Looking for a diamond in the desert: OOL science's errand.

Undeniable? A Conversation with Theistic Evolutionist Hans Vodder
Douglas Axe | @DougAxe

Hans Vodder is a careful thinker, with graduate degrees from the University of St. Andrews (philosophy) and Northwest University (theology) to prove it. I met him a couple of months ago, just before I spoke at the community center in Port Townsend, Washington. Having read my book —  Undeniable  — Hans was instrumental in setting up that event to foster critical dialog over the book’s message.

Although we’re both people of faith, Hans favors the view that God used the evolutionary process to do his work of creating. In other words, while we agree that life is to be attributed to God, we disagree on the plausibility of the evolutionary explanation of life.

When disagreement leads to genuine dialogue, good things are bound to follow. Recognizing this, Hans and I agreed to convert our recent exchange of emails into a public discussion. We don’t know yet whether our conversation will bring us closer to agreement, but even if it doesn’t, each of us will have benefitted from understanding the other better. And we hope you will benefit as well by following the conversation.

Hans started by expressing the following concern about how I use probabilistic reasoning to argue against the standard evolutionary view:

It seems to me (Hans) that the probability distribution might make a big difference if the search has cumulative power and the search space is constrained by environmental factors. Whatever the situation was on early Earth, the specificity of certain features (geographical, climatological, chemical, etc.) would have favored certain outcomes over others: it wouldn’t have been a “level playing field” where any abstract possibility would have had just as much an opportunity for being realized as any other. In other words, might not the environment constrain the search space? If that’s right, the effective search map might be much smaller than a full-blown egg-hunt search.

How much smaller? It’s hard to say, as it seems very difficult to assign probabilistic values for historical events in general. I don’t think these considerations make the probabilistic arguments against evolution go away entirely: the odds do still seem against it. However, I remain extremely doubtful that one can assign anything like an accurate probability value to the historical circumstances under which life, if it evolved on Earth, would have emerged. From where I stand, considering the odds is a cause for caution and humility, but I think we’d be hard pressed to say whether or not a given biological event was “fantastically improbable” or merely “highly improbable.” The calculations cannot be precise enough, so far as I see, to constitute a knock-down argument against evolution.

I (Doug) answered:

I hope I can give you enough of my thinking on the probability question that we can understand each other iteratively.

Let me start by giving you an alternative to the single-sentence summary of the argument I make in Undeniable (see page 160). I could have summarized the argument this way: “Accidental explanations for life necessarily invoke unbelievable coincidences.”

To see why this has to be true, suppose I were to place a small diamond just below the surface of the sand in the Sahara Desert, and you were to set out to find it, knowing nothing other than that it’s in the Sahara. I think we can agree that the challenge for you is nearly impossible. Yes?

We come to that conclusion just by knowing how unsearchably large the Sahara is and how small the thing to be found is. We don’t have to make any assumptions about how you go about searching. Whether you devote years or decades to the diamond hunt, you can’t feasibly search more than an infinitesimal fraction of the Sahara. The fact that this one crucial resource – time — is in limited supply therefore tells us you have only an infinitesimal chance of success.

For example, if a third party (ignorant of the diamond’s location) were to impose geographical constraints on you by saying you can only look in a particular small patch of the Sahara, that wouldn’t help you at all — unless this happened to be the right patch. But for it to be the right patch would be a remarkable coincidence in itself.

The problem with all accidental explanations of life is like this, but far more extreme. You don’t need accurate measurements of probability any more than you needed an accurate measurement of the Sahara. Accuracy is only needed for judging close calls, and this isn’t a close call.

In the end, there’s no way around the fact that for any accidental causes to produce life amounts to a coincidence that’s far too extreme to be credible.

Or at least that’s how I’m thinking of it.

Editor’s note: The conversation continues on Monday.

Why OOL science needs to look past physics and chemistry.

Origin of Life and Information — Some Common Myths
Brian Miller

In previous articles (here,  here, and here) I described the thermodynamic challenges to the origin of life, and I explained the need for information in the first cell to originate from an outside source. Now, I will dispel many of the myths associated with attempts to circumvent the information challenge.

