Still yet more on Maths v. Darwin.

Top Ten Questions and Objections to Introduction to Evolutionary Informatics
Robert J. Marks II

Five years ago, Gregory Chaitin, a co-founder of the fascinating and mind-bending field of algorithmic information theory, offered a challenge:1

The honor of mathematics requires us to come up with a mathematical theory of evolution and either prove that Darwin was wrong or right!

In  Introduction to Evolutionary Informatics2, co-authored by William A. Dembski, Winston Ewert, and myself, we answer Chaitin’s challenge in the negative: There exists no model successfully describing undirected Darwinian evolution. Period. By “model,” we mean definitive simulations or foundational mathematics required of a hard science.

We show that no meaningful information can arise from an evolutionary process unless that process is guided. Even when guided, the degree of evolution’s accomplishment is limited by the expertise of the guiding information source — a limit we call Basener’s ceiling. An evolutionary program whose goal is to master chess will never evolve further and offer investment advice.

Here I answer ten frequently posed questions about and objections to Introduction to Evolutionary Informatics.

1. Why yet another book dissing Darwinian evolution?

Solomon was right. “Of making many books there is no end, and much study wearies the body.”3 There are gobs of books written about evolution, pro and con. Many are excellent. So what’s so important about Introduction to Evolutionary Informatics? On the topic of evolution, the conclusion is in: There exists no model successfully describing undirected Darwinian evolution. Hard sciences are built on foundations of mathematics or definitive simulations. Examples include electromagnetics, Newtonian mechanics, geophysics, relativity, thermodynamics, quantum mechanics, optics, and many areas in biology. Those hoping to establish Darwinian evolution as a hard science with a model have either failed or inadvertently cheated. These models contain guidance mechanisms to land the airplane squarely on the target runway despite stochastic wind gusts. Not only can the guiding assistance be specifically identified in each proposed evolution model, its contribution to the success can be measured, in bits, as active information.

And, as covered in Introduction to Evolutionary Informatics, we suspect no model will ever exist to substantiate the claims of undirected Darwinian evolution.

2. But Darwinian evolution is so complicated, it can’t be modeled!

If this objection is true, we have reached the same conclusion by different paths: There exists no model successfully describing undirected Darwinian evolution.

3. You model evolution as a search. Evolution isn’t a search.

We echo Billy Joel: “We didn’t start the fire!” Models of Darwinian evolution, Avida and EV included, are searches with a fixed goal. For EV, the goal is finding specified nucleotide binding sites. Avida’s goal is to generate an EQU logic function. Other evolution models that we examine in Introduction to Evolutionary Informatics likewise seek a prespecified goal.

The evolution software Avida is of particular importance because Robert Pennock, one of the co-authors of the first paper describing Avida,4 gave testimony at the Darwin-affirming Kitzmiller et al. v. Dover Area School District bench trial. Pennock’s testimony contributed to Judge Jones’s ruling that teaching about intelligent design violates the establishment clause of the United States Constitution. Pennock testified, “In the [Avida computer program] system, we’re not simulating evolution. Evolution is actually happening.” If true, Avida and thus evolution are a guided search with a specified target bubbling over with active information supplied by the programmers.

The most celebrated attempt of an evolution model without a goal of which we’re aware is TIERRA. In an attempt to recreate something like the Cambrian explosion on a computer, the programmer created what was thought to be an information-rich environment where digital organisms would flourish and evolve. According to TIERRA’s ingenious creator, Thomas Ray, the project failed and was abandoned. There has to date been no success in open-ended evolution in the field of artificial life.5

Therefore, there exists no model successfully describing undirected Darwinian evolution.

4. You are not biologists. Why should anyone listen to you about evolution?

Leave aside that this question reeks of the genetic fallacy used in debate to steer conversation away from the topic at hand and down a rabbit trail of credential defense. The question is sincere, though, and deserves an answer. Besides, it lets me talk about myself.

The truth is that computer scientists and engineers know a lot about evolution and evolution models.

As we outline in Introduction to Evolutionary Informatics, proponents of Darwinian evolution became giddy about computers in the 1960s and 70s. Evolution was too slow to demonstrate in a wet lab, but thousands and more generations of evolution can be put in the bank when Darwinian evolution is simulated on a computer. Computer scientists and engineers soon realized that evolutionary search might assist in making computer-aided designs. In Introduction to Evolutionary Informatics, we describe how NASA engineers used guided evolutionary programs to design antennas resembling bent paper clips that today are floating and functioning in outer space.

Here’s my personal background. I first became interested in evolutionary computation late last century when I served as editor-in-chief of the IEEE6 Transactions on Neural Networks.7 I invited top researchers in the field, David Fogel and his father Larry Fogel, to be the guest editors of a special issue of my journal dedicated to evolutionary computing.8 The issue was published in January 1994 and led to David founding the IEEE Transactions on Evolutionary Computing9 which today is the top engineering/computer science journal dedicated to the topic.

My first conference paper using evolutionary computing was published a year later10 and my first journal publication on evolutionary computation was in 1999.11 That was then. More recently my work, funded by the Office of Naval Research, involves simulated evolution of swarm dynamics motivated by the remarkable self-organizing behavior of social insects. Some of the results were excitingly unexpected12 including individual member suicidal sacrifice to extend the overall lifetime of the swarm.13 Evolving digital swarms is intriguing and we have a whole web site devoted to the topic.14

So I have been playing in the evolutionary sandbox for a long time and have dirt under my fingernails to prove it.

But is it biology? In reviewing our book for the American Scientific Affiliation (ASA), my friend Randy Isaac, former executive director of the ASA, said of our book, “Those seeking insight into biological or chemical evolution are advised to look elsewhere.”15 We agree! But if you are looking for insights into the models and mathematics thus far proposed by supporters of Darwinian evolution that purport to describe the theory, Introduction to Evolutionary Informatics is spot on. And we show there exists no model successfully describing undirected Darwinian evolution.

5. You use probability inappropriately. Probability theory cannot be applied to events that have already happened.

In the movie Dumb and Dumber, Jim Carey’s character, Lloyd Christmas, is brushed off by beautiful Mary “Samsonite” Swanson when told his chances with her are one in a million. After a pause for introspective reflection, Lloyd’s emergent toothy grin shows off his happy chipped tooth. He enthusiastically blurts out, “So you’re telling me there’s a chance!” Similar exclamations are heard from Darwinian evolutionist advocates. “Darwinian evolution. So you’re telling me there’s a chance!” So again, we didn’t start the probability fire. Evolutionary models thrive on randomness described by probabilities.

The probability-of-the -gaps championed by supporters of Darwinian evolution is addressed in detail in Introduction to Evolutionary Informatics. We show that the probability resources of the universe and even string theory’s hypothetical multiverse are insufficient to explain the specified complexity surrounding us.

