And yet even more primeval tech vs. Darwin

 Natural Engineering in the Lifestyle of Honey Bees

Eric Hadin 

A week ago, my wife came in and announced, “There’s a scary-looking bees’ nest in the lilac bush!” Wasps routinely try to build nests around our house, so I was prepared for the worst when I went out to investigate. What I found was a basketball-sized cluster of honey bees — a “swarm.” There was no nest, only a living ball of thousands of bees hanging from a branch. 

I’ve never done any beekeeping, but fortunately, we have some friends who do. We had no idea, but apparently a swarm of bees in May on an easily accessible branch is something to get excited about! Soon, our beekeeper friends rolled up in their pickup truck. One pulled on jacket and bee-proof bonnet, set a large container (a portable hive box) on top of a stepladder underneath the swarm, took hold of the branch, and shook it. The swarm of bees, all festooned together, fell in a clump into the box. Or, rather, most of them did. Hundreds of them draped over the sides, which our undaunted friend scooped into the box (with gloved hands), while hundreds more buzzed around. The couple who came kept reassuring us, “They’re not going to sting because they’re focused on staying with the queen.” I learned that the queen bee’s presence is of utmost importance for the thousands of others.

Thanks for the Bees

Our friends extended thanks for the bees, then went home, while we went inside for a belated supper. The next day, I saw a smaller swarm around a branch in the same lilac bush. Here’s the interesting thing. Our friends said that they didn’t think they had captured the queen since the bees were acting agitated, so they came right back over to recover the remaining small swarm. When they added it to the hive with the bulk of the bees, all of them settled down right away. The queen had come home.

Here was a fascinating example of a finely tuned aspect of living organisms that was surely worth further investigation. A trip to the university library and online research quickly yielded multiple sources of information about honey bees from specialists of all types. As I’ve read up on bee behavior and their life cycles, a striking picture appears of ingenious design in living systems.

Natural Engineering

A recent research article reported on the use of x-ray microscopy to provide three-dimensional, time-resolved details on how bees manufacture their iconic honeycomb structure. Several observations from the authors are worth mentioning:1

Honeycomb is one of nature’s best engineered structures.

Engineers recognize design, and never has good human-level engineering come about by anything other than intelligent design.

Honeycomb is a structure that has both fascinated and inspired humans for millennia, including serving as inspiration for many engineering structures. It is a multifunctional structure that acts as a store for food, a nursery for developing honey bee brood, and a physical structure upon which honey bees live. It is constructed of wax produced by bees in specialized glands in their abdomen. Wax is an expensive commodity and so comb construction can be quite costly for a honey bee colony. Honeycomb is constructed in such a way to minimize wax consumption.

Honeycomb construction is optimized to serve multiple purposes for the bee colony, subject to the constraint of material and labor costs. Sounds like the bees are a responsible engineering firm.

The ability of bees to “know” how to manufacture the structurally optimal hexagonal-packed honeycomb is even more amazing when one considers that the worker bees constructing it hatched less than three weeks earlier.

While not a perfect analogy, a colony of bees may be compared to a multicellular living organism. Each member of the colony seems to know what to do at each stage of its life for the good of the whole “organism.” An isolated bee will soon die, even if supplied with nutrients, suggesting that it is designed to function as part of the whole. 

Arranged by a Designer

We could say that the whole honey bee colony is greater than just the sum of its individual members. This state of affairs usually arises when the individual components of a complex system are specifically arranged by a designer to accomplish a predetermined purpose. Consider any complex electrical or mechanical device. All of the components of my laptop would make a fascinating pile if laid out on a table; but they’re even more fascinating when assembled and functioning together as a whole, according to their designed purpose.

A professor of entomology at Iowa State University, studying the behavior of honey bee colonies, writes:

Each bee appears to specialize, for a time at least, on a particular job. Thinking about this, you may decide that a single bee is somewhat like a single cell of your own body. The work force in charge of a particular job, such as feeding larvae, would then correspond to one of your tissues. And if you follow this analogy further, you may conclude that a colony of honey bees is like an organism — a superorganism.2

Aspects of an organism that manifest in a honey bee colony include caring for developing larvae, securing and processing nutrients (similar to metabolism), tending the queen (whose presence coordinates the behavior of the entire colony), guarding the hive and patrolling for intruders (similar to an immune system), temperature regulation (fanning their wings to cool the hive, clustering and vibrating their wings to heat the cluster of bees), growth of the whole colony in terms of the number of individual bees, reproduction of the “organism” (resulting in the phenomenon of the honey bee swarm), coordination of activities mediated by a variety of communication channels, and a sense of purpose.

Observers of complex, functional systems, whether nonliving or alive, rationally conclude that, “If something works, it’s not happening by accident.”3

Beyond Mere Survival

The honey bee colony “works” and accomplishes a purpose beyond mere survival. It diligently stockpiles nectar which its workers convert to honey in amounts exceeding its needs.4 Honey’s unique ingredients give it value as a food source for humans that has been recognized for millennia.

The high total sugar concentration [primarily fructose and glucose, with a smaller amount of sucrose] in honey is beneficial in that most yeasts cannot ferment in it. Also, together with one other constituent (glucose oxidase), it gives the honey antimicrobial properties, and it can be stored safe from spoilage…5

Beyond the direct production of honey for our use, the role of honeybees as pollinators is of critical importance in agriculture:

Bees and other pollinators play a critical role in our food production system. More than 100 U.S. grown crops rely on pollinators. The added revenue to crop production from pollinators is valued at $18 billion.6

Continuing to ponder bee behavior, comments made by Professor Richard Trump of Iowa State University are instructive:

If a honey bee, with her microbrain, knows what she is doing, this is cause for wonder. If she does not know — if she is fully programmed by those sub-microchips of DNA that come to her as a legacy from her ancestors — this is even greater cause for wonder. It is incredible.7

Here are a couple of examples that may cause us to wonder how bees know how to do what they do. Researchers have found that bees possess an internal organic timer, which in conjunction with an awareness of the rotation of the Earth, allows them to efficiently time their foraging activities to arrive at flowers when pollen sources are at their peak. 

The famous “waggle dance” that a scout bee performs back at the hive after discovering a food source communicates to other bees (by touching, since the inside of the hive is dark) both the distance and the direction of the food in relation to the current position of the sun. Bee keepers have found that if they reorient the honeycomb on which the bee is dancing, the undaunted bee will adapt its dance so that it still correctly communicates the proper direction to the food source.8 Sometimes the dancing scout bee will continue its dance for more than an hour, and over this time, the position of the sun has changed. In response, the bee will compensate for the sun’s movement across the sky by gradually adjusting the angle of its dance.

