The waiting time problem in a model hominin population

The waiting time problem in a model hominin population

https://reasonandscience.catsboard.com/t2585-the-waiting-time-problem-in-a-model-hominin-population

Behe The edge of evolution 2007 4

The book "edge of evolution" is principally about the probability of new protein-protein binding sites arising by chance and necessity. Experimental evidence (mostly chloroquine resistance) shows such protein-protein binding sites to be difficult to evolve by chance mechanisms. He says the empirical (extrapolation) of the "edge" of evolution is no more than two coordinated protein-protein binding sites could have evolved in a lineage in all the time available on earth. The flagellum has perhaps dozens of such sites.
It is a quantitative argument.

Recall the example of sickle cell disease. The sickle cell mutation is both a life saver and a life destroyer. It fends off malaria, but can lead to sickle cell disease. However,hemoglobin C-Harlem has all the benefits of sickle, but none of its fatal drawbacks. So in western and central Africa, a population of humans that had normal hemoglobin would be worst off, a population that had half normal and half sickle would be better off, and a population that had half normal and half C-Harlem would be best of all. But if that’s the case, why bother with sickle hemoglobin? Why shouldn’t evolution just go from the worst to the best case directly? Why not just produce the C-Harlem mutation straightaway and avoid all the misery of sickle? The problem with going straight from normal hemoglobin to hemoglobin C-Harlem is that, rather than walking smoothly up the stairs, evolution would have to jump a step. C-Harlem differs from normal hemoglobin by two amino acids. In order to go straight from regular hemoglobin to C-Harlem, the right mutations would have to show up simultaneously in positions 6 and 73 of the beta chain of hemoglobin. Why is that so hard? Switching those two amino acids at the same time would be very difficult for the same reason that developing resistance to a cocktail of drugs is difficult for malaria—the odds against getting two needed steps at once are the multiple of the odds for each step happening on its own. What are those odds? Very low. The human genome is composed of over three billion nucleotides. Yet only a hundred million nucleotides seem to be critical, coding for proteins or necessary control features. The mutation rate in humans (and many other species) is around this same number; that is, approximately one in a hundred million nucleotides is changed in a baby compared to its parents (in other words, a total of about thirty changes per generation in the baby’s three-billion-nucleotide genome, one of which might be in coding or control regions). In order to get the sickle mutation, we can’t change just any nucleotide in human DNA; the change has to occur at exactly the right spot. So the probability that one of those mutations will be in the right place is one out of a hundred million. Put another way, only one out of every hundred million babies is born with a new mutation that gives it sickle hemoglobin. Over a hundred generations in a population of a million people, we would expect the mutation to occur once by chance. That’s within the range of what can be done by mutation/selection.

To get hemoglobin C-Harlem, in addition to the sickle mutation we have to get the other mutation in the beta chain, the one at position 73. The odds of getting the second mutation in exactly the right spot are again about one in a hundred million. So the odds of getting both mutations right, to give hemoglobin C Harlem in one generation in an individual whose parents have normal hemoglobin, are about a hundred million times a hundred million (10^16). On average, then, nature needs about that many babies in order to find just one that has the right double mutation. With a generation time of ten years and an average population size of a million people, on average it should take about a hundred billion years for that particular mutation to arise—more than the age of the universe. 

Hemoglobin C-Harlem would be advantageous if it were widespread in Africa, but it isn’t. It was discovered in a single family in the United States, where it doesn’t offer any protection against malaria for the simple reason that malaria has been eradicated in North America. Natural selection, therefore, may not select the mutation, and it may easily disappear by happenstance if the members of the family don’t have children, or if the family’s children don’t inherit a copy of the C-Harlem gene. It’s well known to evolutionary biologists that the majority even of helpful mutations are lost by chance before they get an opportunity to spread in the population. If that happens with C-Harlem, we may have to wait for another hundred million carriers of the sickle gene to be born before another new C-Harlem mutation arises.



Gunter Bechly Fossil Discontinuities: Refutation of Darwinism & Confirmation of Intelligent Design  Oct 11, 2018

Michael Behe discovered the waiting time problem as a problem for darwinism in his book the age of evolution and he didn't make a mathematical calculation but he looked at the empirical data from malaria drug resistance and what he found is that a lot of the malaria drugs resistance developed very quickly in a few years because only point mutations were necessary but in the case of chloroquine the drug chloroquine it took several decades and the reason was it was discovered later that there you needed a coordinated mutations to mutations neutral for each other had to come together to produce this kind of resistance against chloroquine and then he  simply transpose the data if you look at the vast population size of malaria microbes compared to the population size of vertebrates and their short generation time and you transpose these data he came up to the hypothesis that invertebrates were a single coordinated change he would have to need longer than the existence of the whole universe 10 to the power of 15 years now this is of course would be a problem and for example in human evolution we have all these nice fossils so if the signal coordinated change would take longer than the universe then then it would be game over so of course evolutionary biologists tried to repute me 