Besides, a posteriori probability is used all the time. The size of your last tweet can be measured in bits. Claude Shannon, who coined the term bits in his classic 1948 paper,16 based the definition of the bit on probability. Yet there sits your transmitted tweet with all of its a posteriori bits fully exposed. Another example is a posteriori Bayesian probability commonly used, for example, in email spam filters. What is the probability that your latest email from a Nigerian prince, already received and written on your server, is spam? Bayesian probabilities are also a posteriori probabilities.

So a hand-waving dismissal of a posteriori probabilities is ill-tutored. The application of probability in Introduction to Evolutionary Informatics is righteous and the analysis leads to the conclusion that there exists no model successfully describing undirected Darwinian evolution.

6. What about a biological anthropic principle? We’re here, so evolution must work.

Stephen Hawking has a simple explanation of the anthropic principle: “If the conditions in the universe were not suitable for life, we would not be asking why they are as they are.” Gabor Csanyi, who quotes from Hawking’s talk, says, “Hawking claims, the dimensionality of space and amount of matter in the universe is [a fortuitous] accident, which needs no further explanation.”17

“So you’re telling me there’s a chance!”

The question ignored by anthropic principle enthusiasts is whether or not an environment for even guided evolution could occur by chance. If a successful search requires equaling or exceeding some degree of active information, what is the chance of finding any search with as good or better performance? We call this a search-for-the-search. In Introduction to Evolutionary Informatics, we show that the search-for-the-search is exponentially more difficult that the search itself! So if you kick the can down the road, the can gets bigger.

Professor Sydney R. Coleman said after the Hawking’s MIT talk, “Anything else is better [than the ‘Anthropic Principle’ to explain something].”18 We agree. For example, check out our search-for-the-search analysis in Introduction to Evolutionary Informatics.

7. What about the claim that “All information is physical”?

This is a question we have heard from physicists.

In physics, Landauer’s principle pertains to the lower theoretical limit of energy consumption of computation and leads to his statement “all information is physical.”

Saying “All computers are mass and energy” offers a similar nearly useless description of computers. Like Landauer’s principle, it suffers from the same overgeneralized vagueness and is at best incomplete.

Claude Shannon counters Landauer’s claim:

It seems to me that we all define “information” as we choose; and, depending upon what field we are working in, we will choose different definitions. My own model of information theory…was framed precisely to work with the problem of communication.19

Landauer is probably correct within the narrow confines of his physics foxhole. Outside the foxhole is Shannon information which is built on unknown a priori probability of events which have not yet happened and are therefore not yet physical.

We spend an entire chapter in Introduction to Evolutionary Informatics defining information so there is no confusion when the concept is applied. And we conclude there exists no model successfully describing undirected Darwinian evolution.

8. Information theory cannot measure meaning.

Poppycock.

A hammer, like information theory, is a tool. A hammer can be used to do more than pound nails. And information theory can do more than assign a generic bit count to an object.

The most visible information theory models are Shannon information theory and KCS information.20 The consequence of Shannon’s theory on communication theory is resident in your cell phone where codes predicted by Shannon today allow maximally efficient use of available bandwidth. KCS stands for Kolmogorov-Chaitin-Solomonoff information theory named after the three men who independently founded the field. KCS information theory deals with the information content of structures. (Gregory Chaitin, by the way, gives a nice nod-of-the-head to Introduction to Evolutionary Informatics.21)

The manner in which information theory can be used to measure meaning is addressed in Introduction to Evolutionary Informatics. We explain, for example, why a picture of Mount Rushmore containing images of four United States presidents has more meaning to you than a picture of Mount Fuji even though both pictures might require the same number of bits when stored on your hard drive. The degree of meaning can be measured using a metric called algorithmic specified complexity.

Rather than summarize algorithmic specified complexity derived and applied in Introduction to Evolutionary Informatics, we refer instead to a quote from a paper from one of the world’s leading experts in algorithmic information theory, Paul Vitányi. The quote is from a paper he wrote over 15 years ago, titled “Meaningful Information.”22

One can divide…[KCS] information into two parts: the information accounting for the useful regularity [meaningful information] present in the object and the information accounting for the remaining accidental [meaningless] information.23

In Introduction to Evolutionary Informatics, we use information theory to measure meaningful information and show there exists no model successfully describing undirected Darwinian evolution.

9. To achieve specified complexity in nature, the fitness landscape in evolution keeps changing. So, contrary to your claim, Basener’s ceiling doesn’t apply in Darwinian evolution.

In search, complexity can’t be achieved beyond the expertise of the guiding oracle. As noted, we refer to this limit as Basener’s ceiling.24 However, if the fitness continues to change, it is argued, the evolved entity can achieve greater and greater specified complexity and ultimately perform arbitrarily great acts like writing insightful scholarly books disproving Darwinian evolution.

We analyze exactly this case in Introduction to Evolutionary Informatics and dub the overall search structure stair step active information. Not only is guidance required on each stair, but the next step must be carefully chosen to guide the process to the higher fitness landscape and therefore ever increasing complexity. Most of the next possible choices are deleterious and lead to search deterioration and even extinction. This also applies in the limit when the stairs become teeny and the stair case is better described as a ramp. As Aristotle said, “It is possible to fail in many ways…while to succeed is possible only in one way.”

Here’s an anecdotal illustration of the careful design needed in the stair step model. If a meteor hits the Yucatan Peninsula and wipes out all the dinosaurs and allows mammals to start domination of the earth, then the meteor’s explosion must be a Goldilocks event. If too strong all life on earth would be zapped. If too weak, velociraptors would still be munching on stegosaurus eggs.

Such fine tuning is the case of any fortuitous shift in fitness landscapes and increases, not decreases, the difficulty of evolution of ever-increasing specified complexity. It supports the case there exists no model successfully describing undirected Darwinian evolution.

10. Your research is guided by your ideology and can’t be trusted.

There’s that old derailing genetic fallacy again.

But yes! Of course, our research is impacted by our ideology! We are proud to be counted among Christians such as the Reverend Thomas Bayes, Isaac Newton, George Washington Carver, Michael Faraday, and the greatest of all mathematicians, Leonard Euler.25 The truth of their contributions stand apart from their ideology. But so does the work of atheist Pierre-Simon Laplace. Truth trumps ideology. And allowing the possibility of intelligent design, embraced by enlightened theists and agnostics alike, broadens one’s investigative horizons.

Alan Turing, the brilliant father of computer science and breaker of the Nazi’s enigma code, offers a great example of the ultimate failure of ideology trumping truth. As a young man, Turing lost a close friend to bovine tuberculosis. Devastated by the death, Turing turned from God and became an atheist. He was partially motivated in his development of computer science to prove man was a machine and consequently that there was no need for a god. But Turing’s landmark work has allowed researchers, most notably Roger Penrose,26 to make the case that certain of man’s attributes including creativity and understanding are beyond the capability of the computer. Turing’s ideological motivation was thus ultimately trashed by truth.

The relationship between human and computer capabilities is discussed in more depth in Introduction to Evolutionary Informatics.