How Many Lines of Code?

If humans tried to duplicate the capabilities of honey bees by building and programming mini-robots that could fly, how many lines of code would have to be written and executed to make an artificial bee? We can also ask what the likelihood is of all this coded information arising from unguided natural processes. Someone committed to the evolutionary paradigm might answer that any genomic changes that offered a survival advantage would’ve been locked in by the ratchet-like mechanism of natural selection until primitive bee ancestors evolved into the complex, coordinated colonies of honey bees seen today.

Systems engineer Steve Laufmann, co-author of the recent book Your Designed Body, addresses the engineering hurdles facing any proposed evolutionary explanation:

…when evolutionary biologists hypothesize about small and apparently straightforward changes to a species during its evolutionary history, the biologists tend to skip both the thorny engineering details of what’s necessary to make the system work, and the bigger picture of how any system change has to be integrated with all the other systems it interacts with. The result is that biologists tend to massively underestimate the complexities involved.

And here’s the rub: if they’ve massively underestimated those complexities, then they’ve massively underestimated the challenge for any gradual, materialistic evolutionary process to build up these systems a little bit at a time while maintaining coherence and function. 

PP. 324-325

The difficulties outlined by Laufmann are in the context of the human body, but they apply equally well to the complexities of a colony of honey bees. Bee keepers are all too aware of the precarious balance between life and death throughout a single year for a colony of bees. Engineers know that making changes to a delicately balanced complex functional system, even small ones, have a way of upsetting the balance — not towards better function but towards failure and collapse.

Honey bees offer us a glimpse of a remarkable living system involving interdependent, communally cooperative behavior. In some ways, they outshine the best in conscious human attempts to build a thriving society. Perhaps we can learn a thing or two from the humble bee.

Notes

Rahul Franklin, Sridhar Niverty, Brock A. Harpur, Nikhilesh Chawla, “Unraveling the Mechanisms of the Apis mellifera Honeycomb Construction by 4D X-ray Microscopy,” Advanced Materials, Vol. 34, Issue 42, Oct. 20, 2022.

Richard F. Trump, Bees and Their Keepers, (Iowa State University Press, Ames, IA, 1987).

https://evolutionnews.org/2021/12/caltech-finds-amazing-role-for-noncoding-dna/

How do bees make honey? From the hive to the pot | Live Science (accessed 5/28/2023).

Diana Sammataro and Alphonse Avitabile, Beekeeper’s Handbook, (New York: Cornell University Press, 1998). 

pollinator_week_factsheet_06.25.2020 (usda.gov).

Trump, Bees and Their Keepers, p. 78.

Trump, Bees and Their Keepers, pp. 80-1. 

Primeval chronometers vs. Darwinism

 Epigenetic Biotimer Revealed in Flowers

David Coppedge 

Biology should never be considered ordinary. Take almost any biological process, and the details are likely to overwhelm the reader. That is certainly the case with a new paper about flowering in plants. Even in the well-studied lab plant Arabidopsis thaliana, researchers described dozens of genes, proteins, and accessory molecules working together to ensure the proper moment for flowering.

The paper in Plant Cell is difficult to read for laymen, because geneticists have given very odd names to genes and proteins. Then, according to custom, some genes for A. thaliana are written in italics, but other genes and their protein products are italicized in ALL CAPS. One must wade through a jungle of names like KNUCKLES, GIANT KILLER, SPOROCYTLESS, DEFECTIVE ANTHER DEHISCIENCE1, and AT HOOK MOTIF NUCLEAR LOCALIZED PROTEIN18. After first mention in a paper, fortunately, these are usually abbreviated to KNU, GKI, SPL, and so forth, but then it is hard to remember what they do, especially when they all interact in complex ways. 

Complexifying the situation further, the nomenclature rules have changed over time and are not consistent between publications. Some letters are not capitalized, and some have a suffix consisting of letters and numbers to identify a particular allele. There are also rules for mutant forms and wild type forms. The rules may seem like a mess to non-specialists (read about them at Arabidopsis.org), but I suppose the strange mnemonic names are more helpful than hard-to-memorize strings like g2934ab0x or worse (although rules for epigenetic marks are less mnemonic, like H3K27me3 (“histone 3, lysine 27, methyl group 3”). For this reason, I will try to refrain from referring to the labels and focus instead on processes and functions that go on, which are truly amazing.

The Basics

In a nutshell, scientists at the Nara Institute of Science and Technology (NAIST) in Japan identified key genes, proteins, and epigenetic factors that switch on flowering with precision timing. So accurate was the “biotimer” they found, they could predict when flowering would occur, even if they altered some of the components. They created mutants of some components, and with a mathematical model they designed, they could calculate to the day when an apical meristem (i.e., the tip of a growing stem) would switch its stem cells from proliferation mode to differentiation mode and start to grow the parts of the flower. The precision astonished them. Phys.org says,

The intricate process of flower development has long fascinated scientists seeking to unravel the mysteries behind nature’s precision timing. In a study published in the journal The Plant Cell, a research team led by Nara Institute of Science and Technology (NAIST), Japan has shed light on the inner workings of floral meristem termination and stamen development, uncovering a unique mechanism driven by the interplay of genetic and epigenetic factors.

In Southern California where I live, everyone is thrilled when the poppies bloom. Vast acres of the plants bloom together in late March or early April, as if on cue, painting whole valleys and hillsides in golden orange. The flowers can also close up if the temperature drops or the wind blows and then reopen when the sun shines warmly again. How do they do it? In every part of the world, plants show remarkable timing in their flowering: cherry blossoms in Washington DC, tulips in Holland, daylilies in Taiwan. Their secrets remain mysterious. In California, everyone thought the heavy winter rains would yield a poppy superbloom, but it was only modest compared to those of past years during the drought. Somehow, plants sense just the right combination of external cues to put on their best show.

To unlock the secrets of this remarkable system, the researchers devised a mathematical model capable of predicting gene expression timing with astonishing accuracy. By modifying the length of H3K27me3-marked regions within the genes, they successfully demonstrated that gene activation could be delayed or reduced, confirming the influence of this epigenetic timer. The team’s findings offer a novel perspective on how nature controls the gene expression during flower development.