A common attempt to overcome the need for information in the first cell is to equate  information to a reduction in entropy, often referred to as the production of “negative entropy” or N-entropy. This connection is in certain contexts justified by the fact that both  entropy and the Shannon formulation for information use the same mathematics and can be related to probability and uncertainty. For instance, this approach can be used to calculate the amount of work required to  generate specific amounts of information in the amino acid sequences of proteins. However,  entropy is not equivalent to the information in cells, since the latter represents functional information To illustrate the difference, imagine entering the kitchen and seeing a bowl of alphabet soup with several letters arranged in the middle as follows:

REST TODAY AND DRINK PLENTY OF FLUIDS

I HOPE YOU FEEL BETTER SOON

You would immediately realize that some intelligence, probably your mother, arranged the letters for a purpose. Their sequence could not possibly be explained by the physics of boiling water or the chemistry of the pasta.

To continue the analogy, you mention your design inference to your friend Stanley Miller the Third who happens to be an origin-of-life chemist. Stanley believes any attribution of design to pasta sequences in soup is based on the concerned-parent-of-the-gaps fallacy, so he mocks your superstitious beliefs. He then states that the sequence could have come about as a result of the boiling soup cooling to room temperature. Since cold soup has a lower entropy than hot soup, he believes the reduction in entropy could have generated the information in the message. You would immediately recognize that a reduction in thermal entropy has no physical connection to the specific ordering of letters in a meaningful message. The same principle holds true in relation to the origin of life for the required sequencing of amino acids in proteins or nucleotides in DNA.

A related error is the claim that biological information could have come about by some  complex systems or non-linear dynamics processes. The problem is that all such processes are driven by physical laws or fixed rules. And, any medium capable of containing information (e.g., Scrabble tiles lined up on a board) cannot constrain in any way the arrangement of the associated symbols/letters. For instance, to type a message on a computer, one must be free to enter any letters in any order. If every time one typed an “a” the computer automatically generated a “b,” the computer could no longer contain the information required to create meaningful sentences. In the same way, amino acid sequences in the first cell could only form functional proteins if they were free to take on any order.

Moreover, protein chemists have determined that the vast majority of sequences in proteins today are indistinguishable from being purely random, which further confirms that those in the first cell also appeared random to first approximation. Any relevant divergence from pure randomness would have been due to  constraints associated with protein folding, such as the formation of a-helixes. To reiterate, no natural process could have directed the amino acid sequencing in the first cell without destroying the chains’ capacity to contain the required information for proper protein folding. Therefore, the sequences could never be explained by any natural process but only by the intended goal of forming the needed proteins for the cell’s operations (i.e., teleologically).

A third error relates to attempts to explain the genetic code in the first cell by a stereochemical affinity between amino acids and their corresponding codons. According to this model, naturally occurring chemical processes formed the basis for the connection between amino acids and their related codons (nucleotide triplets). Much of the key research promoting this theory  was conducted by biochemist Michael Yarus. He also devised theories on how this early stereochemical era could have evolved into the modern translation system using ribosomes, tRNAs, and supporting enzymes. His research and theories are clever, but his conclusions face numerous challenges.

For instance, Yarus’s experiments did not actually measure the direct attraction between individual amino acids and their related codons, but they tested for binding between amino acids and sets of generated nucleotide chains (aptamers). His team reported that certain amino acids bound to aptamers which contained a higher than random percentage of their corresponding codons or anticodons at the binding sites. However, other researchers were unconvinced by the findings. For instance, Andrew Ellington’s team questioned whether the correlations in these studies were statistically significant, and they argued that his theories for the development of the modern translation system were untenable. Similarly, Eugene Koonin found that the claimed affinities were weak at best and generally unconvincing. He argued instead that the code started as a  “frozen accident” undirected by any chemical properties of its physical components.

More significantly, even if such affinities existed, they would not help in any realistic origin-of-life theory. Yarus’s model centers on codons embedded in longer sequences of nucleotides folding around single amino acids. Any model for translating sequences of codons into chains of amino acids would require a much longer strand of RNA to fold around multiple amino acids and then consistently link them together in the right order. And, these RNAs would eventually have to lose the “non-coding” nucleotides surrounding the relevant codons – while somehow retaining the affinities which had previously required the removed nucleotides – in order to become modern versions of RNA and DNA. Even if such extraordinary feats could occur, the translation would take place in the wrong direction.

Within the presupposed RNA world framework, nucleotide sequences came into being which eventually evolved into RNA-based enzymes. A selective process was believed to replicate the more efficient enzyme-like sequences over others in order to eventually produce “ribozymes” which could perform all of the needed functions for some sort of protocell. However, the ribozyme sequences would have had no relationship via any code to amino acid sequences which could fold into functional proteins. Therefore, any process which could perform the translation would initially be completely useless. Instead, proteins would have needed to come into existence independently through their own selective process, and then their sequences would have needed to be encoded into new RNAs. However, Yarus’s model does not work in reverse. Another process would have been needed for the amino-acid-to-RNA encoding, but the underlying code would not have corresponded to Yarus’s affinity-based code. As a result, the decoding process would have lost the encoded information.