and indeed in 2008 the earth and Schmidt they published a paper in genetics where they said they have refuted his result was completely unrealistic they did they made a mathematical calculation based on the methodological apparatus of population genetics and simulations and they came with a number of 260 million years. Wonderful this is really much shorter than Big E the problem is we have only 6 million years available since the splitting of the human lineage from the chimp lineage so that is what evolutionary biologists say is the time needed for a single coordinated mutation and you have to keep in mind this is a mathematical model which always involves simplifications and simplifications may involve errors so what is more likely that the empirical data from B from a lot of drug resistance are closer to the truth or the mathematical simulation I would suggest that rather this ten to the power of 15 is closer to the the real constraint in nature but anyway we arrive at times that are much too long for for evolution to occur the available windows of time a second study by San thought can basically do the same results and the problems 

also if you compare for example children a with human DNA it's always there are they're so similar 95 percent similarity thus these five percent differences means millions of differences in in base pairs and these differences have to arise by mutations and have to spread in the population so you have to accommodate even these 5% difference in this available window of time of six million years and it doesn't add up in population genetics.

Rick Durrett Waiting for Two Mutations: With Applications to Regulatory Sequence Evolution and the Limits of Darwinian Evolution 2008 Nov  3

We now show that two coordinated changes that turn off one regulatory sequence and turn on another without either mutant becoming fixed are unlikely to occur in the human population. Theorem 1 predicts a mean waiting time of216 million years.


John Sanford The waiting time problem in a model hominin population 17 September 2015 1

Functional information is normally communicated using specific, context-dependent strings of symbolic characters. This is true within the human realm (texts and computer programs), and also within the biological realm (nucleic acids and proteins). In biology, strings of nucleotides encode much of the information within living cells. How do such information-bearing nucleotide strings arise and become established?

Methods
This paper uses comprehensive numerical simulation to understand what types of nucleotide strings can realistically be established via the mutation/selection process, given a reasonable timeframe. The program Mendel’s Accountant realistically simulates the mutation/selection process, and was modified so that a starting string of nucleotides could be specified, and a corresponding target string of nucleotides could be specified. We simulated a classic pre-human hominin population of at least 10,000 individuals, with a generation time of 20 years, and with very strong selection (50 % selective elimination). Random point mutations were generated within the starting string. Whenever an instance of the target string arose, all individuals carrying the target string were assigned a specified reproductive advantage. When natural selection had successfully amplified an instance of the target string to the point of fixation, the experiment was halted, and the waiting time statistics were tabulated. Using this methodology we tested the effect of mutation rate, string length, fitness benefit, and population size on waiting time to fixation.

Results
Biologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. To establish a string of two nucleotides required on average 84 million years. To establish a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive.

Conclusion
We show that the waiting time problem is a significant constraint on the macroevolution of the classic hominin population. Routine establishment of specific beneficial strings of two or more nucleotides becomes very problematic.










More readings:
https://evolutionnews.org/2016/08/the_origin_of_m/

1. https://tbiomed.biomedcentral.com/articles/10.1186/s12976-015-0016-z
2. https://www.youtube.com/watch?v=M7w5QGqcnNs
3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2581952/
4. https://3lib.net/book/2514862/707789

Uchicagomedicine Most human-chimp differences due to gene regulation--not genes March 9, 2006 5

The vast differences between humans and chimpanzees are due more to changes in gene regulation than differences in individual genes themselves, researchers from Yale, the University of Chicago, and the Hall Institute in Parkville, Victoria, Australia, argue in the March 9, 2006, issue of the journal Nature.

The scientists provide powerful new evidence for a 30-year-old theory, proposed in a classic paper from Mary-Claire King and Allan Wilson of Berkeley. That 1975 paper documented the 99-percent similarity of genes from humans and chimps and suggested that altered gene regulation, rather than changes in coding, might explain how so few genetic changes could produce the wide anatomic and behavioral differences between the two.

Using novel gene-array technology to measure the extent of gene expression in thousands of genes simultaneously, this study shows that as humans diverged from their ape ancestors in the last five million years, genes for transcription factors--which control the expression of other genes--were four times as likely to have changed their own expression patterns as the genes they regulate.

Because they influence the activity of many "downstream" genetic targets, small changes in the expression of these regulatory genes can have an enormous impact.

"When we looked at gene expression, we found fairly small changes in 65 million years of the macaque, orangutan, and chimpanzee evolution," said study author Yoav Gilad, PhD, assistant professor of human genetics at the University of Chicago, "followed by rapid change, along the five million years of the human lineage, that was concentrated on these specific groups of genes. This rapid evolution in transcription factors occurred only in humans."