The Histone Code

The story revolves around epigenetic markers on the genes of the A. thaliana stem cells. Over twenty years ago, David Allis (1951-2023) introduced a bold concept: there was another code at work in the genome: a combinatorial regulatory system. In its obituary, Nature Genetics says,

Perhaps Allis’s most famous conceptual contribution to the field of chromatin research was the elaboration of the ‘histone code’ hypothesis more than 20 years ago. This framework suggested that histone post-translational modifications (PTMs), in different combinations, along with the proteins that can ‘write’, ‘read’ or ‘erase’ them, constitute the basis for a gene regulatory code. In other words, certain histone PTMs could label particular chromatin regions and potentially influence their transcriptional activity. Many of these histone PTMs have been used extensively to characterize or infer a cell state, identity and behavior. For example, methylation marks at H3K27 and H3K9 are mostly associated with gene repression, whereas others, such as H3K4 methylation and H3K27 acetylation, are associated with active regulatory regions.

And so it is in A. thaliana, the authors of the current paper show. The genetic code has the blueprint to make the parts; the epigenetic “histone code” has the switch and the timer.

How It Works

The biotimer described in the paper works by a process of “passive dilution” that is cell cycle dependent. The normal condition for the AGAMOUS transcription factor is to repress flowering. This factor, abbreviated AG, is studded with histone markers (H3K27me3) which repress multiple genes required for “floral meristem termination,” the term for the switch to flowering. Stem cells will proliferate (divide) endlessly by mitosis until the switch is thrown to stop making clones of themselves and start differentiating into stamens, pistils, and petals. It reminds me of Paul Nelson’s comment about chicken egg development in the documentary Flight, where he describes how certain cells in the embryo “are committing themselves, in most cases irreversibly, to particular functional roles.”

For flowers to form, the floral meristem (floral stem cells) must irreversibly commit to becoming cells making up the various floral organs (sepals, petals, stamens, and carpels), a process known as floral meristem termination. Proper timing of floral meristem termination involves temporal activation of the transcription factor gene KNUCKLES (KNU) by its upstream regulator AGAMOUS (AG) via cell cycle-dependent dilution of the repressive histone modification at lysine 27 of histone H3 (H3K27me3) along the KNU coding sequence. This intrinsic ‘biotimer’ will activate KNU at precisely the right time to ensure proper flower development.

Passive dilution involves the washing out of the histone markers at each cell division. AG evicts PRC2, a histone methylator, and prevents histone H3 marks on nucleosomes. If a cell has six of these repressive markers at the beginning, the daughter cells will have three after the next cell division. At some point, there will not be enough markers to repress differentiation, and the cell will commit irreversibly to floral meristem termination. By inserting values into their mathematical model of this passive dilution mechanism, they were able to accurately predict when a plant in the lab would commence flowering. They validated the model with mutant forms of the genes, either speeding up or slowing down this mechanistic “countdown timer” operated by the epigenetic code. When one protein was activated too early, it produced short stamens that were sterile. This shows that attention to timing between parts of the system is crucial to successful flower development.

Interestingly, the biotimer was also temperature dependent. The team grew some of the plants at 18°C (64° F) instead of the usual 22° C (72° F) and observed that flowering was delayed. The explanation is that lower temperature slows down mitosis, which slows down the passive dilution mechanism. 

We also observed a delay in KNU activation by growing plants at 18°C, likely due to slower growth kinetics. This observation emphasizes the dynamic regulation of H3K27me3 in response to extracellular and intracellular cues and suggests a role for the cell cycle–dependent biotimer in coordinating the balance between cell proliferation and differentiation.

It’s a wise strategy to ensure that flowers will have good weather conditions for blooming. Temperature is only one external cue that probably affects the timer. “Additional experiments will be necessary,” they say, to clarify the effect of lower temperatures and other external cues. These may include water and nutrient availability, day length, risk of herbivores, presence of fungal partners, or other factors.

Appropriately the paper avoids Darwin. How flowering plants exploded into appearance was an abominable mystery to him. The evolution-free paper and news release used a term alien to unguided natural processes but familiar to engineers and designers of complex systems with multiple cooperating parts: 

Through meticulous investigations in the model plant Arabidopsis thaliana, the team discovered that AG serves as a master conductor, orchestrating gene expression through a process known as cell cycle-coupled H3K27me3 dilution. This remarkable phenomenon involves the dilution of a histone modification called H3K27me3 along specific gene sequences, effectively kickstarting gene activation. The scientists identified several key genes directly regulated by AG at various time points of this cycle.

The study revealed a genetic network tightly controlled by AG, with genes such as KNUCKLES (KNU), AT HOOK MOTIF NUCLEAR LOCALIZED PROTEIN18 (AHL18), and PLATZ10 emerging as critical players. “By unraveling the inner workings of this regulatory circuit, we gained unprecedented insight into the intricate timing mechanisms that drive proper floral meristem termination and stamen development,” says first author Margaret Anne Pelayo.

Orchestration: aside from its well-known meaning in music — getting all the skilled instrumentalists to play their own designed parts at the right time in harmony — it also means “the plans or planning necessary to arrange something or cause something to happen.” To see an automatic mechanism in a humble herb working to achieve orchestration of multiple parts within a stem cell in a meristem as it switches to flower preparation is quite remarkable. Yet even that is just the start of an entire concert of orchestrated masterpieces as the organs develop, the petals take on their shapes and colors, and the completed flower opens for business. Below, watch as a musical orchestra celebrates this biological orchestration. Bravo!

<iframe width="460" height="259" src="https://www.youtube.com/embed/LjCzPp-MK48" title="Time-Lapse: Watch Flowers Bloom Before Your Eyes | Short Film Showcase" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

JEHOVAH'S Magnum opus.

 Proverbs ch.8:22REB"YAHWEH had constituted me the beginning of his way, Before his works At the commencement of that time;"

Proverbs ch.8:30REB"Then I was beside him as a master worker.+

I was the one he was especially fond of+ day by day;

I rejoiced before him all the time;+ "

Micah Ch.5:2REB"Thou therefore Bethlehem Ephrathah, Though little to be among the thousands of Judah Out of thee shall Mine come forth, to be ruler in Israel,—Whose comings forth have been from of old, from the days of age-past time." 

John ch.1:30NLT"He is the one I was talking about when I said, ‘A man is coming after me who is far greater than I am, for he existed long before me.’" 

John ch.6:62NIV"Then what if you see the Son of Man ascend to where he was before!" 

John ch.8:58NASB"Jesus said unto them, Verily, verily, I say unto you, Before Abraham was, I am(Contrast how the NASB's translators render "eimi"in harmony with the surrounding context at John ch.14:9)."