Take Aways

In Introduction to Evolutionary Informatics, Chaitin’s challenge has been met in the negative and there exists no model successfully describing undirected Darwinian evolution. According to our current understanding, there never will be. But science should never say never. As Stephen Hawking notes, nothing in science is ever actually proved. We simply accumulate evidence.27

So if anyone generates a model demonstrating Darwinian evolution without guidance that ends in an object with significant specified complexity, let us know. No guiding, hand waving, extrapolation of adaptations, appealing to speculative physics, or anecdotal proofs allowed.

Until then, I guess you can call us free-thinking skeptics.

Thanks for listening.

Robert J. Marks II PhD is Distinguished Professor of Electrical and Computer Engineering at Baylor University.

Notes:

(1) Chaitin, Gregory. Proving Darwin: Making Biology Mathematical. Vintage, 2012.

(2) Marks II, Robert J., William A. Dembski, and Winston Ewert. Introduction to Evolutionary Informatics. World Scientific, 2017.

(3) Ecclesiastes 12:12b.

(4) Lenski, R.E., Ofria, C., Pennock, R.T. and Adami, C., 2003. “The evolutionary origin of complex features.” Nature, 423(6936), pp. 139-144.

(5) ID the Future podcast with Winston Ewert. “Why Digital Cambrian Explosions Fizzle…Or Fake It,” June 7, 2017.

(6) IEEE, the Institute of Electrical and Electrical Engineers, is the largest professional society in the world, with over 400,000 members.

(7) R.J. Marks II, “The Joumal Citation Report: Testifying for Neural Networks,” IEEE Transactions on Neural Networks, vol. 7, no. 4, July 1996, p. 801.

(8) Fogel, David B., and Lawrence J. Fogel. “Guest editorial on evolutionary computation,” IEEE Transactions on Neural Networks 5, no. 1 (1994): 1-14.

(9) R.J. Marks II, “Old Neural Network Editors Don’t Die, They Just Prune Their Hidden Nodes,” IEEE Transactions on Neural Networks, vol. 8, no. 6 (November, 1997), p. 1221.

(10) Russell D. Reed and Robert J. Marks II, “An Evolutionary Algorithm for Function Inversion and Boundary Marking,” Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 794-797, November 26-30, 1995.

(11) C.A. Jensen, M.A. El-Sharkawi and R.J. Marks II, “Power Security Boundary Enhancement Using Evolutionary-Based Query Learning,” Engineering Intelligent Systems, vol. 7, no. 9, pp. 215-218 (December 1999).

(12) Jon Roach, Winston Ewert, Robert J. Marks II and Benjamin B. Thompson, “Unexpected Emergent Behaviors from Elementary Swarms,” Proceedings of the 2013 IEEE 45th Southeastern Symposium on Systems Theory (SSST), Baylor University, March 11, 2013, pp. 41-50.

(13) Winston Ewert, Robert J. Marks II, Benjamin B. Thompson, Albert Yu, “Evolutionary Inversion of Swarm Emergence Using Disjunctive Combs Control,” IEEE Transactions on Systems, Man and Cybernetics: Systems, v. 43, #5, September 2013, pp. 1063-1076.

Albert R. Yu, Benjamin B. Thompson, and Robert J. Marks II, “Swarm Behavioral Inversion for Undirected Underwater Search,” International Journal of Swarm Intelligence and Evolutionary Computation, vol. 2 (2013). Albert R. Yu, Benjamin B. Thompson, and Robert J. Marks II, “Competitive Evolution of Tactical Multiswarm Dynamics,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 43, no. 3, pp. 563- 569 (May 2013).

Winston Ewert, Robert J. Marks II, Benjamin B. Thompson, Albert Yu, “Evolutionary Inversion of Swarm Emergence Using Disjunctive Combs Control,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 43, no. 5, September 2013, pp. 1063-1076.

(14) NeoSwarm.com.

(15) Review of Introduction to Evolutionary Informatics, Perspectives on Science and Christian Faith, vol. 69 no. 2, June 2017, pp. 104-108.

(16) Claude E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal 27: 379-423 and 623–656.

(17) Gabor Csanyi “Stephen Hawking Lectures on Controversial Theory,” The Tech, vol. 119, issue 48, Friday, October 8, 1999.

(18) The bracketed insertion in the quote is Csanyi’s, not ours.

(19) Quoted in P. Mirowski, Machine Dreams: Economics Becomes a Cyborg Science (New York: Cambridge University Press, 2002), 170.

(20) Cover, Thomas M., and Joy A. Thomas. Elements of Information Theory. John Wiley & Sons, 2012.

(21) Review for Introduction to Evolutionary Informatics.

(22) Paul Vitányi, “Meaningful Information,” in International Symposium on Algorithms and Computation: 13th International Symposium, ISAAC 2002, Vancouver, BC, Canada, November 21-23, 2002.

(23) Unlike our approach, Vitányi’s use of the so-called Kolmogorov sufficient statistic here does not take context into account.

(24) Basener, W.F., 2013. “Limits of Chaos and Progress in Evolutionary Dynamics.” Biological Information — New Perspectives. World Scientific, Singapore, pp. 87-104.

(25) Christian Calculus.

(26) See, e.g., Penrose, Roger. Shadows of the Mind. Oxford University Press, 1994.

(27) Hawking, Stephen. A Brief History of Time (1988). AppLife, 2014.

Scientism v. Classical liberalism?

Sure, “Teach the Controversy,” Says an Evolutionist – But You Know What’s Coming Next
Sarah Chaffee

PLOS, the “Public Library of Science,” is the publisher of a number of a high-profile open access science journals – PLOS ONE, PLOS Biology, PLOS Medicine, and others. They also publish a range of blogs, including Sci-Ed, which deals with issues relating to science and education. A recent headline there caught my eye,“Go ahead and ‘teach the controversy:’ it is the best way to defend science.”

That’s provocative. But here’s the subtitle: “as long as teachers understand the science and its historical context.” Well, who could disagree?

But you can probably guess what’s coming. The author is Mike Klymkowsky, a University of Colorado Boulder Professor of Molecular, Cellular, and Developmental Biology. Klymkowsky, as expected, says teaching about the evolution controversy is fine as long as you show how absurd that “controversy” really is. In other words, you can expose students to diverse views on Darwinian theory, so long as the takeaway for them is the orthodox evolutionary one.

But I wanted to point out a comment left at the end of the article by someone identified as CWGross, who notes:

There is no good reason to believe the naturalist-materialist, Scientistic proposition that science has epistemological primacy (in fact, it is a self-contradictory axiom), so we automatically fail if we refuse to discuss the limitations of science and its inadequacies in questions of morals, politics, aesthetics, relationships, spirituality, etc. Without doing so, we implicitly teach science as an authoritarian system which WILL be rejected, as you note, when it conflicts with lived moral, political, aesthetic, relational, spiritual, etc. experience. You do it yourself, in your confession of ideological dogmatism: “Yet, as a person who firmly believes in the French motto of liberté, égalité, fraternité, laïcité, I feel fairly certain that no science-based scenario on the origin and evolution of the universe or life, or the implications of sexual dimorphism or racial differences, etc, can challenge the importance of our duty to treat others with respect, to defend their freedoms, and to insure their equality before the law.” You express disapproval over philosophies of theistic evolution while at the same time refusing to entertain the implications of a purely materialistic science for your own liberalism.