"For 30 years scientists have suspected that gene regulation has played a central role in human evolution," said Kevin White, PhD, associate professor of genetics and ecology and evolution at Yale and senior author of the study. "In addition to lending support to the idea that changes in gene regulation are a key part of our evolutionary history, these new results help to define exactly which regulatory factors may be important, at least in certain tissues. This helps open the door to a functional dissection of the role of gene regulation during the evolution of modern humans."

To measure changes in gene expression from different species, White and Gilad developed the first multi-species gene array. This allowed them to compare the level of expression of more than 1,000 genes between humans, chimps, orangutans and rhesus macaques--representing about 70 million years of evolution. To make the samples comparable, the researchers studied tissue from the liver--one of the most homogeneous sources--from five adult males from each of the four species.

They focused their search on expression levels of two sets of genes, those that remained largely unchanged across all four species, suggesting that there was little room--or need--for improvement, and those that changed most dramatically, usually in the human lineage--an indication of powerful incentives to adapt to a changing environment.

Of the 1,056 genes from all four species, 60 percent had fairly consistent expression levels across all four species. "The expression levels of these genes seem to have remained constant for about 70 million years," the authors wrote, "suggesting that their regulation is under evolutionary constraint."

Many of these genes are involved in basic cellular processes. The authors suggest that altering the regulation of these fundamental and ancient genes may be harmful. In fact, five of the 100 most stable genes have altered expression levels in liver cancer.

When they also looked for human genes with significantly higher or lower expression levels, they found 14 genes with increased expression and five with decreased expression. While only 10 percent of the genes in the total array were transcription factors, 42 percent of those with increased expression in humans were. None of those with lower expression were transcription factors. This pattern, the authors note, is consistent with "directional selection."

Previous studies have found that many of these same genes have also evolved rapidly in humans, accumulating changes in their coding sequence as well as in expression rates. "Together," they add, "these findings raise the possibility that the function and regulation of transcription factors have been substantially modified in the human lineage."

This is a very efficient way to make big changes with very little effort, according to Gilad. By altering transcription factors, the entire regulatory network can change with very few mutations, increasing the impact and minimizing the risk.

"The big question," he said, "is why are humans so different? What sort of changes in the environment or lifestyle would drive such a rapid shift in the expression of genes--in this case in the liver--in humans and in no other primate?"

Part of the answer, he suspects, is rapid alterations in diet, probably related to the acquisition of fire and the emerging preference for cooked food. "No other animal relies on cooked food," he said. "Perhaps something in the cooking process altered the biochemical requirements for maximal access to nutrients as well as the need to process the natural toxins found in plant and animal foods."

This is just the first of a series of similar studies, said Gilad, that will look at changes in gene expression over evolutionary time. The next steps are to look at larger arrays of genes and to focus on other tissue types.

Additional authors include Alicia Oshlack, Gordon Smyth and Terence Speed from the Hall Institute in Parkville, Victoria, Australia. This study was supported grants from the Keck Foundation, the Beckman Foundation and the National Human Genome Research Institute to Professor White.


Gennadi V. Glinsky Impacts of genomic networks governed by human-specific regulatory sequences and genetic loci harboring fixed human-specific neuro-regulatory single nucleotide mutations on phenotypic traits of Modern Humans April 18, 2020

Recent advances in identification and characterization of human-specific regulatory DNA sequences set the stage for the assessment of their global impact on physiology and pathology of Modern Humans. Gene set enrichment analyses (GSEA) of 8,405 genes linked with 35,074 human-specific neuro-regulatory single-nucleotide changes (hsSNCs) revealed a staggering breadth of significant associations with morphological structures, physiological processes, and pathological conditions of Modern Humans. Significantly enriched traits include more than 1,000 anatomically-distinct regions of the adult human brain, many different types of cells and tissues, more than 200 common human disorders and more than 1,000 records of rare diseases. Thousands of genes connected with neuro-regulatory hsSNCs have been identified, which represent essential genetic elements of the autosomal inheritance and offspring survival phenotypes. A total of 1,494 hsSNC- linked genes are associated with either autosomal dominant or recessive inheritance and 2,273 hsSNC-linked genes have been associated with premature death, embryonic lethality, as well as pre-, peri-, neo-, and post-natal lethality phenotypes of both complete and incomplete penetrance. Differential GSEA implemented on hsSNC-linked loci and associated genes identify 7,990 genes linked to evolutionary distinct classes of human-specific regulatory sequences (HSRS), expression of a majority of which (5,389 genes; 67%) is regulated by stem cell-associated retroviral sequences (SCARS). Interrogations of the MGI database revealed readily available mouse models tailored for precise experimental definitions of functional effects of hsSNCs and SCARS on genes causally affecting thousands of mammalian phenotypes and implicated in hundreds of common and rare human disorders. These observations suggest that a preponderance of human-specific traits evolved under a combinatorial regulatory control of HSRS and neuro-regulatory loci harboring hsSNCs that are fixed in humans, distinct from other primates, and located in differentially-accessible chromatin regions during brain development.