John ch.17:5NASBAnd now You, Father, glorify Me together with Yourself, with the glory which I HAD(Past tense) with(greek.para=alongside) You before the world existed." 

Colossians ch.1:15-17REB"Who is an image of the unseen God, Firstborn(Prototokos) of all creation,—

16 Because in him were created all things(See proverbs 8:30) in the heavens and upon the earth, The things seen and the things unseen, Whether thrones or lordships or principalities or authorities,—They all through him and for him have been created,17 And he is before all And they all in him hold together;" 

Hebrews ch.1:2NASB"[a]in these last days has spoken to us [b]in His Son, whom He appointed heir of all things, through whom(see 

1John ch.1:1NASB"What was from the beginning(Grk.apo arkhe), what we have heard, what we have seen with our eyes, what we have looked at and touched with our hands, concerning the Word(Grk.logos)+ of Life" 

John ch.1:1NASB"In the beginning was the Word(logos), and the Word(logos) was with (The)God(Grk.Ho Theos), and the Word(logos) was God."

John ch.1:3NASB"All things came into being through Him(See proverbs ch.8:30), and apart from Him [b]not even one thing came into being that has come into being. "

Revelation ch.3:14ASV"And to the angel of the church in Laodicea write: These things saith the Amen, the faithful and true witness, the beginning(See proverbs ch.8:22,30) of the creation of God:" 

The ultimate insiders?

Yet even more primeval tech vs. Darwin

 Cellulose Doesn’t Just Happen

David Coppedge 

“Wood” you believe that the most abundant biopolymer on Earth requires a host of machines, genes, proteins, and accessories? Cellulose is only made by life. It doesn’t emerge fully formed from volcanoes or abiotic chemistry. To paraphrase Aristotle, if the art of cellulose manufacture were within atoms, we would have cellulose by the nature of atomic physics.

(Aristotle was reasoning that something else than raw nature was needed for shipbuilding: namely, art, which presupposes intelligence and purpose. “If the art of ship-building were in the wood,” he quipped, “we would have ships by nature.”)

Cellulose is most commonly understood as the chief component of the cell walls of plants. It is also manufactured, however, by some microbes like bacteria and algae, fungi and slime molds, and urochordates (tunicates). Each organism makes cellulose according to its needs; bacteria, for example, do not need the extra machinery to make long fibrils that plants need. 

In a Primer in Current Biology, Lise C. Noack and Staffan Persson (hence N&P) described “Cellulose synthesis across kingdoms.” As evolutionists, they attribute the art of cellulose manufacture to evolution: “Other proteins evolved before the emergence of the hexameric rosette structure,” they say in one place.

Cellulose synthesis is present in all kingdoms of life and is characterized by an evolutionarily conserved BcsA/CesA synthase.

Evolutionary conservation is not evolutionary at all, it goes without saying; it means stasis. But having asserted that cellulose synthesis emerged and evolved (or not), the hard work of explaining its origin is put off the table. Most of the article deals with how cellulose is made.

Building Blocks on Other Building Blocks 

The basic building block of cellulose is the sugar glucose, a six-carbon ring structure with the formula C6H12O6. Notably, glucose is not found in abiotic nature either. It is only a product of living cells. Although NASA has claimed some sugars have been detected in meteorites, I could find no reference to glucose being formed outside of living organisms. 

One NIH paper from 2022 starts, “Gluconeogenesis is the pathway by which glucose is formed from non-hexose precursors such as glycerol, lactate, pyruvate, and glucogenic amino acids.” Already we see, even before cellulose synthesis begins, its monomer glucose must be “formed” by a “pathway” in a living cell. Those words suggest an organized process that assembles prior building blocks. N&P bypass that point, assuming the prior existence of glucose in the cell:

Cellulose consists of glucose molecules connected through beta-1,4-acetal linkages, which are generated by cellulose synthases and result in the formation of unbranched glucan chains.

Bacterial Cellulose Synthase

Surprisingly, N&P’s Figure 1 shows more components in the bacterial synthase machinery than in the plant machinery. 

The protein complex that synthesizes cellulose was first discovered in bacteria, where it consists of a core complex composed of two subunits — BcsA and BcsB — and many accessory proteins, the presence of which varies depending on bacterial species (Figure 1A). BcsA is strictly speaking the cellulose synthase because it carries the cytosolic glycosyltransferase domain, as well as a transmembrane domain that allows for cellulose translocation and a regulatory carboxy-terminal PilZ domain that senses cyclic di-GMP (Figure 2A).

We’re just getting started, and already a supply of previously manufactured glucose molecules are needed in the right place at the right time, where the machinery is embedded in the bacterial inner membrane. Then we need the protein complex BcsA with its two subunits, and “many accessory proteins.” But getting the parts list right is only a beginning. The parts have to work together in functional harmony.

The machinery needs to link the glucose molecules together and then translocate them to the outer membrane. This is done by two more protein complexes, BcsB and BcsC. They won’t work correctly without another component: a regulator that senses cyclic di-GMP, abbreviated c-di-GMP. N&P gloss over that detail, so now we must look that up. Nature Reviews says,

c-di-GMP controls cellular processes at the transcriptional, translational and post-translational level, and through an increasing number of c-di-GMP-binding proteins and riboswitches.

We have lost count of the number of components to make cellulose and get it moved to where it is needed, and this is in a bacterium! Consider just one of the other machines:

BcsB is the cocatalytic subunit or co-polymerase because its presence is required for cellulose polymerization. It contains a periplasmic carbohydrate-binding domain that might guide the glycan chain towards the outer membrane secretory components.

The term cocatalytic implies cooperation between machines. This component, furthermore, must guide the chain to where it is needed. Another machine, BcsZ, regulates the arrangement of the polymers.

Plant Cellulose Synthase

The cellulose machinery in plants has fewer components but more functional requirements. It doesn’t need the two translocators BcsB and BcsC, because the polymers go directly from the inner membrane to the cell wall. Instead of the polymerase BcsA, plants and some algae combine the glucose molecules into polymers with a machine called CesA. 

The authors speculate about a possible ancestral relationship between CesA and the bacterial BcsA synthase, but admit that “the phylogenetic relevance of terminal complex organization is still somewhat unclear.” Whatever; CesA in plants is arranged in geometrically-perfect “rosettes” of six sets of 3 CesA domains held together with three other proteins, PCR, CSR, and NTD. The rosette structure gives plant cellulose its cable-like formation, woven like strands of a rope. These cables confer the strength needed to support tall trees. 