Klymkowsky wants to marshal a purely naturalistic science in the classroom. CWGross points out the conflict with Klymkowsky’s own liberal ideology. Only an “authoritarian” approach can bridge the gap, expecting students to embrace both (rigidly materialist science, liberalism) without acknowledging the contradiction.

Our preference is for a pedagogy that is much more modest, and more authentically liberal. Klymkowsky worries that students are “vulnerable to intelligent-design creationist arguments centered around probabilities.” ID isn’t creationism, and there’s a lot more to it than “probabilities,” but never mind. We  oppose pushing intelligent design into public school classrooms.

Instead, we want students and teachers to be able to explore scientific controversies over mechanisms of evolution and the origin of life discussed in mainstream, peer-reviewed scientific publications. Let them study these questions for themselves, and arrive at their own conclusions. Let them struggle, too, with the philosophical implications, but, of course, not in the science classroom.

On the evolution controversy, Klymkowsky would likely benefit from more study himself. His understanding of what we argue about appears to be limited. He writes, “For example, a common attack against evolutionary mechanisms relies on a failure to grasp the power of variation, arising from stochastic processes (mutation), coupled to the power of natural, social, and sexual variation.”

For a start, he should review the discussions from November’s Royal Society meeting. In fact, some of the ideas presented there would be fascinating to share with students. In the world of professional science, at the highest levels, the foundations of evolutionary theory are up for debate. That fact should not be concealed from young people.

Molecular biology's real life cliffhangers v. Darwin.

Hat Grab: Cells Take Extreme Measures to Rescue Their DNA
Evolution News | @DiscoveryCSC

There’s a famous  scene in an Indiana Jones movie  where the hero barely makes it under a closing gate descending on him in an underground tunnel. He rolls under the gate in the nick of time, but his signature fedora comes off. With fractions of a second to spare, he reaches his arm under the gate and snatches the hat.

Something like that happens in the cell. Sometimes, when chromosomes are being winched apart by the spindle into the daughter cells, fragments of DNA break off and become entangled in the spindle’s microtubules. Unless they are rescued and make it into the nuclei of the new cells, disaster could result. The resulting cells will become unstable, resulting in cancer or cell death. Time is of the essence! The cell is following a precisely choreographed screenplay, where thousands of actors must play their roles perfectly at the right time and place. Like the gate descending on Indiana Jones, the cleavage furrow is rapidly constricting the midpoint of the spindle, with those fragments stuck there. Can the cell rescue them in time?

This crisis happens daily in life. Like the city folk above ground, oblivious to Indiana Jones and his frantic brush with death under the streets, we hear and see nothing of the near-catastrophes happening inside our cells. But if it weren’t for the cell’s fast-acting hand, all would be lost. The dramatic true story is told in fascinating news from the University of California, Santa Cruz,under the title, “’Hail Mary’ mechanism can rescue cells with severely damaged chromosomes.” The authors liken what happens to a quarterback’s all-or-nothing long pass in the last seconds of a critical football game. It calls for desperate plays.

William Sullivan calls this a “worst case scenario” for the cell. The potential consequences include cell death or a cancerous cell growing out of control. But Sullivan, a professor of molecular, cell, and developmental biology at UC Santa Cruz, has found that the cell still has one more trick up its sleeve to rescue the broken chromosome.

The latest findings from Sullivan’s lab, published in the June 5 issue of Journal of Cell Biology, reveal new aspects of a remarkable mechanism that carries broken chromosomes through the process of cell division so that they can be repaired and function normally in the daughter cells. [Emphasis added.]

Sullivan’s research team studied a strain of fruit flies that they mutated to increase the incidence of DNA fragmentation. By inserting fluorescent tags, they were able to witness “this amazing mechanism, like a Hail Mary pass with time running out.” What they saw was not unlike Indiana Jones’s arm reaching for his hat.

The mechanism involves the creation of a DNA tether which acts as a lifeline to keep the broken fragment connected to the chromosome….

Sullivan’s research has shown that chromosome fragments don’t segregate with the rest of the chromosomes, but get pulled in later just before the newly forming nuclear membrane closes. “The DNA tether seems to keep the nuclear envelope from closing, and then the chromosome fragment just glides right in at the last moment,” Sullivan said.

It’s a good thing this tether works most of the time. When it doesn’t, the action-adventure movie turns into a horror flick.

If this mechanism fails, however, and the chromosome fragment gets left outside the nucleus, the consequences are dire. The fragment forms a “micronucleus” with its own membrane and becomes prone to extensive rearrangements of its genetic material, which can then be reincorporated into chromosomes during the next cell division. Micronuclei and genetic rearrangements are commonly seen in cancer cells.

Think about what is required for this trick to work. Genes have to construct the tether, and enzymes have to know where to attach it. This means that all the information to pull off this whole stunt has to be written into the script before the director calls, “Action!” Could evolution write a script like that? In the neo-Darwinist version, cells that did not have the tether would die or grow cancerous. The cost of selection would be enormous. All the players and their props would have to learn their roles by chance, figuring out by sheer dumb luck where to be and what to do before a cell could succeed at this stunt and survive. We don’t think Sullivan or his funding agencies are relying on chance to pull that off.

“We want to understand the mechanism that keeps that from happening,” Sullivan said. “We are currently identifying the genes responsible for generating the DNA tether, which could be promising novel targets for the next generation of cancer therapies.”

Sullivan has just received a new four-year, $1.5 million grant from the National Institute of General Medical Sciences to continue this research.

The “Hail Mary pass” is just one of a whole catalog of strategies the cell can draw on to protect its genome. Here’s another strategy announced at Rice University, where researchers determined that “Biology’s need for speed tolerates a few mistakes.”

Biology must be in a hurry. In balancing speed and accuracy to duplicate DNA, produce proteins and carry out other processes, evolution has apparently determined that speed is of higher priority, according to Rice University researchers.

Rice scientists are challenging assumptions that perfectly accurate transcription and translation are critical to the success of biological systems. It turns out a few mistakes here and there aren’t critical as long as the great majority of the biopolymers produced are correct.

Although the researchers are evolutionists, we can see that what they really found is optimization at work (a form of intelligent design in action).

A new paper shows how nature has optimized two processes, DNA replication and protein translation, that are fundamental to life. By simultaneously analyzing the balance between speed and accuracy, the Rice team determined that naturally selected reaction rates optimize for speed “as long as the error level is tolerable.”

