The Origin of Life
Origin of Life
A man thought creating a being wasn’t that hard and so God said “well let’s see you try”. So the man bends down to gather some dirt and God says “Oh no you don’t, make your own dirt”.
I am not going to argue evolution vs creation. That is a continuing debate that better men than me can argue. In this paper, I am only going to suggest that Evolution Theory should remain Theory and not be treated as fact. Scientific hubris treats material naturalism as proven – but in rejecting metaphysical inquiry – have they over stepped reality?
Are the known evolutionary mechanisms sufficient to generate the observed degree of functional integration and coordination, given what we know about actual biological systems?
What degree of total biological information-system divergence is required before two organisms cease to be capable of forming one coherent reproductive/developmental lineage?
The separation between species may not be well represented by a one-dimensional genetic distance. The relative similarity between genetic code is not the only factor in determining species.
It could instead be something more like a high-dimensional compatibility space.
Imagine each organism has a state:
S=(G,E,B,R,M,P,…)
where, conceptually:
G = genome
E = epigenetic state
B = bioelectric state/network
R = regulatory networks
M = morphogenetic dynamics
P = physiological state
Two organisms could be genetically quite close but have sufficiently different regulatory/bioelectric/developmental architectures that separate the species.
Conversely, considerable genetic differences might sometimes be tolerated because the
higher-level system remains functionally compatible.
We don’t have a metric like:
40% genomic divergence + 20% epigenetic divergence + 30% bioelectric divergence = species boundary.
In the future, theories of speciation may need to measure not merely genetic divergence, but divergence in the entire developmental information architecture of organisms.
Biochemist Michael Behe coined the term irreducible complexity. Essentially, he asks – If a system needs every single piece to work, a gradual step-by-step evolutionary process seems impossible. An incomplete system would have no function, meaning natural selection would not preserve or build it. However, science continues to claim – there is no reliable way to identify systems that could not have evolved by natural selection.
An irreversibly complex but not-yet-complete machine has no function, and is thus invisible to natural selection and therefore “unevolvable.” If it does not work, it confers no advantage, and there is therefore no occasion for its selection. Evolutionist counter that a simple precursor evolved for a different task, could join with other functional precursors to form the complex unit. If evolution is a truly random process then “preadaptation,” in which necessary components randomly assemble themselves until they are ready to “snap together” into a functional whole – should not exist. Evolution is blind to what it needs. It only adapts to the current conditions at hand. It cannot predict or desire to change into something more adaptable, more efficient or more survivable.
Suppose we discover a biological machine containing ten components. We establish that each component has a plausible evolutionary history. That does not yet explain the entire machine.
We then need to explain how those components became: present in the same lineage; available at the same developmental stage; expressed in the appropriate cells; expressed at appropriate levels;
correctly localized; chemically compatible; regulated in relation to one another; connected to the appropriate signaling pathways; and coordinated in time.
And now add genetic-code.
If the development of an organ depends not simply on possessing the right genes but on many cells having the right states, communicating with one another, interpreting positional information, and coordinating their behavior, then the evolutionary question becomes more sophisticated.
Essentiality is context-dependent. A gene can be essential in one organism or environment and dispensable in another. Studies comparing bacteria have found substantial differences in which genes are essential.
Even more interestingly, determining the minimal genome has repeatedly encountered genes whose functions were poorly characterized or unknown. Earlier minimal-genome work noted that a substantial fraction of candidate essential genes had unknown functions; later work on E. coli likewise identified large numbers of essential genes whose functions were incompletely characterized.
“How much of the information contained in a genome do we actually understand well enough to determine its contribution to the living system?”
Suppose we subsequently discover that what we thought was merely “unused DNA” actually contains regulatory information controlling when and where several of those components are expressed. Then the original evolutionary problem was underestimated. We didn’t merely have ten parts.
We had:
10 parts + regulatory information + timing + localization + interaction + feedback
And potentially: regulation of the regulators.
“How did a multilevel information-processing and control architecture evolve?”