At the risk of getting too deep in the weeds, this quote gives a taste of the complexity of making cellulose. Conserved, remember, means not evolved.

At the level of the amino-acid sequence, the glycosyltransferase domain has four conserved regions: the first three each contain a D residue, whereas the fourth contains a Q(Q/R)xRW motif. The resulting D–D–D–Q(Q/R)xRW motif is conserved in all BcsA and CesA proteins and is essential for glycosyltransferase function. This highlights a remarkable conservation from sequence to tertiary protein folding, indicative of a conserved enzymatic mechanism. Apart from the D–D–D–Q(Q/R)xRW motif, plant and some algae CesA proteins have three additional domains that are likely involved in protein oligomerization: an amino-terminal RING-like zinc-finger domain (NTD), a plant-conserved region (PCR) and a class-specific region (CSR) (Figure 2B). Although the role of the NTD in CesA oligomerization is still unclear, the PCR and CSR domains are thought to be responsible for the rosette architecture of the cellulose synthase complex in plants (Figure 2C).

N&P discuss some of the similarities and differences in these machines within different species. Some parts are interchangeable, they note. Those details do not affect the overall impression that many parts are needed to make cellulose. Bacterial cellulose polymers can be less organized, because they contribute to biofilm formation. In plants, though, the polymers are shaped into microfibrils, sheets, or ribbons.

There is a strong correlation between terminal complex organization and microfibril dimensions. Rosette CesA complexes from plants and algae form small-diameter microfibrils ranging from 2 to 3.5 nm. However, single or multiple row arrangements of terminal complexes can give rise to much wider and thicker microfibrils, up to 25 nm in diameter, or flat ribbons of cellulose up to 100 nm in width. Depending on the structure, cellulose microfibrils engage with a variety of other polysaccharides and glycoproteins to form complex networks.

Just when our heads are spinning trying to remember all the parts, N&P discuss “Additional subunits of the cellulose synthase complexes.” I count at least 17 more proteins “involved in different regulatory aspects of cellulose synthesis.” 

Let’s recap the importance of cellulose with this quote from a chemistry lesson from Imperial College London:

Cellulose is another glucose polymer (molecular weight 150,000-1 million) found in the cell walls of plants. Over 50% of the total organic matter in the world is cellulose. For example, wood is about 50% cellulose, and cotton is almost 100% cellulose. It is a strong, rigid linear molecule, and these features allow it to be used as the main structural support for plants. The glucose units are again held together by linkages, but this time every second glucose unit is flipped over. These links are called b,1:4 linkages, and human bodies do not possess the enzymes necessary to break this bond. Therefore any cellulose we eat passes through the digestive tract undigested, and acts as roughage. Grass feeding animals, such as cows, however, can digest cellulose, since they have extra stomachs to contain the grass for long periods while it is broken down by special bacteria.

Because of the enormous number of parts, machines and regulators involved in cellulose manufacture, we have wood, lumber, and shipbuilding. The art of shipbuilding may not be in the wood, but what would Aristotle have thought about the art of cellulose manufacture therein?

Yet more on why it's design all the way down.

On the "real" multiverse?

It's design all the way down.

 Model Cell Visualized as a Compact Factory

David Coppedge 

In Episode 6 of Michael Behe’s video series Secrets of the Cell, the animator portrayed little human factory workers, robots, and machines at work inside a magnetotactic bacterial cell. The cartoon characters are seen managing energy production, loading docks with miniature forklifts, coding software, packaging the iron-containing magnetosomes for delivery on conveyor belts, and doing all kinds of things that we can relate to at a human level. Real cells, though they operate with many of the same functional requirements, are squishy. They don’t look like the animation. How can we visualize the innards of a cell in a way that relates the actual appearance to the factory-like operations that go on?

Capturing all the interior parts of a cell in their complex relationships took a lot of work, but some researchers have set a new high bar for biophysical imaging. The Allen Institute in Seattle issued news on April 1 that describes their work visualizing the “shape space” of a typical cell. Senior Scientist Matheus Viana explains the thinking:

                 “We know that in biology, shape and function are interrelated, and understanding cell shape is important to understand how the cells function,” Viana said. “We’ve come up with a framework that allows us to measure a cell’s shape, and the moment you do that you can find cells that are similar shapes, and for those cells you can then look inside and see how everything is arranged.”

Shape Space Is Function Space

The first task of their project was to get the exterior shape nailed down. Identifying the shape of healthy genetically engineered stem cells was not easy, because they are squishy. No two are identical, even when grown under the same conditions. Stem cells in the middle of the epithelial tissue sample have different shapes than those on the edges. Complicating the task further is the fact that not all similar cells are performing the same functions at the same time. Some may be undergoing mitosis when observed; this profoundly affects the cell’s shape.

The researchers found that most of their 215,081 cells were bean-shaped or pear-shaped to various degrees. By measuring the “bean-ness” and “pear-ness” of thousands of cells according to 8 shape criteria, they arrived at an average shape. This allowed them to study the locations of 25 organelles and other interior parts which they followed using fluorescent tags.

The result is the rotating model cell shown in the press release. It bears a distant resemblance to Behe’s compartmentalized factory. Notice their own words revealing similarities:

When they looked at the position of the 25 highlighted structures, comparing those structures in groups of cells with similar shapes, they found that all the cells set up shop in remarkably similar ways. Despite the massive variations in cell shape, their internal organization was strikingly consistent.

If you’re looking at how thousands of white-collar workers arrange their furniture in a high-rise office building, it’s as if every worker put their desk smack in the middle of their office and their filing cabinet precisely in the far-left corner, no matter the size or shape of the office. 

One might apply this description to the Behe cell factory image. The control center, import center and delivery center tend to follow a predictable internal organization.

Visualizing Functional Changes During Mitosis

The Allen Institute team’s first dataset comprised a “large baseline population of cells in interphase.” Then, they studied the shapes of cells at the outer edges of epithelial tissues. Both of those datasets involved static images. Things became really interesting when they added the 4th dimension: time. Their crowning achievement was a 3D model incorporating observations of dividing cells — mapping all 25 organelles and structures — during five stages of mitosis. The result is a colorful, interactive “Interactive Mitotic Stem Cell” that biologists will find profoundly interesting to explore at IMSC.AllenCell.org. 