These problems simply highlight one of the challenges for the RNA world hypothesis and for any materialistic explanation for the genetic code. A viable theory would have to explain for both the encoding and decoding several steps:

Amino acids and nucleotides would have to be created in abundance and then brought together. They would have to originate in separate locations, since the conditions needed for their synthesis are quite different, and cross-reactions would have prevented the creation of either. (See  Shapiro’s Origins.)

A functional protein or RNA strand would have to unfold to allow for its sequence to be translated. And, such functional sequences would have to separate themselves from other useless chains. An enormous number of chains would have to exist for a useful sequence to have had any chance of forming.

Individual codons would have to be so strongly attracted to their corresponding amino acids, that they would attach to them for an extended period of time.

Some enzyme-like molecules would have to come along and then polymerize the nucleotides into strands of RNA or the amino acids into proteins.

All useful products would have to migrate to some safe location until they could be encapsulated into a cellular membrane. A viable membrane would have to be selectively semipermeable, so it would allow the right molecules to enter and waist products to leave.

Neither Yarus nor any other researcher has even come close to properly addressing any of these issues in a purely materialistic framework. Nor will they, for any realistic scenario requires intelligent agency to properly coordinate all of these fantastically improbable steps.

Physics v. OOL science.

Tornadoes, Ice, and Cells: The Challenge from Thermodynamics to Origin-of-Life Scenarios
Evolution News | @DiscoveryCSC

On a new episode of ID the Future, physicist and Center for Science & Culture research coordinator Brian Miller talks with host Sarah Chaffee about the thermodynamics of the origin of life. Dr. Miller has been unfolding a four-part series on the subject here at Evolution News, concluding on Monday.

Explaining why materialist theories of origins hit a wall when examining the physics of abiogenesis. Dr. Miller discusses the difference between systems such tornadoes, ice, and living cells. Learn more about equilibrium, self-organization, and how the cell defies natural tendencies towards high entropy and low energy.

Listen to the podcast here, or download it here.

As Dr. Miller concludes, referring to the origin of the genetic code and the information it bears at the heart of the cell:

The encoding and the decoding and the information had to be there all at once, which means it had to preexist the existence of the cell, because it had to exist before it was embodied in physical reality. But the only place that information in a code can exist outside of physical reality is in a mind, and that points very clearly to intelligent design.

Second only to cosmic fine-tuning, this would seem to be the most fundamental challenge to materialism there could be. With no original life, without the guidance and intervention of a designing agent, obviously Darwinian evolution is absolutely nothing even to begin to work with. This is a subject on which materialists are largely silent, and with good reason.

Unsettled science?

There's No Grand Unity Called "Science"
Ralph Dave Westfall 

Editor's note: We are delighted to welcome Dr. Westfall as a new contributor. He is an emeritus professor in the Computer Information Systems Department at California Polytechnic University, Pomona.

Doug Axe's piece at Evolution News the other day was very good ("Public Opinion Is the Ultimate Peer Review"), but it implicitly supports the misconception that science is one big thing. That idea is the primary basis for all pejorative propaganda attacking dissenters as being "anti-science."

It is false to say that there is one single activity to be identified as "Science." In truth, there are only individual fields of study, some of which deserve being called sciences, while others arguably do not. They don't all fit into one overarching category because the methodologies and criteria for what count as valid findings vary so greatly among them. (A cynic might suggest that in contrast to people who do research in psychology, physicists function in a different and not very parallel universe.)

The panorama can be taxonomized as follows. First, divide the fields of study into: (A) the natural or physical sciences, and (B) the social sciences. Then divide the natural sciences, separating (A1) those concerned with homogeneous entities and deterministic (at least in the aggregate) relationships, from (A2) the ones that deal with chaotic processes (like climatology).

Most of the progress in knowledge and technology comes from the (A1) category. Although researchers in the other categories would like you to think they are making comparable contributions to society, they are not.

In the public eye, most of the credibility of "Science" comes from tangible products resulting from the findings of computer science, physics, and chemistry -- for example, computers, jumbo jets, medical technologies such as MRI scanners, etc. Very few question the accomplishments of these kinds of sciences. But that doesn't mean that other sciences produce comparably valid results.