Before we discuss complexity, lets remind ourselves several fundamental facts:
Cosmology – the origin of the universe – the age of the earth and the big bang theory are under question. A young universe can look like an old system if that is the way it was made. A carpenter can make a table and chair look like an antique and a painter can forge a masterpiece by manipulation of the process.
Chemical evolution – turning rocks into cells – There is no know pathway. We are not talking about a pathway in theory that cannot be proven due to testability. We are talking about a fact that there is no current accepted theoretical path. That is why there are Bottoms up, Top down, Panspermia, Thermal Vents, RNA world etc theories. NO ONE – has a working theory that can be tested or suggested as the one pathway for chemical evolution.
Why would a rock need to evolve?
Microevolution – Mutation, migration, natural selection, and genetic drift are the evolutionary forces that drive genetic changes of natural populations from one generation to the next.
Example:
The family Canidae comprises roughly three dozen living species, including wolves, coyotes, foxes, jackals, and domestic dogs. Domestic dogs are generally classified as Canis lupus familiaris, a domesticated subspecies of the gray wolf (Canis lupus). Genomic studies have identified about 23 major genetic clades among dog breeds, reflecting patterns of shared ancestry, geography, morphology, and historical function. Human selection has subsequently produced more than 350 recognized dog breeds worldwide, although the exact number varies among breed registries.
This is known among biologists as microevolution and has been tested and exists. But it is at the species level. It explains intra species variation (Family, Genus, Species). But it does not explain Orders, Class, Phylum. Kingdom or Domain.
Macroevolution – Macroevolution, tries to explain the larger evolutionary picture that is the appearance of the greater groups, such as the evolution of mammals, insects, and plants. Its framework is based on inferences from Comparative anatomy and morphology, Fossil evidence, Molecular evidence, Genomic evidence and Phylogenetic analysis.
Continuing with our Dog evolution:
“…What actual fossil/genetic evidence supports a transition from Canis lepophagus to Canis lupus?”, the important answer is: the old textbook version of that transition is no longer well supported. There is morphological evidence that C. lepophagus belongs on the stem leading toward the later radiation of Canis. But there’s a major qualification: that does not establish that C. lepophagus directly evolved into C. lupus.
The biggest problem: the geography The oldest definite Canis lupus fossils are approximately
1 million years old in the Yukon. Meanwhile, C. lepophagus is substantially older and is primarily a
North American animal. Modern phylogenetic work increasingly indicates that the lineage leading to the gray wolf and coyote originated in Eurasia, rather than simply progressing:
C. lepophagus → C. lupus
A 2022 reassessment specifically notes that earlier authors proposed C. lepophagus as an ancestor of either coyotes or wolves, but that the morphology does not support a close direct relationship to either modern species, and that the wolf/coyote lineage is now regarded as having a Eurasian origin.
For a strong lepophagus → lupus case, we’d want fossils showing a chronologically ordered series in which:
C. lepophagus occurs first; progressively younger populations acquire wolf-like skull proportions;
their teeth progressively approach C. lupus; intermediate forms occur geographically between the populations; eventually unmistakable early C. lupus appears; independent genetic/phylogenetic evidence agrees with that sequence.
We don’t currently have that clean sequence.
– ChatGPT
The horse tree has real problems too. The old illustration often shows something like:
Eohippus/Hyracotherium → Mesohippus → Merychippus → Pliohippus → Equus
as though one species simply transformed into the next.
That’s not how modern paleontology understands horse evolution. The fossil record actually contains a branching bush with many side lineages, radiations, and extinctions. The classic linear model was explicitly replaced by a much more complicated tree as additional fossils were discovered.
Even the whale tree isn’t perfect. There have been conflicts between molecular and fossil-based phylogenies of whales. Researchers have explicitly documented cases where morphological characters from fossils and molecular data from living species produced different relationships.
Inferences vs Facts:
“An inference is the act of passing from one proposition, statement, or judgment considered as true to another whose truth is believed to follow from that of the former” –
https://www.merriam-webster.com/dictionary/inference
Science almost never uses “proven” in the mathematical sense. A better vocabulary is directly observed, strongly inferred, weakly inferred, or speculative.