I strongly recommend readers spend a little time at the site. It reminds me of a project described in Illustra’s film Metamorphosis, where biologist Richard Stringer took a time series of MRI images of a butterfly chrysalis, sliced them into hundreds of frames, and built a 3D model of what goes on during the transformation from chrysalis to butterfly. Illustra color-coded the structures so that viewers could watch from any angle as the wings take shape, the digestive system gets dramatically rearranged, and all the new organs for the adult are constructed.

Similarly, in the Allen Cell visualization tool, viewers can watch what happens to each organelle during mitosis. This is a much richer experience than students get in high school biology, where the focus is usually on the chromosomes. Now, one can see what happens to the mitochondria, the Golgi apparatus, the nucleolus, the nuclear envelope, lysosomes, gap junctions, actin filaments and everything else during five mitotic stages. Viewers can spin and magnify the cell, switch the 25 organelles on and off, play a rotation animation, and watch the parts in different degrees of detail.

The team noticed that some organelles stay relatively stable during mitosis, migrating to the apical nodes, while others like the nuclear envelope and Golgi undergo dramatic changes, essentially disintegrating and reorganizing into new structures like marching band players in a “scatter” formation. Biology teachers will love this visualization tool. For ID advocates, it opens new opportunities for design-based hypotheses: for instance, what orchestrates each organelle’s particular sequence of changes from one cell into two cells, and what controls their spatial relationships to other organelles?

The Allen team sees their “shape space” tool as a complement to protein-based studies. 

Other systematic image-based approaches have catalogued the location of human proteins in several cell types and used the locations of proteins and structures within cells to identify differences in intracellular spatial patterns among cells in distinct states. Our work complements these approaches with its focus on analyses of 3D cell organization at the intermediate level of cellular structures (rather than individual proteins), and on the generation of quantitative measurements of distinct aspects of organization, which enables statistical comparisons and provides a more nuanced, systematic definition of cellular organization and reorganization. Together, these studies bring a crucial missing dimension — that is, the spatiotemporal component — to the single-cell revolution. The full image dataset and analysis algorithms introduced here, as well as all the reagents, methods, and tools needed to generate them, are shared in an easily accessible way (https://www.allencell.org/). These data are available to all for further biological analyses and as a benchmark for the development of tools and approaches moving towards a holistic understanding of cell behaviour.

Having a model of a normal healthy cell digitized in a computer, medical professionals will be able to identify abnormal states earlier. Watch the Darwin-free video “How do you measure a human cell?” to witness the excitement they experienced when their model cell was all put together after seven years of work. And this is just the beginning. The new model was all for one cell type, but a human body has many different cell types acting in multiple situations, subject to different pathologies. 

“This study brings together everything we’ve been doing at the Allen Institute for Cell Science since the institute was launched,” said Ru Gunawardane, Ph.D., Executive Director of the Allen Institute for Cell Science. “We built all of this from scratch, including the metrics to measure and compare different aspects of how cells are organized. What I’m truly excited about is how we and others in the community can now build on this and ask questions about cell biology that we could never ask before.”

Viana’s very large team published their results open access in Nature on January 4. The only things that “evolved” in the paper were the scientists’ own intelligently designed techniques for imaging and setting up experiments. Everything else was in “machine language”—

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challengeowing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behavioursthat they determine. Here we reduced this complexity by focusing on cellular organization — a key readout and driver of cell behaviour — at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures.

The Allen team’s pioneering effort to digitize a 3D normal stem cell undergoing mitosis can now be expanded by other teams who want to investigate other cell types — neurons, muscle cells, erythrocytes, bone cells — in any other organism from microbe to mammal. I’m reminded of pictures of various embryonic mammals in the womb: a giraffe taking shape, an elephant, a mouse. Once the basic sequence of gestation was visualized for the human, it became fascinating to look for similarities and differences in other mammals. Similarly, the Allen project visualizing a “model stem cell” begins what will surely lead to additional models for other cell types.

If, as ID advocates know from experience, specified complexity in biology grows as a function of detail, the future looks bright for design apologetics. Leeuwenhoek would have been amazed.

Anecdote

There’s news about magnetotactic bacteria that Dr. Behe discussed in his video. The Helmholtz Association for German Research Centres reports (via Phys.org) that these microbes can remove heavy metals, including uranium, from wastewater. “Due to their structure, they are positively predestined for such a task,” the article says, noting that they can be easily separated from water using magnets. Notable quotes:

Because they exhibit a feature that differentiates them from other bacteria, magnetotactic bacteria form nanoscopic magnetic crystals within the cell. They are arranged like a row of beads and so perfectly formed that humans would currently be unable to reproduce them synthetically. Each individual magnetic crystal is embedded in a protective membrane.

Together, the crystals and membrane form the so-called magnetosome which the bacteria use to align themselves with the Earth’s magnetic field and orientate themselves in their habitat. It also makes them suitable for simple separation processes.

Magnetotactic bacteria can be found in almost any aqueous environment from fresh water to saltwater, including environments with very few nutrients. Microbiologist Dr. Christopher Lefèvre has even discovered them in the hot springs of Nevada.

In search of Adam and Eve?

 Protein Evolution, the Waiting-Time Problem, and the Intriguing Possibility of Two First Parents

Andrew McDiarmid 

On a new episode of ID the Future, host Eric Anderson gets an update on the recent work of Dr. Ann Gauger, Senior Fellow at Discovery Institute’s Center for Science and Culture. Dr. Gauger explains her continuing research into the limits of protein evolution, efforts that are challenging prevailing assumptions about the role of proteins and mutations in a Darwinian account of life. She also discusses her work on the related waiting times problem, demonstrating the difficulty for Darwinian processes in accounting for the diversity we see in biology. In addition, Gauger shares her journey into researching human origins. After being asked to evaluate the scientific case against Adam and Eve, she dove into population genetics to see if monogenesis — the hypothesis that all humans are descended from two first parents — was even a possibility. What she discovered may surprise you. Don’t miss this review of Dr. Gauger’s fascinating and important research. Download the podcast or listen to it Here.

On artificial intelligence and genuine stupidity?

 Breaking ChatGPT: Its Inability to Find Patterns in Numerical Sequences

William A Dembski 

Here’s a sequence of numbers: 2,4,6,8,10,12,… What’s the next number in the sequence? Obviously 14. That one was simple. But what if we make finding the pattern more difficult? It turns out ChatGPT has serious difficulties with numerical patterns. The problem is not just that it can’t figure out the pattern. It makes up patterns that don’t exist. And then it acts as though it knows what’s going on when in fact it is clueless.