When you think about what a cell has to do before it divides, there’s not much room for evolution in the mistakes. Millions of base pairs must be duplicated in a time crunch, while the molecular machinery is in operation. It’s like duplicating a factory while the machinery is running! A smart manager will recognize that the cost of being too precise is not worth the delay if the results are adequate to meet the requirements. They use an analogy we are familiar with:

Kinetic proofreading is the biochemical process that allows enzymes, such as those responsible for protein and DNA production, to achieve better accuracy between chemically similar substrates. Sequences are compared to templates at multiple steps and are either approved or discarded, but each step requires time and energy resources and as a result various tradeoffs occur.

“Additional checking processes slow down the system and consume extra energy,” Banerjee said. “Think of an airport security system that checks passengers. Higher security (accuracy) means a need for more personnel (energy), with longer waiting times for passengers (less speed).”

Despite the one evolution reference, these researchers smell design:

“That makes just as much sense for biology as it does for engineering,” Igoshin said. “Once you’re accurate enough, you stop optimizing.”

We see a similar optimization strategy in news from Brandeis University about double-stranded break (DSB) repair. When one strand of DNA breaks, that’s bad. When both strands of DNA become separated, that’s really bad. Specialized enzymes can inspect and repair these DSBs, but they also have to sacrifice accuracy for speed. The enzymes look for similar sequences to use as a template for the “bandage” that will re-join the strands.

But how perfect does the match have to be? Ranjith Anand, the first author on the Nature paper, said this was one of the central questions that the Haber lab wanted to answer.

They found that repair was still possible when every sixth base in a stretch of about 100 bases was different. Previous studies of RAD51 in the test tube had suggested that the protein had a much more stringent requirement for matching.

That one of the six base pairs could be a mismatch surprised the scientists. The process “is permissive of mismatches during the repairing,” says Anand….

We begin to see a kind of molecular triage going on, as if battlefield medics use whatever is on hand to keep the soldier from dying. “Most damage gets accurately repaired, so the cell is unaffected,” the article says. For somatic cells, imperfect bandages will probably cause no significant harm. Darwinism would require that the mistakes (1) become incorporated into the germline, and (2) provide functional innovations that are positively selected. And thus a wolf became a whale, and a dinosaur took flight into the skies.

Sensible viewers of these action adventures undoubtedly sense good directing, acting, and optimization behind them. Clifford Tabin expressed his amazement about life’s development in Phys.org back in 2013.

When I teach medical students, they’re more interested in the rare people who are born with birth defects, They want to understand embryology so they understand how things go awry, but I’m more interested in the fact that for everyone sitting in my classroom—all 200 of those medical students and dental students — it went right! And every one of them has a heart on the left side and every one of them has two kidneys, and how the heck do you do that?

You are not just a ball of cells, he says; you are the result of mechanical principles that guide the growth of structures through many stages, subject to physical forces, that usually work. And that is indeed astonishing.

On science and the superhuman.

Experience, Rational Debate; Science Depend On The Supernatural
Posted by William J Murray 

I’m going to lay out three basic arguments for belief in the supernatural. First, science itself would not be possible were it not for the effects of unseen, higher-order supernatural causes. Second, science and rational debate would not be possible unless we all have faith in the supernatural – unseen spirits not bound to material causes. Third, each of us has direct personal experience of the supernatural every waking second of every day.

Let’s first define what “supernatural” means. From Merriam-Webster:

of or relating to an order of existence beyond the visible observable universe; especially of or relating to God or a god, demigod, spirit, or devil.
unable to be explained by science or the laws of nature : of, relating to, or seeming to come from magic, a god, etc.
attributed to an invisible agent (as a ghost or spirit)

1: Science depends on measuring supernatural effects

We call these observable, reliable and measurable effects physical laws, forces and universal constants, but those terms are misleading, much like referring to “chance” as a causal agency. Those terms do not represent causal objects or energies we can point at, but are rather descriptions (or models) of observed patterns of behavior of matter and energy for which there is no known or observable cause. The names of these patterns and models are used as if they apply to causal things, but this is a conceptual error. When we say “gravity causes X to fall”, it is not gravity causing it because gravity is the description of the physics of the event. Something “causes it to fall”, but it is not gravity; it is whatever causes the pattern of behavior we call “gravity”.

What is the “natural world”?

The natural world is the set of phenomena that can be described and predicted according to behavioral and interactive constants. However, those laws and constants do not describe where or how such laws and constants exist in the first place, or what they are, or even how they are affecting physical phenomena. These invisible and mysterious causes are supernatural both by definition and logically because they: (1) necessarily relate to an order of existence beyond the observable natural world (since they cause the behavior that defines what we call “the natural world”, (2) are unable to be explained by science or the laws of nature (since science depends upon observing behavioral patterns, and behavioral patterns cannot explain what causes such patterns in the first place), and  (3) these patterns are attributed to invisible, unknown agents (which we erroneously refer to with objectifying terminology –  forces, constants and laws).

The science of the natural world depends upon an unknown, unseen superset of mysterious agencies causing the predictable, reliable, rationally understandable patterns of behavior we observe and describe as the set of natural-occurring phenomena.

2: Science & rational debate depend upon faith in the supernatural

Conducting science requires one to accept that humans have a free will capacity to identify objective facts about the universe and integrate them into theoretical systems that can be properly verified or disproved via true/false statements about experimental outcomes according to abstract principles assumed to be universally valid.  Logically, this means humans must have a capacity that transcends thought as the mere product of happenstance chemical interactions.  IOW, scientists must have faith that humans have the capacity to override whatever thoughts interacting chemicals happen to produce and instead force them down correct, truthful paths from an assumed objective viewpoint. Such a transcendent observational and willful capacity is necessarily supernatural, as the natural is only capable of producing whatever happenstance thoughts and “wilfulness” interacting chemicals happen to produce.

Rational debate depends upon the same assumption; that humans have some kind of non-physical agency which can supervise and override physical thought processes down paths which are correct according to abstract principles which are considered objectively binding. Such an agency is unseen and would necessarily have the power to intervene in the natural patterns producing thoughts and generating conclusions.

It is only by faith in such a supernatural agency and in the supernatural authority of abstract principles accepted as objectively valid that we can expect to be able to overcome the happenstance course of physical cause and effect in the course of our rational and scientific endeavors.

3: Everyone directly experiences the supernatural daily

Each of us experience ourselves as a seat of consciousness with direct, top-down, intentional, prescriptive control (to varying degrees) over the behaviors of many elements of our bodies and thinking processes.  We don’t know how to make various cellular or chemical reactions occur that are necessary for motion and thought. Somehow, without any technical or mechanical knowledge at all, with no understanding of how to initiate or control any of the various chemical and mechanical resources, simple intention can operate what is probably the most highly advanced and complex piece of equipment in the universe with amazing precision. Like a ghost inhabiting a doll out of a movie, our will alone can set physical forces in motion, control them, and stop them on command – no physics, chemistry or mechanical knowledge required whatsoever.  It is precisely like magic.