You can take this even further. Throughout history much of progress initially came from the tinkerers, inventors, and engineers. The relevant sciences were discovered or substantially elaborated after the fact to understand why the things they created actually worked. The Romans built great aqueducts two thousand years ago and the church produced grand cathedrals in the Middle Ages before materials science was developed. "The era of the steam engine ... was well into its second century before a fully formed science of thermodynamics had been developed." (See "Engineering Is Not Science.")

And unlike science, replication is not an issue in engineering. People may be able to get away with "scientific findings" that can't be reproduced, but not with bridges that collapse.

The climate change alarmists, for one, seem to think that just calling their opponents anti-science should be more than enough to shut them up, or at least convince others to ignore evidence contrary to their catastrophic warming narrative. However there is an implicit assumption in the "anti-science" epithet: that all sciences produce comparably reliable results. Say, that the science of climatology can be trusted as much as the science of aeronautics.

Here are a couple of thought-provoking questions to ask anyone who accuses others of being anti-science: Would you book a flight on an airplane that was as unreliable as weather forecasts more than ten days in advance? Or whose landings were as inconsistent as the frequently changing dietary recommendations from nutritional research?

No, there is no grand unity called "Science." It might clarify the public debate on these topics if we could help people understand this important point.

Protein folds v. Darwin.

Escape from Randomness: Can Foldons Explain Protein Functional Shapes?
Evolution News | @DiscoveryCSC

Does the subject of protein folding excite you? Read this to see why perhaps it should:

Protein folding is among the most important reactions in all of biology. However, 50 y after C. B. Anfinsen showed that proteins can fold spontaneously without outside help, and despite the intensive work of thousands of researchers leading to more than five publications per day in the current literature, there is still no general agreement on the most primary questions. How do proteins fold? Why do they fold in that way? How is the course of folding encoded in a 1D amino acid sequence? These questions have fundamental significance for protein science and its numerous applications. Over the years these questions have generated a large literature leading to different models for the folding process. [Emphasis added.]

In short, your life depends on protein folding, and the subject provides a classic contest between intelligent design and scientific materialism. That’s enough to make a thoughtful person take notice.

The quoted passage comes from a paper in the Proceedings of the National Academy of Sciences by two biophysicists at the University of Pennsylvania. They review the vast corpus of literature on the subject to assess the best current models for explaining how one-dimensional sequences of amino acids can end up as three-dimensional shapes that perform functional work. To appreciate the challenge, try to assemble a string of beads, some of which have electric charges or attractions to water, that will, when let go, spontaneously fold into a tool. Your cells do something like that all the time, and usually do it right.

Biologic Institute research scientist Douglas Axe has worked on the problem of protein folding for much of his career. He has been joined by another scientist, Discovery Institute’s Ann Gauger, to show why protein folding gives evidence for intelligent design. The subject is also discussed at length in Axe’s most recent book, Undeniable: How Biology Confirms Our Intuition That Life Is Designed (Harper One, 2016).

Here’s the problem for materialism in a nutshell: the number of ways you can assemble amino acids that won’t fold vastly exceeds the ways that will fold. To expect a random process to search “sequence space” (the set of all sequences of amino acids) and arrive at one that folds is so highly improbable, it will likely never occur in multiple universes. Axe followed Michael Denton’s hunch that “functional proteins could well be exceedingly rare” and put some numbers to it. He determined that there is only “one good protein sequence for every 10^74 bad ones” (Undeniable, p. 57). This was about 10 million billion billion billion times more improbable than Denton’s initial estimate.

As Axe goes on to say, materialists didn’t exactly put “out of business” signs on their doors when he published his results. That brings us to the current paper — one of the latest attempts to find a way to avoid the implications of design and find a natural, unguided means of searching sequence space for those elusive folds.

The authors, S. Walter Englander and Leland Mayne, know all too well that random search is hopeless. Even in the 1990s, “Levinthal had contributed the seminal observation that a random search could not account for known folding rates.” Most proteins find their native fold extremely rapidly — some in microseconds. Some need a little help from “chaperones” such as GRO-EL that allow the polypeptide to fold in a barrel-like chamber. In either case, the authors know that random attempts at finding the proper or “native” fold, even for a correctly-sequenced polypeptide, would be far too slow if there were many pathways to the correct fold. This led scientists early on to suspect that proteins follow an energy landscape that nudges them to the native fold, much like a funnel guides ball bearings down a narrow hole. The ball may bounce around in the funnel, but the shape of the energy landscape forces it in the right direction. This is known as energy landscape theory (ELT).