Pattern completion tasks like this have been part of aptitude testing for a long time. The NSA and CIA, for instance, have used them to help in hiring analysts. These tasks also epitomize a key obstacle facing artificial intelligence, which is its inability to model abductive inference, or what is also called inference to the best explanation (Erik Larson explains the failure of AGI, or artificial general intelligence, to model this form of inference in his 2021 Harvard University Press book, The Myth of Artificial Intelligence).

Effect to Cause

Inference to the best explanation is effect to cause reasoning. It looks at some effect to be explained and then tries to come up with an explanation. The problem is that the range of possible explanations is unlimited. Somehow humans are able to whittle down that range and engage in successful IBEs (inferences to the best explanation). Computers, however, have no facility in this regard, and LLMs (large language models) based on neural nets, such as ChatGPT, are a case in point. 

It’s one thing to make the theoretical case that these models cannot adequately perform inferences to the best explanation. It’s another to provide concrete examples of this failure. I’m going to do that now. Below are four queries by me to ChatGPT4 for figuring out the underlying pattern of a numerical sequence and then producing additional numbers in the sequence. Producing such additional numbers is straightforward once one sees the pattern.

The breakdown in each of these cases startling. Indeed, the incomprehension is face-palm level. I’ll insert some commentary in [square brackets]. Everything else is from four recent exchanges with ChatGPT4. I made the sequences reasonably difficult. It would be one thing if ChatGPT4 had simply said that it didn’t see the pattern, confessing ignorance. But in each case it pretended to understand the pattern. And in each case it claimed to produce the correct continuation to the pattern — and failed!

For the first of these, I’ll provide the continuation and an explanation of the pattern. For the other three, I’ll simply provide the continuation of the pattern, leaving it as a puzzle to readers to figure out the underlying pattern. Note that unlike the example with which I started this article, I don’t include any breaks (e.g., commas or spaces) between the numbers. 

Numerical Pattern #1: Primes Plus Fibonaccis

My Query:

What is the pattern in the following numbers and what are the next two digits: 3471016213040

ChatGTP’s Response:

This sequence appears to follow a pattern based on the positional value of digits (zero-based) in the decimal representation of pi (π).

π to several decimal places is: 3.14159265358979323846…

The sequence appears to take each digit’s value from pi’s decimal representation as an index, starting from zero:

The 0th digit of pi is 3.

The 1st digit of pi is 1.

The 4th digit of pi is 1.

The 1st digit of pi is 1.

The 5th digit of pi is 9.

The 9th digit of pi is 2.

The 2nd digit of pi is 4.

The 6th digit of pi is 5.

The 5th digit of pi is 9.

The 3rd digit of pi is 1.

The 8th digit of pi is 6.

The 9th digit of pi is 2.

The 7th digit of pi is 9.

So, the sequence corresponds to the decimal representation of pi, where each digit in the sequence represents the index in the decimal representation of pi.

To find the next two digits, we follow the pattern:

The 9th digit of pi is 2.

The 2nd digit of pi is 4.

So, the next two digits in the sequence should be 24. Thus, the extended sequence would be: 347101621304024.

[Me again: This is word salad. ChatGPT seems to find some similarity between the sequence I put forward and the number pi. But that similarity is entirely imaginary. In fact, this number is formed by taking the prime numbers 2, 3, 5, 7, 11, 13, 17, 19, etc. and adding them, number by number to the Fibonacci numbers 1, 1, 2, 3, 5, 8, 13, 21, etc. That method yields 3, 4, 7, 10, 16, 21, 30, 40, which when commas and spaces are removed is just the number I posed in my query to ChatGPT, namely, 3471016213040. The next prime number after 19 is 23, and the next Fibonacci number after 21 is 34, the sum of which is 57. That is the number that ChatGPT should have put down. Instead it put down 24 — not even close. And its explanation was nonsense, betraying complete incomprehension.]

Numerical Pattern #2

My Query:

What is the pattern of the following sequence of numbers (in particular, how would it be continued): 111121112211312211331122211

ChatGTP’s Response:

This sequence appears to be a variant of the “Look-and-Say” sequence. In this sequence, each term describes the previous term. However, in the typical Look-and-Say sequence, each term is made up of two-digit numbers where the first digit is the count and the second digit is what is being counted.

In this variant, the sequence seems to use one-digit counts and what’s being counted.

Here is how the sequence seems to work:

111: Describes one ‘1’ => ’11’

121: Describes one ‘1’, one ‘2’ => ‘1121’

1121: Describes two ‘1’, one ‘2’ => ‘211221’

211221: Describes one ‘1’, one ‘2’, two ‘1’ => ‘121221’

121221: Describes one ‘1’, one ‘2’, two ‘2’, one ‘1’ => ‘11122121’

11122121: Describes three ‘1’, two ‘2’, one ‘1’ => ‘31221131’

31221131: Describes one ‘1’, one ‘3’, two ‘2’, two ‘1’ => ‘11312221’

So, the next term in the sequence after 111121112211312211331122211 should be: 3113112211322112211213322111.

[Me again: In this case ChatGPT got it right that this was a variant of the look-and-say sequence, but it was spinning its wheels when trying to figure out how exactly it was a variant. I leave it as an exercise to the reader to figure out the precise pattern here. The next numbers, which ChatGPT should have put down if it knew what it was doing, would be 13213221. It got that completely wrong.]

Numerical Pattern #3

My Query:

What is the pattern in the following sequence of digits and what six numbers come next: 271828394274528731641187875644998090

ChatGPT’s Response:

This sequence appears to represent the first few digits of the decimal expansion of e (Euler’s number) in groups of two, with each group shifted one digit to the right from the previous group.

Here’s the first part of e to 50 decimal places:

2.71828182845904523536028747135266249775724709369995957…

When we group them in twos and align them with your sequence:

27, 18, 28, 18, 28, 45, 90, 45, 23, 53, 60, 28, 74, 71, 35, 26, 62, 49, 77, 57, 24, 70, 93, 69, 99, 59, 57…

We can see that your sequence lines up with these groups, but each group starts one digit later than the previous group.

So, the next six numbers, each taken from the next digit of e and the one after it, would be:

49, starting from the 49th digit of e.

57, starting from the 50th digit of e.

24, starting from the 51st digit of e.

70, starting from the 52nd digit of e.

93, starting from the 53rd digit of e.

69, starting from the 54th digit of e.

So, the sequence extended by the next six numbers would be:

271828394274528731641187875644998090495724709369.