Furthermore, our will can instantly access any of virtually countless memories without any understanding whatsoever of how the memory process works or how the data retrieval process works.  We can simply intend to write or say something on a subject and gain immediate access to a seemingly never-ending stream of information corresponding to our intent. We can imagine things that do not even exist in the real world, our minds effortlessly rendering a massive virtual reality for us to experience as we daydream or sleep-dream. We cannot see this agency; we cannot explain how it can immediately differentiate from innumerable, variant intents to magically set billions of cellular processes and chemical interactions on a precise course to find memories, find or generate thoughtful, relevant information, or direct our body to precisely achieve a limitless variance of actions.

We experience this self-will as transcending mere physical causation from a higher order of existence, being able to direct the matter and energy of our bodies at will.  We have power over our physical and mental nature exactly like a supernatural ghost in a machine, capable of the most wondrous and amazing feats of physical complexity, creativity and computation without any understanding of how any of it is physically initiated, maintained or controlled.

——————————

That all of these things are considered “mundane” hides their astounding, miraculous, supernatural nature.

On asking the right questions.

Foundational Question: Is Intelligent Design Science?

Steve Laufmann

Editor's note: Mr. Laufmann is a consultant in the growing field of Enterprise Architecture, dealing with the design of very large, very complex, composite information systems that are orchestrated to perform specified tasks in demanding environments.

It's long been said that the path to the right answers lies in asking the right questions. Better questions tend to yield better answers. Not-so-good questions may yield little, or even hide the answers. Some questions illuminate, others obfuscate.

Intelligent design provokes (at least) two foundational questions. I'll briefly address the first of these here, and the second in a subsequent article. First:

Is Intelligent Design Science?

This question is central in the ongoing debate about ID. It's argued about often, sometimes loudly, and occasionally well. Because the topic has been covered thoroughly in this forum and others, I'll present here just five observations about how this question is asked, debated, and answered.

1. The answer is yes, intelligent design is science, though not everyone knows it yet.

However, the reasons behind this are understood neither widely nor well. In my experience there are two common areas of misunderstanding:

First, many people try to apply demarcation criteria that are not appropriate for historical sciences. Demarcation criteria, such as observability, testability, falsifiability, predictive capability, and repeatability, do not generally apply to the causes of unobserved and non-repeatable events in the past. This is as true of the random events in Darwinist theory as it is for the actions of an intelligent agent in ID. Further, philosophers of science have largely abandoned demarcation criteria because there are too many exceptions to such rules.

Second, relatively few people are versed in the methodological nuances of historical sciences, so fail to properly apply these to ID.

2. The dispute over ID has more to do with the definition of science than with any particular scientific merits of ID.

A central question lies at the heart of the origins debate: Are unintelligent causal forces capable of producing the complex orchestrations of information and processing machinery that we see in biology? These orchestrations occur at multiple levels, from molecular machinery within a cell to complex body subsystems (like the skeletal or circulatory systems) to the human mind.

Darwin's proponents argue that the answer is yes, unintelligent forces can do all the necessary work. ID proponents argue the other way. (See "Evolution's Grand Challenge" for my take on this question.)

As historical sciences, both Darwinism and ID deal with past events in space-time history. All historical sciences apply a similar rational process to infer the best explanation (cause) for observed outcomes (effects).

The rules are the same. The process is the same. The reasoning follows a similar path. Clearly, there is methodological equivalence between Darwinism and ID. How then can the Darwinian view be science while ID is not?

3. Most of the fireworks around ID are due to a conflict of worldviews.

In what way does ID offend? Put simply, the problem lies in the answers ID is willing to consider, and with the philosophical implications of those answers.

Many scientists, and a good number of laymen as well, conflate science with materialist philosophy. Many go further and conflate materialist philosophy with rationality.

By remaining open to the possibility that an intelligent, purposeful cause was involved in the history of life, ID presents an affront not to science, but to the presuppositions of materialism.

4. The question about ID as science is asked by different people for different purposes.

Some people genuinely want to understand ID, and whether and how it fits into the objective study of the natural world.

There are good answers for such people. (See the links below for helpful resources.)

A second class of questioner is less interested in a productive discussion. For this person, questions about the scientific bona fides of ID can be used to hold at arm's length any serious conversation about detectable design and intelligent causation.

Once science, materialism, and rationality become conflated, the definition of science can function in two important ways: (a) as sufficient warrant to disregard the claims associated with ID, or (b) as a bludgeon to beat up anyone who seriously entertains ID or associates with its proponents, especially those that otherwise look like perfectly respectable scientists.

One strategy that's proven fairly effective is to label any proposal that dares to challenge a strictly materialistic perspective as "pseudoscience." Once it's tagged with a derogatory label, all related questions can be safely ignored. These tactics are so well rehearsed that they've become pro forma, often a matter of unconscious habit, among the most committed materialists.

It's essential, then, to discern the motives behind the question and to respond accordingly. The first class of questioner sincerely wants to understand how all this fits together. The latter doesn't understand how all it fits together and doesn't want to.

The same question can thus lead to very different conversations. Yet both are worth the effort to engage.

5. Whether ID is science is, at some level, merely a category question.

Consider the origins question from another perspective: What actually happened in space-time history?

If the ID hypothesis is correct and an intelligent cause intervened in the course of past events, but consideration of this possibility is not science, then by this definition science is incapable of fully addressing the question of origins. As a category of investigation, then, science becomes less interesting and it's likely that a new category will be invented that will look just like science, but without the presuppositional baggage of materialism.

If science, or at least biology, is to remain relevant into the next century, it must come to terms with the growing body of evidence that demands a new look at the causality behind biological information and the biological machinery that processes that information. Biologists may well need to acknowledge that they've conflated science with their philosophical prejudices, rethink those cherished 19th-century presuppositions, reconsider the causal mechanisms they're open to, and follow the evidence wherever it leads.

This is a practice that was once called "science."

Caught in the Vortex

ID can easily get caught in the vortex between competing worldviews. While ID may not align with a person's presuppositions, that may have more to do with his view of the universe than with the universe itself.

But the questions persist, and the growing body of evidence is real. The gaps in materialism's explanatory capability are growing, and will not disappear merely because materialists wish them to do so.

Since what we really want to know is how these things came to be, it seems only reasonable to remain open to all likely explanations, at least until they can be discarded based on scientific, rather than philosophical, grounds.

Biology offers profound mysteries ripe for exploration. Given how little we really know about how life works, much less how it came to be, it seems only prudent to keep our options open and follow the data. And to ask good questions.

For a more thorough treatment of ID and science, see the following resources:

• "How Can We Know Intelligent Design is Science?" by Casey Luskin
• Signature in the Cell: DNA and the Evidence for Intelligent Design, by Stephen Meyer
• "What's the Mechanism of Intelligent Design?" by Ann Gauger
• "Answering a Common Complaint: Does Intelligent Design Require Faith?" by Casey Luskin
• "Why Intelligent Design Is Science: A Reading List," by Casey Luskin
• "More on How We Can Know Intelligent Design Is Science," by Casey Luskin

In a subsequent post, I'll look at a second foundational question, which has the potential to yield still more interesting answers.