A critical feature of the funneled ELT model is that the many-pathway residue-level conformational search must be biased toward native-like interactions. Otherwise, as noted by Levinthal (57), an unguided random search would require a very long time. How this bias might be implemented in terms of real protein interactions has never been discovered.

The authors are not content with evolutionary just-so stories:

One simply asserts that natural evolution has made it so, formulates this view as a so-called principle of minimal frustration, and attributes it to the shape of the funneled energy landscape. Proteins in some unknown way “know” how to make the correct choices.

Sorry, no dice.

A calculation by Zwanzig et al. at the most primary level quantifies the energy bias that would be required. In order for proteins to fold on a reasonable time scale, the free energy bias toward correct as opposed to incorrect interactions, whatever the folding units might be, must reach 2 kT (1.2 kcal/mol). The enthalpic bias between correct and incorrect interactions must be even greater, well over 2 kcal/mol, because competition with the large entropic sea of incorrect options is so unfavorable. Known amino acid interaction energies, less than 1 kcal/mol (59), seem to make this degree of selectivity impossible at the residue–residue level.

Are we excited yet? This is getting really interesting. The suspense is growing. With randomness out of the question, what will they do?

They basically take a divide-and-conquer approach. Getting a big polypeptide to fold is too hard, but maybe if they can break the problem down into bite-size chunks, they can get to the target without intelligence. After all, it’s much easier to knit an afghan if the granny squares come ready-made so that you don’t have to make each one from scratch. “Quantized” in this manner, the problem becomes more tractable.

The structural units that assemble kinetic intermediates are much the same as the cooperative building blocks of the native protein. This strategy separates the kinetic folding puzzle into a sequence of smaller puzzles, forming pieces of the native structure and putting them into place in a stepwise pathway (Fig. 1B). This is the defined-pathway model.

They give the name “foldon” to a small chain of amino acids “perhaps 15 to 35 residues in size” that folds a little bit. If the polypeptide is composed of a number of these prefabricated foldons, maybe the whole protein will find its native fold quickly, descending the funnel in a stepwise fashion. Experiments unfolding and refolding some proteins actually show this kind of stepwise energy landscape. They like that:

The purpose of this paper is to consider the present status of these quite different models and relate them to the central questions of protein folding — how, why, and the encoding problem. We propose to rely on the solid ground of experiment rather than the countless less-definitive suggestions and inferences that have been so often used in this difficult field.

Empirical rigor; what’s not to like about that? So instead of imagining a correct sequence of amino acids from scratch, they substitute a sequence of foldons, increasing the probability of completing the search in time. Will this work in evolutionary terms?

The opposed defined-pathway model stems from experimental results that show that proteins are assemblies of small cooperative units called foldons and that a number of proteins fold in a reproducible pathway one foldon unit at a time. Thus, the same foldon interactions that encode the native structure of any given protein also naturally encode its particular foldon-based folding pathway, and they collectively sum to produce the energy bias toward native interactions that is necessary for efficient folding.

So how, exactly, did this clever solution emerge without intelligence?

Available information suggests that quantized native structure and stepwise folding coevolved in ancient repeat proteins and were retained as a functional pair due to their utility for solving the difficult protein folding problem.

“Co-evolution” again. So much for empirical rigor. They’re back to just-so storytelling mode. Let’s think this through. Each granny square in the quilt is a product of chance, according to materialist resources. Does a black granny square know that it will fit nicely into a complete quilt following a geometrical pattern of black, red and yellow squares? Unless each granny square has an immediate function, evolution will not preserve it. Similarly, no foldon will be “retained” with some future hope that it might have “utility for solving the difficult protein folding problem.” The foldon couldn’t care less! It had to be functional right when it emerged.

An intelligent designer could plan foldons as a useful strategy for constructing various complex proteins in a modular way. A designer could even preserve useful foldons, much like a computer programmer writes subroutines to use in other programs. Unless each subroutine actually does something useful for the system as a whole, though, what good is it? Say you have a subroutine that says, “Repeat whatever argument arrives in the input register.” Unless the system needs that function as part of what it’s doing, you can run the subroutine till the cows come home and nothing good will come of it.

In short, the foldon strategy doesn’t lower the probability of success, and it doesn’t solve “the difficult protein folding problem” for the evolutionist. It’s all divide and no conquer.