[Me again: ChatGPT’s explanation is mystifying. I did indeed take the first six digits of Euler’s e to start off this sequence, but I did so in part to misdirect ChatGPT, because it seems to pay attention to the opening of a numerical sequence and then make more of that opening than it deserves. After those first six digits of e, the digits that appear after it bear no resemblance to e. ChatGPT is claiming that the continuation of that sequence is 495724709369. In fact, the continuation is 122557.

Numerical Pattern #4

My Query:

What is the pattern in the following numbers and what five digits are next: 314152718262820442642464088428

ChatGPT Response:

The sequence seems to be an alternation between digits of pi and squares of natural numbers.

Here’s how it works:

The digits of pi are: 3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9,…

The squares of natural numbers are: 1, 4, 9, 16, 25, 36, 49, 64, 81, 100,…

When interwoven, you get: 3 (from pi), 1 (1^2), 4 (from pi), 1 (2^2), 5 (from pi), 9 (3^2), 2 (from pi), 6 (4^2), 5 (from pi), 3 (5^2), 5 (from pi), 8 (6^2), 9 (from pi), 7 (7^2), 9 (from pi), 4 (8^2), 2 (from pi), 6 (9^2), 4 (from pi), 4 (10^2), 6 (from pi), 4 (11^2), 0 (from pi), 8 (12^2), 8 (from pi), 4 (13^2), 2 (from pi), 8 (14^2).

Following this pattern, the next five digits would be from the next digit of pi (8) and the square of the next natural number (15^2 = 225). So the next five digits should be “8225”.

[Me again: This explanation is nonsensical, like the others. Yes, I start with the first five digits of pi and then the first five of Euler’s e. But after that, I’m not trying to evaluate or approximate these numbers. Note that ChatGPT lists the next five digits to continue the pattern as 8225. There are only four digits here. It is, as Wolfgang Pauli used to say, not even wrong. The next five digits should be 48280.]

Conclusion

Finding patterns in numerical sequences seems, on the basis of these four tests, to be a systemic problem and breakdown for ChatGPT. It appears to be emblematic of a more general problem of its being unable to carry out abductive inferences, or what are also known as inferences to the best explanation. The numerical patterns that I considered were not particularly obvious. I plan to do some further tests to see how simple the patterns can be made but where ChatGPT will still fail to uncover them.

ChatGPT’s failure with inference to the best explanation is a gaping hole in its ability to achieve genuine language comprehension. Add to this it’s failure at self-transcendence (as shown by its inability to extract itself from self-referential linguistic situations — see here and also the same problem for Google Bard), and we have good reason to doubt the linguistic comprehension of these systems in general. We should therefore distrust these systems for any serious inquiry or decision.

The scriptures' antitrinitarian bias is unrelenting.

 Hebrews ch.1:1-3KJV"(Grk. Ho Theos)God, who at sundry times and in divers manners spake in time past unto the fathers by the prophets,

2 Hath in these last days spoken unto us by his Son, whom he hath appointed heir of all things, by whom also he made the worlds;

3 Who being the brightness of his glory, and the express image( Grk. kharakter)of his person(hypostasis)...." 

The Father is here identified as ho theos THE God of the O T patriarchs and prophets according to trinitarians the Father is not a God and thus cannot be the God of anyone certainly not the God of the ancient patriarchs the God of the Bible. Jesus is said to be the Kharakter of JEHOVAH'S hypostasis rendered variously nature,substance,person here is part of thayers commentary:

that which has foundation, is firm; hence,

a. that which has actual existence; a substance, real being: 

Thus the verse.3 is rendered in part this way in the NIV:"3The Son is the radiance of God’s glory and the exact representation of his being..." 

Thus the Father being the God is a God in his own right which is a real problem for the creeds which in an effort to retain an appearance of monotheism insists that none of the constituents of the trinity is a God(though being fully God) in his own right. And also a being which in trinitarian theology ought only to be true of the entire trinity itself/himself? The son is spoken of as being the Kharakter of the God's(i.e the Father's) being. Here is thayers commentary in part:

the mark (figure or letters) stamped upon that instrument or wrought out on it; hence, universally, "a mark or figure burned in (Leviticus 13:28) or stamped on, an impression; the exact expression (the image) of any person or thing, marked likeness, precise reproduction in every respect" (cf. facsimile):

Obviously the imprint is not of the same substance/nature as the seal with which it is made. The impress is an artifact of the seal not the other way around thus we find not the slightest hint of this equality between Jesus and his God suggested by Trinitarians but rather the reverse clear indications of JEHOVAH'S transcendent supremacy.

The supremacy of the Father permeates the scriptures II

 John ch.8:54NIV"If I glorify myself ,my glory means nothing. My Father,is the ONE who glorifies me." 

Once more Jesus identifies his Father as the one God of Israel. For Jesus and his fellow Jews the Father and the God were identical,the father was not a member of a collective deity. And can we even conceive of the Father claiming that if he glorified himself his glory would be nothing.(btw why is the Holy Spirit not glorifying him)

John ch.14:6NIV"I am the way the truth and the life. No one comes to the Father except through me." 

Here again we see that the Father and the God are the same person. Unless we wish to claim that Jesus is merely mediator between man and a subsisting member of the God. Also if all members of this Godhead are truly co-equal why is it that only the Father requires a mediator and the Son and the spirit don't. 

John ch.14:28"You heard me say,"I am going away and am coming back to you." If you loved me, you would be glad that I am going to the Father,for the Father (the God) is greater than I" 

The Son's plain declaration that the person identified as the Father is greater than the person identified as the Son really ought to be the end of the matter,unfortunately we have had to witness the most cringe inducing mental gymnastics in connection with this text. 

Hebrews ch.6:13NIV"When (the)God made his (third person singular)promise to Abraham since there was no one greater for him to swear by.." 

JEHOVAH is immutable, so the apostle's declaration holds true at all times and in all places. 

John ch.6:57NIV"Just as (in the same manner that) the living Father sent me and I live because of the Father (or the Father caused me to live),so (or in like manner) the one who feeds on me will live because of me." 

If someone else caused one to live then one is most certainly not the one God of scripture. And the comparison with the way Christ will resurrect faithful followers should be a safeguard against attempts to needlessly mystify the verse. But who caused the Son to live The Father (i.e the God).

Luke ch.18:19NIV""why do you call me good" Jesus answered" no one is good_except (the)God alone."" 

Here is another verse that really ought to be as plain as day as to its meaning ,but regarding which Christendom's theologians have elected for the most appalling mental contortions rather than the plain reading of the text. The Father is good in a way that distinguishes him from even the very best of his Sons. And this distinction is a transcendent one.