Not lost on careful observers was the irony that Behe had proposed that Plasmodium could in fact acquire two coordinated mutations via evolutionary means. Yet intent on maintaining the lore of “one small step at a time for evolution,” Coyne and Carroll eschewed Behe’s offer of two coordinated mutations. In a creative albeit bizarre kind of reverse-gamble, they wagered, “We’ll see your two mutations and raise it to one!”

Over the next several years, arguments went back and forth, and more ink was spilled by the debaters than by a clumsy apprentice at the print shop. Yet despite the nitpicking of definitions, the fights over math, and the repeated accusations that Behe must not understand how evolution really works, those of us who watched the battle of wits from the sidelines noticed that Behe’s basic question remained awkwardly unanswered by his critics: How much can evolution really accomplish?

Moran and the Luck of the Draw

One of the more engaged critics of Behe’s argument was Dr. Larry Moran, professor of biochemistry at the University of Toronto. Moran seems to be on board with the broader evolutionary narrative, but does not consider himself to be a Darwinist. Not long before Behe published The Edge of Evolution, Moran posted a detailed description of his views on his Sandwalk blog titled “Evolution by Accident.” Moran laid out the case for a non-Darwinian view of evolution, building on Jacques Monod’s argument that “pure chance…is at the very root of the stupendous edifice of evolution,” as well as Gould’s famous replay-the-tape-of-life analogy.

For the most part, I agree with Moran’s assessment of the randomness of evolution, my primary quibble being that Moran doesn’t go far enough in recognizing the role of chance in the evolutionary narrative, specifically in the case of so-called selective events. Upon careful analysis, Darwin’s selection mechanism also collapses to a largely chance-based affair, and so the effort to distance oneself from the shadow of Darwin by embracing random evolution is, to a large extent, a distinction without a difference. Yet that is a nuance and a discussion for another time, should I ever have the honor of the proverbial drink at the pub with Moran.

The key point for readers here is that armed with his chance-centered view of evolution, Moran dove into the debate with Behe over chloroquine resistance. The backs and forths between Moran and Behe (and by their supporters and detractors) throughout the summer of 2014 were too numerous to detail here. Then, following several years of relative peace (at least on this particular front), the battle began anew.

In part to silence the spurious accusation that he doesn’t respond to his critics, in November 2020 Behe published A Mousetrap for Darwin, a collection of his numerous rebuttals to critiques of his three prior books. Included in Mousetrap are several responses to Moran. Moran quickly penned a hurried response on his Sandwalk blog arguing, in essence, that Behe was both wrong about how chloroquine resistance came about and had misinterpreted the mechanisms of evolution.

Behe’s Misunderstanding or Misunderstanding Behe?

Significantly, Moran acknowledges the main thrust of Behe’s argument, noting that:

Behe has correctly indentified [sic] an extremely improbably evolution event; namely, the development of chloroquine resistance in the malaria parasite. This is an event that is close to the edge of evolution, meaning that more complex events of this type are beyond the edge of evolution and cannot occur naturally. [Emphasis added.]

This is a very important acknowledgement, and a reader of The Edge of Evolution might well say to Moran, “Welcome aboard!”

Instead, Moran’s main disagreement (coaxed along at various times by P. Z. Myers, Kenneth Miller, and company) seems to be that Behe has misunderstood how malaria resistance came about. Moran acknowledges that “none of us have a serious problem with this guesstimate [1 in 10^20 malaria-cell replications], but several of us have objected to the way Behe interprets it.”

Flashing back to 2007, we remember Behe had suggested that the simplest explanation for the extreme rarity of resistance to chloroquine was that at least two coordinated mutations were required. This was in stark contrast to the drug atovaquone, for example, which required but a single point mutation, and against which resistance arose faster than the average person could learn to pronounce “Plasmodium falciparum.”

Casey Luskin observed that much indignation was brought to bear by some of Behe’s critics for Behe’s use of the word “simultaneous,” but it was clear to any thoughtful reader of The Edge of Evolution that Behe had never claimed that the two mutations had to arise at the same moment in one fell swoop, such as in the exact same reproduction cycle. His point was simply that the two mutations needed to eventually be together at a particular point in time in a particular cell to confer the needed benefit, regardless of precisely when the mutations arose or which mutation came first. Unlike some of Behe’s critics, Moran, to his credit, granted Behe’s point about the mutations having to be together simultaneously to provide the needed benefit. Moran’s concern was more about the possible routes to chloroquine resistance.

What Guesses Were Reasonable?

It was not at all clear in 2007 — my understanding is that it is still not completely clear — exactly which mutational routes are available to Plasmodium in humans in the wild, nor all the other factors or nuances that might bear on the problem. Moran himself notes that “there are lots of complications and many unknown variables” and that we can “provide estimates” but “can’t give precise calculations.”

The best anyone could do while waiting for more definitive research in 2007 was to make an educated guess as to the exact pathway(s) to resistance. The question is, what guesses were reasonable in light of the malaria data?

Then in 2014, an important paper by Summers et al. shed additional light on the development of chloroquine resistance. Although limited to experiments involving frog oocytes in the lab, this research provided solid experimental evidence detailing the specific mutations involved. The researchers identified two initial routes to chloroquine resistance, with additional mutations leading to “the attainment of full transport activity.” Behe’s critics pounced on this as a possible chink in Behe’s argument, grasping onto the possibility that there might be various ways to achieve chloroquine resistance, including from combinations of more than two mutations.

Behe for his part correctly noted that, if anything, the new research supported his primary argument. Indeed, one of the key takeaways of Summers et al. is that chloroquine resistance is a multi-mutational event, with both of the identified routes to resistance requiring “a minimum of two mutations” to get started. Behe’s 2007 prediction that chloroquine resistance did not result from a series of individually beneficial mutations, but required a multi-mutational event, turned out to be correct. Yet critics still asserted that the key take-home lesson was elsewhere to be found.

In the second part of this response, we’ll examine the data and the implications of chloroquine resistance for the broader evolutionary story.

File under "well said" LXXIX

Matthew 19:24KJV"And again I say unto you, It is easier for a camel to go through the eye of a needle, than for a rich man to enter into the kingdom of God."

Jesus of Nazareth.

meditations of aservantofJEHOVAH

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