Comparative anatomy and morphology
Suppose you find that humans, bats, whales and dogs all have: one upper arm bone two forearm bones wrist bones five-digit ancestral pattern but those structures have been modified for walking, flying, swimming, etc.
The anatomical pattern is directly observable.
But the claim:
“These structures were inherited from a common ancestor.”
is an inference.
Fossil evidence
Fossils are actual physical evidence from the past. The interpretation is something like: Fossil X lived approximately 400 million years ago and possesses characteristics intermediate between groups A and B; therefore it provides evidence about the evolutionary history connecting A and B.
That’s an inference.
Molecular evidence
This is where the evidence can literally compare their inherited molecular sequences. Then mathematical models ask: What evolutionary history would most plausibly produce these patterns?
That’s the inference.
Genomic evidence
Genomics takes the molecular argument much further. Instead of comparing one gene, scientists can compare: thousands → millions → billions of DNA bases.
The Smithsonian, for example, describes genetic evidence as showing that humans, chimpanzees and bonobos form a closer group than any of them does with gorillas or other primates. (Family, Genus, Species) But even with enormous genomic datasets, the resulting tree is still an inference.
Phylogenetic analysis is the most “inferred” component
A phylogenetic tree isn’t something we literally observe. Nature describes modern phylogenetics explicitly as reconstructing evolutionary history mathematically from characteristics of contemporary organisms, with fossils providing a window into past history.
So the tree is an inference about history.
Modern phylogenomic research explicitly recognizes that portions of the tree of life may be difficult—or potentially impossible—to resolve with high confidence.
In summary, while scientists have made strides in understanding and manipulating the components of life, creating life from nothing—without any pre-existing biological material—remains an unsolved challenge.
The data does’t contain the conclusion, even though science makes inferences from it..
The data are observations. The evolutionary interpretation is a model explaining those observations. That distinction should be maintained even when the scientific community considers the model extremely successful.
Why do inferences from facts not equal a fact?
Genetic information
The genome contains not merely protein-coding sequences but a much larger regulatory architecture whose functions are incompletely understood. JCVI-syn3A It is a synthetic cell with minimal genetic material (Minimal Synthetic Cell).Created by the J. Craig Venter Institute (JCVI) in collaboration with other organizations, this is considered one of the biggest breakthroughs in synthetic biology, as it is an organism capable of growing and dividing on its own in the laboratory with the fewest genes ever developed
The synthesis of M. mycoides JCVI-syn3.0 (531 kbp, 473 genes) genome is smaller than that of any autonomously replicating cell found in nature. The minimal cell concept appears simple at first glance but becomes more complex upon close inspection. In addition to essential and nonessential genes, there are many quasi-essential genes, which are not absolutely critical for viability but are nevertheless required for robust growth.
Unexpectedly, it also contains 149 genes with unknown biological functions, suggesting the presence of undiscovered functions that are essential for life.
However, this bacteria-like organism behaved strangely when growing and dividing, producing cells with wildly different shapes and sizes. Now, scientists have identified seven genes that can be added to tame the cells’ unruly nature, causing them to neatly divide into uniform orbs.
But, they didn’t build that cell completely from scratch. Instead, they started with cells from a very simple type of bacteria called a mycoplasma. They destroyed the DNA in those cells and replaced it with DNA that was designed on a computer and synthesized in a lab.
“Building a functional synthetic genome that drives the propagation of a free-living cell (JCVI-syn1.0, nearly wild-type Mycoplasma mycoides subspecies capri)” – Genetic requirements for cell division in a genomically minimal cell Pelletier, James F. et al. Cell, Volume 184, Issue 9, 2430 – 2440.e16
The researchers have now added 19 genes back to this cell, including the seven needed for normal cell division, to create the new variant, JCVI-syn3A. This variant has fewer than 500 genes.
In the JCVI-syn3A version, the research team reinserted 19 genes (including 7 genes essential for controlling cell division, such as ftsZ and sepF ). As a result, these synthetic cells can grow and divide into complete and uniform spheres, just like living organisms in nature.
