This is Part 6 of the Creation & Origins series. Part 5 — Soft Tissue That Shouldn’t Exist looked at fossils that shouldn’t have survived. This post moves from bones to code — the argument that DNA isn’t just chemistry, it’s language, and language points somewhere.
A Molecule That Wasn’t Supposed to Say Anything
When James Watson and Francis Crick worked out the double-helix structure of DNA in 1953, both men were committed materialists. The project, as they understood it, was to find the physical, chemical basis of heredity — to show that life really was just matter, with no need for anything beyond it. They got their structure. Then, five years later, Crick went further: he proposed what’s called the sequence hypothesis, the recognition that the chemical subunits running along the interior of the helix weren’t just structural — they functioned like alphabetic characters in a written language, or like the zeros and ones in a section of software. Even confirmed skeptics of intelligent design have made a similar observation in passing: Bill Gates has compared DNA to a software program more advanced than anything humans have written, and Richard Dawkins has described the genetic code as machine code, essentially indistinguishable from a computer’s. Whatever else DNA is, both sides agree it reads like a code.
The Bead-on-a-Rope Version
Ken Ham puts the idea in the most accessible terms in the series: imagine a length of rope strung with red and blue beads. If you know Morse code, that rope can spell out a message — even something as long as the entire Bible, given enough rope. The beads themselves aren’t the information. The information is in the specific order the beads are strung, plus a code system that assigns meaning to that order. DNA works the same way: four chemical letters — A, C, G, T — arranged in a specific sequence carry the instructions for building a heart, a kidney, a brain, the entire biochemical blueprint of an organism, layered instruction on instruction. The molecule is a real, physical thing. The message riding on top of it is something else again.
Tracing the Effect Back to Its Cause
This is where the philosophical version of the argument gets sharper. Stephen Meyer’s case runs like this: in every other instance we’ve ever directly observed — software, hieroglyphics, a newspaper headline, a radio signal — specified, functional information has one known source: a mind. We’ve never observed matter, left to itself, generating that kind of information from scratch. So when we find specified information inside a living cell, the same reasoning that lets us infer a cause from an effect anywhere else in science should let us infer a mind here too.
It’s worth naming the obvious objection, because it’s a fair one: isn’t this circular? You’re defining information as the kind of thing minds produce, then finding it in DNA, then concluding a mind must have produced it. Meyer’s reply is that the inference isn’t definitional, it’s retrodictive — the same backward reasoning from effect to cause that underwrites fields like forensics or the search for extraterrestrial intelligence. Astronomers scanning the sky for alien signals aren’t looking for atoms or energy; they’re looking for exactly this kind of specified pattern, on the assumption that finding it would indicate a mind, because that’s the only cause ever confirmed to produce it. Meyer’s argument borrows that same logic and points it at the cell instead of the stars. Whether that’s a legitimate extension of the reasoning or a question-begging one is exactly the kind of thing readers should weigh for themselves — it’s a strong inference from uniform experience, not a direct observation of what actually happened four billion years ago.
The Wistar Problem
Even granting that DNA carries a code, evolutionary theory needs a mechanism that can rewrite that code over time — and this is where a stranger episode comes in. In 1966, the Wistar Institute in Philadelphia hosted a conference bringing mathematicians, physicists, and computer engineers face to face with evolutionary biologists, in a meeting that became known, only half-jokingly, as Mathematical Challenges to the Neo-Darwinian Theory of Evolution. The mathematicians’ objection was simple: in every digital system they understood, random changes to a functioning sequence overwhelmingly degrade it long before they produce something new and functional. If DNA is genuinely that kind of digital system, they asked, why should biology be exempt from the same math?
That challenge has since moved from a thought experiment to a lab result. Molecular biologist Daniel Tawfik ran mutagenesis experiments on protein folds — the specific three-dimensional shapes a protein has to hold to function — and found that somewhere between three and fifteen random mutations was typically enough to destabilize a fold past the point of function. Long before random change could stumble into a new, useful structure, it wrecked the one already working. Advocates of the design argument, including Douglas Axe, read this as experimental confirmation of the Wistar mathematicians’ worry: functional sequences sit on narrow, isolated peaks in a vast space of possibilities, and undirected change is far more likely to fall off the peak than climb a new one.
Carter’s Second Argument — Getting It Started vs. Keeping It Going
Geneticist Robert Carter frames DNA as a two-edged problem for evolution, not one. The first edge is origin: DNA is difficult to build from scratch in a prebiotic setting, since its phosphate backbone is chemically rare and unstable, and even a randomly assembled sequence of the right molecules wouldn’t spell out functional genes — random letters don’t organize themselves into instructions.