Englander and Mayne make a big deal out of “repeat proteins” that make up about 5 percent of the global proteome. These repeat proteins “have a nonglobular body plan made of small repeated motifs in the 20–40 residue range that are assembled in a linear array.” Are they good candidates for foldons? We know that many proteins contain repetitive structures like alpha coils and beta sheets, but the essence of a functional protein is not its repetitive parts but in its aperiodic parts. We’ve seen this requirement in other types of intelligent design, such as language. Sure; sometimes a series of dashes makes a nice separator between paragraphs, but you won’t get much meaning out of all repetitive sequences. Let’s see if they can do it:

The different families of repeat proteins are very different in detailed structure but within each family the repeats are topologically nearly identical. These observations suggest that repeat proteins arose through repeated duplication at an early stage in the evolution of larger proteins from smaller fragments. Available examples show that globular organization can arise from continued repetitive growth that closes the linear geometry, and by the fusion of nonidentical units, and so would carry forward their foldon-like properties.

The utility of foldons for the efficient folding of proteins might be seen as a dominant cause for the development and retention of a foldon-based body plan through protein evolution. In this view, contemporary proteins came so consistently to their modular foldon-based design and their foldon-based folding strategy because these linked characteristics coevolved. However, the fact that many known foldons bring together sequentially remote segments requires, at the least, some additional mechanism.

This sounds like the evolutionary story that duplicated genes became seeds of new genes. So if we duplicate the line of dashes, and then change some of the dashes to commas, will we get somewhere? Hardly. If we strip out the “mights” and “maybes” of their story, not much is left but the concluding admission that “some additional mechanism” is needed to get folded proteins. (We have one! Intelligence!) And get this: even if you get a polypeptide to fold into a globule, it’s trash unless it actually performs a function.

When scientific materialists began tackling the protein folding problem, they expected that biased energy landscapes leading to deterministic folds would soon be discovered. That didn’t happen.

However, how this propensity might be encoded in the physical chemistry of protein structure has never been discovered. One simply asserts the general proposition that it is encoded in the shape of the landscape and to an ad hoc principle named minimal frustration imposed by natural evolution.

Here they state Axe’s search challenge in their own words:

Quantitative evaluation described above shows that individual residue — residue interaction energies are inadequate for selecting native-like interactions in competition with the large number of competing nonnative alternatives. The assertion that the needed degree of energetic bias is supplied by the shape of an indefinite energy landscape because nature has made it so is — plainly said — not a useful physical — chemical explanation.

The foldon proposal that Englander and Mayne prefer, however, is not any better, despite their praise for it:

The question is what kind of conformational searching can explain the processes and pathways that carry unfolded proteins to their native state. The foldon-dependent defined-pathway model directly answers each of these challenges.

All they have done, however, is displace the challenges from amino acid sequences to foldon sequences. Since the foldons are composed of amino acid sequences, however, nothing is solved; it is still radically improbable to arrive at a sequence that will produce a functional protein without design. No amount of evolutionary handwaving changes that:

Evolutionary considerations credibly tie together the early codevelopment of foldon-based equilibrium structure and foldon-based kinetic folding.

So much for empirical rigor. Evolution did it. Problem solved.

We think not. To rub it in, consider that Axe’s calculation of one in 10^74 sequences being functional is way too generous. If we require that the amino acids be left-handed, and demand that all bonds be peptide bonds, the probability drops to one in 10^164. For a quick demonstration of why this is hoping against all hope, watch Illustra Media’s clever animation from their film Origin, titled, “The Amoeba’s Journey.”

Darwinism's quest for a free lunch rolls on.

Free Energy and the Origin of Life: Natural Engines to the Rescue
Brian Miller

In previous articles, I outlined the thermodynamic challenges to the origin of life  and attempts to address them by evoking self-organizing processes. Now, I will address attempts to overcome the free-energy barriers through the use of natural engines. To summarize, a fundamental hurdle facing all origin-of-life theories is the fact that the first cell must have had a free energy far greater than its chemical precursors. And spontaneous processes always move from  higher free energy to lower free energy.  More specifically, the origin of life required basic chemicals to coalesce into a state of both lower entropy and higher energy, and no such transitions ever occur without outside help in any situation, even at the microscopic level.

Attempted solutions involving external energy sources fail since the input of raw energy actually increases the entropy of the system, moving it in the wrong direction. This challenge also applies to all appeals to  self-replicating molecules, auto-catalytic chemical systems, and self-organization. Since all of these processes proceed spontaneously, they all move from higher to lower free energy, much like rocks rolling down a mountain. However, life resides at the top of the mountain. The only possible solutions must assume the existence of machinery that processes energy and directs it toward performing the required work to properly organize and maintain the first cell.