The remaining mystery: Despite being the simplest living organism, scientists still don’t know the precise function of nearly one-third of the genes within this cell, reflecting the vast gaps we still have in understanding the most fundamental mechanisms of life
They took a car, removed the driver and replaced the driver with a robot.
The inference that a man made synthetic organism can demonstrate that a pathway is possible, ignores the absence of a complete understanding of what is required in nature. Taking a set of unknown complex existing materials is far different from arriving at those materials by a natural process.
Its like saying we built an airplane from studying a car – but without knowing anything about metallurgy. How can you build either if you have no steel to work with?
Recently, Denis Noble challenges what he calls the “gene-centric” or “gene-as-master-controller” conception of biology. His systems-biology perspective emphasizes that causation operates
upward and downward through biological levels. DNA influences the behavior of cells, but epigenetic cellular states also influence gene expression. Cells interact with tissues; tissues interact with organs; physiological states influence cellular and molecular processes.
So rather than imagining:
DNA → RNA → protein → organism as a one-way command hierarchy, you get something like
DNA ↔ proteins ↔ cells ↔ tissues ↔ organs ↔ organism ↔ environment
with feedback operating throughout the system.
The inference that we live in a DNA driven world is more complex than first thought. It seems that its not just DNA that controls our development from embryo to adulthood. Epigenetic and environmental factors also direct the DNA protein synthesis.
Michael Levin is even more interesting and relevant because his work investigates bioelectric signaling and cellular collectives. Cells don’t merely behave according to their individual genetic instructions. Cells also communicate through electrical states, and these bioelectric networks can influence what groups of cells collectively do during development. Research on planarian flatworms demonstrates that biological shape is controlled by a rewritable bioelectric code rather than DNA alone. By manipulating the electrical communication between cells, his lab at Tufts University successfully forced fragments of sliced worms to regenerate into functional two-headed or two-tailed organisms without altering their genomes – not a change due to mutations.
Cells use ion channel proteins to create a head-to-tail electrical gradient across the body. This electrical profile acts as a real-time “map” or blueprint that tells cells what anatomical structures to build and when to stop growing. By using pharmacological blocks to interrupt gap junctions (the channels cells use to communicate electrically) – shifting this circuit, a tail fragment was tricked into thinking it was a head, causing a second fully-functional head to sprout. Crucially, if you take one of these two-headed worms and cut them again in plain water—with no further drug treatments—they will continue to regenerate as two-headed worms. Even when they reproduce naturally by ripping themselves in half (fission), the resulting offspring remain two-headed. The “shape memory” is permanently rewritten in the bioelectric layer. In parallel experiments, Levin showed that if you train a planarian worm to find food on a specific substrate and then decapitate it, the regenerated worm (which grows a completely new brain) still retains the memories it learned before the head was cut off.
As data becomes more sophisticated – inferences from less sophisticated data are subject to revision. The interpretation of data is always subject to testing as new tools and new data is discovered.
It is also important to remember that science make mistakes
In 1846, science “discovered” a planet which was subsequently named “Vulcan”. Its existence was predicted by the astronomer Le Verrier. Le Verrier’s most famous achievement is his prediction of the existence of the then unknown planet Neptune, using only mathematics and astronomical observations of the known planet Uranus.
In 1859, Le Verrier was the first to report that the slow precession of Mercury’s orbit around the Sun could not be completely explained by Newtonian mechanics and perturbations by the known planets. He suggested, among possible explanations, that another planet (or perhaps, instead, a series of smaller ‘corpuscules’) might exist in an orbit even closer to the Sun than that of Mercury, to account for this perturbation
The success of the search for Neptune based on its perturbations of the orbit of Uranus led astronomers to place some faith in this possible explanation, and the hypothetical planet was even named Vulcan.
The need for the planet as an explanation for Mercury’s orbital peculiarities was later rendered unnecessary when Einstein’s 1915 theory of general relativity showed that Mercury’s departure from an orbit predicted by Newtonian physics was explained by effects arising from the curvature of spacetime caused by the Sun’s mass.