The second edge, Carter argues, is maintenance. DNA is chemically unstable on an ongoing basis — cytosine, one of its four bases, degrades constantly and has to be actively repaired, and that’s just one of several routine chemical failure points cells manage every day through dedicated repair systems. Even with those systems running, mutations slip through, and the overwhelming majority are neutral to harmful rather than beneficial. Carter’s genetics work points to millions of small genetic deletions scattered differently across human populations worldwide, read on his account as evidence of a single ancestral population’s genome gradually breaking apart over a few thousand years, rather than slowly accumulating new function over deep time. If that reading is right, genomes should be running down, not building up — and a young timeline predicts that pattern more comfortably than a multi-million-year one does.
Prediction vs. Accommodation
Both the Wistar-style mutation problem and Carter’s genetic-entropy argument fit the pattern this series keeps circling back to. A young-earth, created-information model predicts a functioning, front-loaded genetic code that’s degrading over a short timeline, which lines up with what mutation-accumulation studies actually measure. The standard evolutionary model has to accommodate the same data by finding enough rare beneficial mutations, over a much longer timeline, to outrun that degradation — a harder needle to thread, but not an impossible one, and mainstream researchers have proposed real candidate mechanisms for it.
The Mainstream Case — And It’s a Real One
The steelman here matters, because DNA is a language, therefore a mind wrote it is often presented as though there’s no serious answer on the other side. There is.
First, on new genetic information: evolutionary biology doesn’t rely purely on point mutations rewriting single letters. Gene duplication — where a cell accidentally copies an entire gene — creates a redundant copy free to mutate without breaking the original’s function, and several documented cases, including antifreeze glycoprotein genes in Antarctic icefish and novel digestive enzymes in bacteria that metabolize synthetic compounds, are cited as duplication-plus-mutation producing genuinely new function rather than just tuning existing function. Richard Lenski’s decades-long E. coli experiment is often invoked the same way, as a directly observed case of a population evolving a new metabolic capability under lab conditions.
Second, on the specified-complexity concept underlying Meyer’s information argument: critics, including mathematicians and computer scientists sympathetic to evolutionary theory, argue the concept isn’t rigorously quantifiable outside of judging, after the fact, that something is specified. Without an independent, agreed-upon way to calculate the probability of a given biological sequence arising by any evolutionary pathway, they argue the design inference risks being an intuition dressed up as a calculation.
Third, on the code analogy itself: DNA-as-software is a genuinely useful analogy, but critics note it remains an analogy. Every code humans have directly watched being written was written by a mind — but humans have also only ever watched a vanishingly small, recent slice of biological history. Extrapolating that codes need code-writers from that sample all the way back to the origin of the first code, roughly four billion years back, is exactly the kind of extrapolation this series has flagged before as resting on an assumption rather than a direct observation, on both sides of the debate.
It’s also worth noting plainly: most working evolutionary biologists who find real problems with strict neo-Darwinian mechanisms — and there are more of them than popular science coverage usually lets on — respond by searching for new naturalistic mechanisms, such as epigenetics, natural genetic engineering, or self-organizing chemistry, rather than entertaining design as a live candidate answer. That’s not evidence they’re wrong. It is, arguably, the clearest illustration this series has offered yet of the presuppositional wall from Part 1: methodological naturalism doesn’t just prefer natural explanations, it rules design out of consideration before the evidence is weighed.
🎥 A Different Angle: Video Explainer
This isn’t a rehash of what you just read. The video below approaches the DNA-as-language question from a different angle than the essay above — a good gut-check before moving into the next post.
Where This Leaves Us
DNA reads like a language by any reasonable definition of the word, and both sides of this debate agree on that much. Where they part ways is on what a language, once found in a cell, is allowed to imply — and on whether the mutation-and-selection mechanism has the raw creative power evolutionary theory needs it to have, or is mostly a filter running on top of information that was already there. Carter’s genetic-entropy case and the Wistar-to-Tawfik mutation problem are among the more concrete, testable versions of that question in this whole series — not just philosophy, but data both sides can examine.
Next in the series, we ask what it means for the origin of life itself if DNA can’t easily arise from raw chemistry and can’t easily hold together over deep time — and whether genomes look more like they’re wearing out than building up.
🎧 A Different Angle: Audio Overview
Not a rehash — a short, conversational audio walkthrough of this post’s core argument, useful for revisiting the material on a commute or a walk.
Go Deeper: The Full Source Video
Ken Ham’s bead-on-a-rope illustration and the DNA-as-language case referenced in this post are drawn from the video below.
Why DNA Will Blow Your Mind — Ken Ham (full video).
This is Part 6 of the Creation & Origins series. Next: Part 7 — Life Can’t Start, Life Can’t Hold — origin-of-life chemistry and genetic entropy.
📖 From the Same Author — The Divine Fractal
The Fractal Bible Series by R.M. Mayes
If the order and design running through this series speaks to you, it isn’t the only place Scripture shows its architecture. The Fractal Bible series traces a single pattern — Creation, Fracture, Restoration — repeating at every scale of the biblical text, from a single verse to the whole sweep of redemption history, grounded in thirty years of reading Scripture cover to cover. Same conviction as this series, applied to the text itself: the patterns aren’t accidents.
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