Modern cells perform these tasks using a host of molecular assemblies, such as ATP synthase and chloroplasts. Ancient cells may not have used these tools, but they had to possess some analogous ones that could extract free energy from such sources as high-energy chemicals, heat, or sunlight. The problem is that this machinery could only be assembled in cells that had such machinery already in full operation. But, no such machinery on the early earth could have existed.

Recognizing this problem, many origins researchers have proposed the existence of naturally occurring settings that effectively functioned as  thermodynamic engines (cycles) or their close equivalent. Proposed systems drive a constantly repeating cyclical that includes three basic components:

Energy and/or material is collected from an outside source.

Energy and/or material is released into the surrounding environment.

Energy is extracted from the flow of energy and matter through the system and redirected toward driving chemical reactions or physical processes that advance the formation of the first cell.

A prime example is the  proposal by geologist Anthonie Muller that thermal cycling generated  ATP molecules, which are a primary source of energy for cellular metabolism. Muller argues that volcanic hot springs heated nearby water which drove a convection cycle with heated water moving away from the spring, then cooling, and then reentering the region near the spring to reheat. The water fortuitously contained ADP molecules, phosphate, and an enzyme (pF1) which combines the ADP and phosphate to form ATP. The thermal cycle synchronized with the enzyme/reaction cycle as follows (components from the thermal cycle described above are labeled):

The pF1 enzyme bound to the ADP and to the phosphate, and then the enzyme folded to chemically bond the two molecules together to form ATP. This reaction moves toward higher free energy, so it would not normally occur spontaneously. However, the folding of the enzyme provides the needed energy (Component 3).

The conformational change of the enzyme gives off heat in the process (Component 2).

The bound complex of the ATP and the enzyme enter the heated region near the hot spring. The heat causes the enzyme to unfold and release the ATP, and in the process of unfolding the enzyme absorbs heat (Component 1). The enzyme is again able to bind to ADP and phosphate, thus restarting the cycle.

The net result is that energy is extracted from the heat flow and redirected toward the production of ATP. The ATP could then provide the needed free energy to organize the first cell.

This scenario, however, has many obvious problems. First, the abiotic production of ADP would have been in extremely small quantities, if anything, due to the challenges of producing its key components, particularly  adenine and ribose,and then linking all of the molecules together properly. Next, the existence of any long amino acid chains is highly unlikely near a hot spring, so the needed enzyme would not have existed. Even if such chains were in abundance, the chances of the amino acids stumbling across the proper sequence to form the correct 3D structure to drive the ATP reaction are next to nil.

Even if all of these problems are ignored, thermal cycling would still not prove a viable source of energy. The existence of ATP does nothing to help promote life unless the energy released by ATP breaking down into ADP and phosphate could be coupled directly to useful reactions, such as combining amino acids into chains. However, such coupling is only possible if aided by information-rich enzymes with the precise structure to bind to the correct molecules associated with the target reactions. For the reasons mentioned above, no such enzymes would have existed.

Another scenario is advanced by biochemist Nick Lane and geochemist Michael Russell.  In their proposal, alkaline hydrothermal vents in acidic oceans could have served as the incubators for life. Their theory is that some membrane-like film formed on the surface of a vent, and a proton gradient (difference in concentration) formed between the acidic outside ocean and the basic interior. Protons would have then transported across the membrane (Component 1 of a thermodynamic cycle) through some crevice or micro-pore, which happened to have a ready supply of catalysts such as iron-sulfur minerals, and then exiting into the vent’s interior (Component 2). The catalysts could then have driven chemical reactions that accessed energy from the proton gradient to build cellular structures and drive a primitive cellular metabolism (Component 3). This process would mimic the modern cell’s ability to access the energy from proton gradients across its membrane using machinery such as ATP synthase. Eventually, a fully functional cell would emerge with its own suite of protein enzymes and the ability to create proton gradients and harvest their energy.

To call this scenario unlikely would be generous. It faces all of the challenges of the previous theory plus the implausibility of random chemical catalysts driving the precise reactions needed for life. Origins researchers will undoubtedly come up with many further creative stories of how natural processes could access energy and how life could form in general. However, they will all face the same basic problems:

Natural Tendencies: The natural tendencies of organic chemical reactions are to move in directions contrary to those needed for the origin of life. For instance, smaller organic chemicals are favored over the larger ones needed for cellular structures. When larger ones do form, they tend toward biologically inert tars. Similarly, chains of life’s building blocks tend to break apart, not grow longer.