It's irreducible complexity all the way down.

Local Fitness Landscape Mapped Out for Green Fluorescent Protein
Cornelius Hunter


Proteins are a showstopper for evolution. Proteins consist of dozens, hundreds, even thousands of amino acids and, like most machines, they don't work very well until most of the parts (amino acids in this case) are in place. Half of the amino acids don't give you half the function of a protein. You can read more about this here and here. Now a new paper in Nature, "Local fitness landscape of the green fluorescent protein," reinforces the problem of protein evolution.

One approach to studying how evolution could create new protein designs is to start with some sort of random sequence of amino acids, see how well it works, and try to evolve it to obtain a protein. This is difficult because the protein design space is astronomically huge and proteins are sparse within that space. Any random sequence of amino acids will merely give you junk. Furthermore, the fitness landscape is flat and doesn't provide the guidance evolution needs to move toward functional proteins.

Another approach is to start at the end and work backwards. In other words, start with the finished product -- a functional protein -- and see what the fitness landscape looks like as you swap in different amino acids. This is difficult because, unfortunately for evolution, the fitness landscape drops off precipitously as you move away from the native protein design. Modifying only a few percent of the amino acids leads to a rapid loss of function.

The new paper takes this second approach. It uses a bioluminescent protein known as the green fluorescent protein, taken from the jellyfish Aequorea victoria. It is a wonderful study that systematically mapped out the protein's function (as measured by the protein's fluorescence) for a total of 51,715 different protein sequences that are nearby the native sequence.

The results confirmed what earlier studies had indicated: the protein function drops off dramatically with only a relatively small number of substitutions. But the study also explored the effect of multiple substitutions. It is well known that the effect of two substitutions, for example, is not always simply the sum of their individual effects. They can interact with each other in either positive or negative ways. This is referred to as epistasis.

The new study found that negative epistasis was strong and prevalent. As one of the researchers explained:

We were really surprised when we finally had a chance to look at exactly how the interactions between mutations occur. We also did not expect that almost all the mutations that are only slightly damaging on their own can destroy fluorescence completely when combined together.

It was well understood that evolving a protein is an astronomically unlikely event, and these results indicate it is even more difficult. Those negative results, however, are not reported in the paper. Instead, the paper discusses possible ways that one green fluorescent protein, found in one particular species, may have evolved into other green fluorescent proteins, found in other species. The implications for the initial evolution of a protein are ignored.

Can Islam find a place in western civilisation?:Pros and Cons.

On Darwinism's whale of a tale re:a tail of a whale.

Jonathan Wells on the “Fairy Tale” of Whale Evolution
David Klinghoffer | @d_klinghoffer

Biologist Jonathan Wells, author of Zombie Science slices up the “fairy tale” of whale evolution. He describes three massive acts of re-engineering — under the general headings of breathing, swimming, and reproduction — that would need to be accomplished in turning a land creature into a fully marine one like a whale:

If we wanted to turn a land mammal into a whale, these are a few of the changes we would have to implement. Could the changes have happened accidentally, without design?

People who believe in Darwinian evolution point out that fossils have been found of animals that might have been transitional between fully terrestrial mammals and fully aquatic cetaceans. The fossil animals had legs but probably spent much of their time in the water. Darwinian paleontologists call them “walking whales” because they have a particular ear bone that had previously been found only in cetaceans (though the bone has now been found in an extinct land mammal, Indohyus, that is not classified as a cetacean). But the supposedly transitional animals are anatomically more like amphibious sea lions and otters than whales, and the transition from amphibious to fully aquatic must have happened in a geological blink of an eye.11

Even if the transition were perfectly documented with intermediate forms, however, it would not answer the “how” questions. How did the features needed for a fully aquatic lifestyle originate? How would the hind limbs of a sea lion turn into a fluke (which is very different)? How would a male’s testicles become simultaneously internalized and surrounded by countercurrent heat exchange systems? How would a female develop specialized nursing organs to inject milk forcibly into her calf? Indeed, why would any of these changes occur? Sea lions are already well adapted to their amphibious lives.

An intelligence could have planned to make fully aquatic mammals and designed these features to actualize the plan. But Darwinian theory says no design is allowed, and that leaves us with little more than a fairy tale about how natural selection could turn swimming bears into whales.

The rest is over at Salvo. Read it there.

Darwin thought the ancestral land beast was something like a bear. Even after scrubbing this from updated editions of the Origin of Species, stung by mockery for the suggestion, he continued to hold the view privately. Current theories are hardly more credible.

Biological information v.Darwin

New Peer-Reviewed Paper Challenges Darwinian Evolution
Jonathan M.

Over recent months, papers challenging key elements of Darwinian theory -- the kind of papers which are supposed not to exist -- have increasingly been slipping through the net and finding their way into the peer-reviewed literature. One such paper, "Is gene duplication a viable explanation for the origination of biological information and complexity?," authored by Joseph Esfandier Hannon Bozorgmeh and published online last week in the journal, Complexity, challenges the standard gene duplication/divergence model regarding the origin of evolutionary novelty.

The abstract reports,
All life depends on the biological information encoded in DNA with which to synthesize and regulate various peptide sequences required by an organism's cells. Hence, an evolutionary model accounting for the diversity of life needs to demonstrate how novel exonic regions that code for distinctly different functions can emerge. Natural selection tends to conserve the basic functionality, sequence, and size of genes and, although beneficial and adaptive changes are possible, these serve only to improve or adjust the existing type. However, gene duplication allows for a respite in selection and so can provide a molecular substrate for the development of biochemical innovation. Reference is made here to several well-known examples of gene duplication, and the major means of resulting evolutionary divergence, to examine the plausibility of this assumption. The totality of the evidence reveals that, although duplication can and does facilitate important adaptations by tinkering with existing compounds, molecular evolution is nonetheless constrained in each and every case. Therefore, although the process of gene duplication and subsequent random mutation has certainly contributed to the size and diversity of the genome, it is alone insufficient in explaining the origination of the highly complex information pertinent to the essential functioning of living organisms.
The mechanism under discussion is the phenomenon of gene duplication, which occurs by means of unequal chromosomal crossovers, the retropositioning of spliced mRNA, and copying of an entire chromosome, or even an entire genome. The gene duplication paradigm, as far as the origin of evolutionary novelty is concerned, is as follows: When a gene becomes duplicated, one copy of the gene is retained for its phenotypic utility (e.g. encoding for a protein or functional RNA), while the other copy of the gene is free from selective constraint, and is thus able to mutate and 'explore' alternative combinatorial possibilities (promoted by near neutral drift), in the hope of stumbling upon something useful.