But, From 1846 until 1915 (69 yrs) science accepted the existence of the planet “Vulcan.”
“Clovis first”
The most contentious issue in American archaeology is the so-called Clovis orthodoxy or Clovis first theory. The Clovis culture is a prehistoric Paleoamerican archaeological culture, named for distinct stone and bone tools found in close association with Pleistocene fauna. It existed from roughly 11,500 to 10,800 BCE (~13,500-12,800 years Before Present) near the end of the Last Glacial Period. The theory, known as “Clovis First”, had been the predominant hypothesis among archaeologists in the second half of the 20th century. According to Clovis First, the people associated with the Clovis culture were the first inhabitants of the Americas.
The history of this orthodoxy goes back perhaps to the end of the nineteenth century, before which time it was a heresy. In the 1890s William Henry Holmes of the Smithsonian Institution’s Bureau of American Ethnology and Thomas Chamberlin of the United States Geological Survey chased off many dubious claims for Pleistocene (ice-age) occupation of the New World. The mantle of authority for this gatekeeper role was passed in the 1920s to the physical anthropologist Ales Hrdlicka, also of the Smithsonian. Long after, in 1995, Hrdlicka was singled out by American author Vine Deloria in his book “Red Earth, White Lies” as a heavy-handed zealous defender of the academic status quo who quashed research proposals designed to explore alternative theories.
The Clovis-first theory had now matured into a complete and established orthodoxy. The architects of the theory now became its high priests, and the theory was ripe for the next phase in the cycle of attack and defence of the new status quo.
The history of these claims and refutations is revealing of academe. The weapons of argument used by the defenders certainly appeal to scientific method, careful observation, avoidance of known sources of error, balanced logic, and reason; but the tactics of appeal are clearly biased, selective, petty, personal, and confrontational.
The inferences from the Clovis site have now been revised – as scientific process should be. Archaeologists found well-documented, secure sites south of the ice that date long before Clovis existed. Examples include Monte Verde in Chile (~14,500 to 18,500 years old) and the Buttermilk Creek Complex in Texas (~15,500 years old). Fossilized human footprints discovered at White Sands, New Mexico date back roughly 21,000 to 23,000 years ago during the peak of the last Ice Age. Environmental DNA (eDNA) studies of the Ice-Free Corridor show that the path was a desolate, sterile landscape devoid of plants or animals until roughly 12,600 years ago—meaning humans were already deep in the Americas long before the northern corridor could support life.
The New Theory: Pacific Coastal Migration (The Kelp Highway) has given new inferences. Instead of walking inland through a frozen sheet of ice, the new consensus is that early coastal pioneers traveled by boat or on foot along the Pacific coastline. This theory suggests travelers followed rich marine ecosystems (seaweed, fish, shellfish, and sea mammals) down the coast of Asia, the Aleutian Islands, and the Pacific Northwest. This maritime or coastal-hugging migration could have happened flexibly over 16,000 to 20,000+ years ago, allowing humans to bypass the massive ice sheets entirely and reach South America rapidly.
This is not to say that initial inferences did not have a basis. But it does point to the need to keep inferred interpretations in the realm of theory and not fact. These are just two examples of science making mistakes. Due to persistent scientific investigation, the mistakes were revealed.
Confirmation bias, people’s tendency to process information by looking for, or interpreting, information that is consistent with their existing beliefs. This biased approach to decision making is largely unintentional, and it results in a person ignoring information that is inconsistent with their beliefs. CB is a dangerous part of science. It ignores the fundamental scientific process of unbiased interpretation of data, in favor of making the data fit an expected paradigm. With the search for Vulcan, many false observations of the planet were reported due to the desire to prove its existence and in the Clovis First case, perhaps tradition and reputation were at stake.
I remember a statement (I dont know where or who said it) that “evolution does not occur because of status quo, but because of error”.
In science, if something does not fit the pattern, then it should be explored, not eliminated. Metaphysical explanations – while untestable, should not be eliminated as superstition or myth. They may in fact have validity.