Specificity: Countless molecules could form through innumerable chemical pathways. Life requires that a highly specific set are selected and others are avoided. Such selectively requires a precise set of enzymes that each contain highly specified amino acid sequences. A membrane must also form that has a highly specified structure to allow the right materials in and out.

Choreography: Any scenario requires many actions to take place in a highly specific order, in the right locations, and in the right ways. Life’s building blocks must be formed in their own special environments with the correct initial conditions. After they form, they then need to migrate at the right times to the right locations with a proper collection of other molecules to assist in the next stage of development. (See Shapiro’s Origins.)

Efficiency: All proposed makeshift scenarios for energy production are highly inefficient. They would be fortunate to access miniscule amounts of useful energy over extended periods of time. In contrast, bacteria can form billions of high-energy molecules every hour. Their overall energy production when scaled is comparable to that of a high-performance sports car. No natural process could reach the required efficiencies.

Localization: The energy production must be localized inside a cell membrane. No imaginable process could scale down anything like thermal cycling or protein gradient production to fit inside such a small, enclosed volume.

As science advances, the need for intelligent direction becomes increasingly clear. The more successful experiments are at generating the products of life, the greater the need for investigator intervention and the more highly specified the required initial conditions and experimental protocols. This trend will only continue until researchers honestly acknowledge the evidence for design that stares them in the face.

Russia continues to disgrace itself re:religious liberty.

On junk science re:junk DNA.

Jonathan Wells: Zombie Science Keeps Pushing Junk DNA Myth
David Klinghoffer | @d_klinghoffer

The idea that a vast majority of our DNA is “junk,” an evolutionary relic, was just what evolutionists expected. It made sense. Darwin advocates such as Jerry Coyne and Francis Collins advanced it as proof for their claims. Alas for them, it turned out not to be true.

In a video conversation,  Zombie Science  author Jonathan Wells explains how the “Junk DNA” narrative was overturned by good science, including but far from limited to the ENCODE project. Did evolutionary diehards accept this? No! See it here:





If you follow the scientific literature, new functions for “junk” turn up on an almost weekly basis. But the diehards keep insisting on the myth. They strenuously resist a growing body of evidence. Why? Because as Dr. Wells clarifies, evolution for them is not an ordinary scientific theory. It’s a fixed idea. It is an ideology that must be true “no matter what.”

So how evidence is interpreted is wrenched into line with the ideology. And this is what we mean by “zombie science.” Watch and enjoy.

Yet more on the chasm between life and everything else.

“Life Is a Discontinuity in the Universe”
David Klinghoffer | @d_klinghoffer

In a really excellent new ID the Future episode with Todd Butterfield, Steve Laufmann puts the engineering challenge to gradualist evolutionary schemes about as powerfully as one could do. An enterprise architecture consultant, he is a most gifted and entertaining explainer.

There are 37 trillion cells in the human body, some 200 cell types, and 12,000+ specialized proteins. How does it all come together? In human ontogenesis, a 9-month process “turns a zygote into what I call a tax deduction,” says Laufmann. Building a system like this that “leaps together at the same time to create us” (as Butterfield puts it) is the most stunning engineering feat ever accomplished as far as we know.

The discussion features one memorable phrasing after another. “Life is a discontinuity in the universe,” and explaining it means explaining the property of “coherence” associated with engineered systems. Darwinian theory proposes that this was accomplished through random changes gradually accumulating. That entails maintaining “an adaptive continuum” of life where “any causal mechanism that’s proposed has to be able to produce all the changes for every discrete step within one generation.” In this way, unguided evolution could accomplish trivial changes – on the order of skin color, the shape of the nose or the earlobe – but “basics” (how a spleen functions, for example) are quite outside the range.

For the Darwin proponent, it looks hopeless. Laufmann: “Random changes only make the impossible even more impossible. It’s like the impossible squared. It just can’t happen.”

Taking all of this together, what you expect, rather than gradual change as evolutionists picture it, is sudden explosions of complexity. And this is just what the fossil record shows.

It’s a wonderful and enlightening conversation, demonstrating again the necessity of introducing the engineer’s perspective in any realistic estimation of how evolution could work. Darwin proponents almost never seem to consider these challenges. Listen to the podcast here, or download it here.