The review paper attempts to "determine the existence and extent of any novel information produced as a consequence of gene duplication." The author further remarks, 
"At stake is whether there is sufficient supporting evidence that the digitally communicated instructions encoded in DNA could have been constructed through known evolutionary processes, or whether the data suggests that an alternative explanation is required as in all codified nonbiological information. Therefore, this would serve as assessing the current arguments regarding the origins of biological and genomic complexity.
The author is careful to delineate what he is describing when he speaks of "information". Carefully contrasting the information present in biological systems with mere Shannon complexity, the author defines a gain in exonic information as "[t]he quantitative increase in operational capability and functional specificity with no resultant uncertainty of outcome." He then procedes to propose means of empirically verifying the role of natural selection in the creation of novel functionality.

Bozorgmehr winds up drawing similar conclusions to those drawn by Behe in his recent Quarterly Review of Biology paper: While many mutations can, at first glance, appear to have resulted in evolutionary novelty (such as in the case of antibiotic resistance), closer inspection reveals that the selected adaptations do not, in fact, result in novel genetic components. Bozorgmehr explains that "[i]n many instances...a loss of function and regulation in a harsh or unusual environment can have a beneficial outcome and thus be selected for -- bacteria tend to evolve resistance to antibiotics in such a way through mutations that would otherwise adversely affect membrane permeability," (see Delcour 2009). One example cited in the paper concerns the acquisition of organophosphorus insecticide resistance in blowflies, which is conferred by a single amino acid substitution in a carboxyl esterase. But this insecticide resistance -- though adaptively selected -- is not a case of neo-functionalization, but rather a loss in enzyme activity (Newcomb et al. 1997).


The paper goes on to examine eight case studies of adaptive evolution which have involved gene duplication, a few examples of which I will summarize in a second post.

Arguing with ghosts?

Revealing Michael Behe, Intelligent Design's "Revolutionary" Biologist

David Klinghoffer

Thanks to a cocktail of misguided public policies, Seattle's downtown core, where we work, is awash with the mentally ill homeless. A sadly familiar figure is the man or woman furiously shouting, cursing, and gesticulating at an unseen conversation partner. There are many such individuals roaming about, crossing paths with the tourists, businesspeople, high-end shoppers, and art museum and symphony patrons. They remind me of a strange feature of the evolution debate.Just as the mentally ill person's fury is directed at an unknown party, much of scientific and popular publishing about evolution is aimed at an unnamed opponent. As a random example, here's an article from Science Daily that crossed my desk this week, "A short jump from single-celled ancestors to animals," reporting a study in the journal Developmental Cell. Without going into detail on the merits of this effort to minimize a glaring difficulty with Darwinism, the article doesn't mention intelligent design. But it's surely directed at us. Major evolutionary transitions are an aspect of life's long history that Darwinian theory can't explain but design can.

Often, when evolutionists argue with an unseen ID proponent, when the context is the Cambrian explosion or life's mysterious origin, the object of their fury is Stephen Meyer. In other contexts, it might be Douglas Axe and Ann Gauger with their incisive questions on protein evolution. Mathematicians William Dembski and David Berlinski are invisible opponents, and so too is our daily reporting and analysis here at Evolution News.

Very often, though, when it comes to the concept of irreducible complexity -- typically unnamed -- the opponent they clearly have in mind is biochemist Michael Behe.

The highly effective new 60-minute documentary Revolutionary: Michael Behe and the Mystery of Molecular Machines, on sale now,makes the invisible visible. The film, written and directed by John West, is an introduction to Behe's thought timed to the 20th anniversary of his revolutionary book  Darwin's Black Box: The Biochemical Challenge to Evolution.I'm proud to have played a small part in bringing the book before the public. As the literary editor at National Review at the time, I commissioned the (laudatory) review from distinguished NYU chemist Robert Shapiro that is briefly shown in the film.

Among other things, Revolutionary is a vindication of Behe, showing how science has refuted objections by Brown University biologist Kenneth Miller and others to Behe's case for design, highlighting molecular machines like the bacterial flagellar motor. Those machines were of course unknown to Darwin, who would be flabbergasted by them. Their exquisite, irreducible complexity is still being investigated and revealed -- making the invisible visible in another sense. For more, see the film's trailer here. Of course Behe is not actually invisible on the stage of biology. His presence is strongly felt. As the film shows, he has changed the terms of evolutionary discussions, an honor that also belongs to another Discovery Institute biologist, Michael Denton, whose book  Evolution: A Theory in Crisis,we learn, inspired Behe's scientific journey.

Oh yes, evolutionists know who they're shouting at, and to prove it, the phrase "irreducible complexity" is sometime allowed to pass their otherwise carefully sealed lips. It's just that Darwin's defenders strain to avoid tainting themselves by directly confronting the mild-mannered professor at Lehigh University. Revolutionary undoes their discretion by introducing us to Behe (and other ID theorists) in a dramatic and personally and intellectually revealing way. It's a fine and accessible primer on ID that tells the story of what really is a scientific revolution.

We see Behe on the Lehigh campus, at home -- just a hundred miles from Dover, Pennsylvania, where he famously clashed with Professor Miller at the 2005 Kitzmiller trial, under the eyes of John E. Jones and his ghostwriters from the ACLU. (That is Judge John E. Jones who, the film amusingly reveals, hoped that Tom Hanks would play him in the movie version of the court battle.) Behe is shown with his family -- talking with his kids, and washing post-supper dishes. The humanizing effect is welcome.

As one paleontologist recounts here, after his own mind was opened to the cogency of design arguments, he met ID scientists and scholars and was surprised to find they bore little resemblance to what he expected based on media caricatures. The shy (as he describes himself), self-effacing, yet stubborn Dr. Behe may also come as a revelation to those who don't know him but assume he must be a cartoon "creationist."

Revolutionary is unlike other ID films I'm familiar with in the way it offers personal stories. One of the most startling concerns University of Idaho microbiologist Scott Minnich, who like Behe, testified at Dover and was censured for it by officials at his university. When contemplating the career perils of coming out for ID and publishing a journal article with Stephen Meyer on the bacterial flagellum, Dr. Minnich happened to be participating as a member of the Iraq Survey Group, assisting the U.S. government in a search for biological and chemical weapons hidden in the country.He was taking shelter, in fact, in Saddam Hussein's Perfume Palace, under approaching mortar fire, as a critical submission deadline loomed. Minnich recalls reflecting that "I may not be here tomorrow morning," as he finally hit the button on his computer to send the file. Now that's a kind of story from the scientific world that you don't hear every day.

Behe, Minnich, Meyer, et al. continue to take fire from opponents who, frankly, don't earn a lot of respect from me by failing to give credit to the authors of the theory they seek to surreptitiously assail. Revolutionary pays fitting tribute Professor Behe, who deserves to be called a hero.

Killer whales v. the walking dead.

A clash of Titans. LIV

And still yet more on the rise of the machines.