DNA sequences of coding genes defining the structure of macromolecules comprising the essential building blocks of life at the cellular and organismal levels remain highly conserved during the evolution of humans and other Great Apes . In striking contrast, a compendium of nearly hundred thousand candidate human-specific regulatory sequences (HSRS) has been assembled in recent years, thus providing further genetic and molecular evidence supporting the idea that unique to human phenotypes may result from human-specific changes to genomic regulatory sequences defined as “regulatory mutations”.

My comment: The authors, based on a naturalistic scientific framework, immediately hypothesize that the difference might be due to mutations of the gene regulatory network. But as Davidson stated:

No subcircuit functions are redundant with another, and that is why there is always an observable consequence if a dGRN subcircuit is interrupted. Since these consequences are always catastrophically bad, flexibility is minimal, and since the subcircuits are all interconnected, the whole network partakes of the quality that there is only one way for things to work. And indeed the embryos of each species develop in only one way.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3135751/

Structurally, functionally, and evolutionary distinct classes of HSRS appear to cooperate in shaping developmentally and physiologically diverse human-specific genomic regulatory networks (GRNs) impacting preimplantation embryogenesis, pluripotency, and development and functions of human brain. The best evidence of the exquisite degree of accuracy of the contemporary molecular definition of human-specific regulatory sequences is exemplified by the identification of 35,074 single nucleotide changes (SNCs) that are fixed in humans, distinct from other primates, and located within differentially-accessible (DA) chromatin regions during the human brain development in cerebral organoids. Therefore, this type of mutations could be defined as fixed neuro-regulatory human-specific single nucleotide changes (hsSNCs). However, only a small fraction of identified DA chromatin peaks (600 of 17,935 DA peaks; 3.3%) manifest associations with differential expression in human versus chimpanzee cerebral organoids model of brain development, consistent with the hypothesis that regulatory effects on gene expression of these DA chromatin regions are not restricted to the early stages of brain development. 

My comment: John Sanford The waiting time problem in a model hominin population 17 September 2015
Biologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. To establish a string of two nucleotides required on average 84 million years. To establish a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive.
 https://tbiomed.biomedcentral.com/articles/10.1186/s12976-015-0016-z

Annotation of SNCs derived and fixed in modern humans that overlap DA chromatin regions during brain development revealed that essentially all candidate regulatory human-specific SNCs are shared with the archaic humans (35,010 SNCs; 99.8%) and only 64 SNCs are unique to modern humans (Kanton et al., 2019). This remarkable conservation on the human lineage of human-specific SNCs associated with human brain development sows the seed of interest for in-depth exploration of coding genes expression of which may be affected by genetic regulatory loci harboring human-specific SNCs.

In this contribution, the GREAT algorithm (McLean et al., 2010, 2011) was utilized to identify 8,405 hsSNCs-linked genes expression of which might be affected by 35,074 human-specific SNCs located in DA chromatin regions during brain development. Comprehensive gene set enrichment analyses (GSEA) of these genes revealed the staggering breadth of associations with physiological processes and pathological conditions of H. sapiens, including more than 1,000 anatomically-distinct regions of the adult human brain, many human tissues and cell types, more than 200 common human disorders and more than 1,000 rare diseases. It has been concluded that hsSNCs-linked genes appear contributing to development and functions of the adult human brain and other components of the central nervous system; they were defined as genetic markers of many tissues across human body and were implicated in the extensive range of human physiological and pathological conditions, thus supporting the hypothesis that phenotype-altering effects of neuro-regulatory hsSNCs are not restricted to the early-stages of human brain development. Differential GSEA implemented on hsSNC-linked loci and associated genes identify 7,990 genes linked to evolutionary distinct classes of human-specific regulatory sequences (HSRS). Notably, expression of a majority of this common set of genes (5,389 genes; 67%) is regulated by stem cell-associated retroviral sequences (SCARS). Collectively, observations reported in this contribution indicate that structurally, functionally and evolutionary diverse classes of HSRS, neuro-regulatory hsSNCs, and associated elite set of 7,990 genes affect wide spectra of traits defining both physiology and pathology of Modern Humans by asserting human-specific regulatory impacts on thousands essential mammalian phenotypes.


Gennadi V. Glinsky A Catalogue of 59,732 Human-Specific Regulatory Sequences Reveals Unique-to-Human Regulatory Patterns Associated with Virus-Interacting Proteins, Pluripotency, and Brain Development 8 Jan 2020

Analysis of 4433 genes encoding virus-interacting proteins (VIPs) revealed that 95.9% of human VIPs are components of human-specific regulatory networks that appear to operate in distinct types of human cells from preimplantation embryos to adult dorsolateral prefrontal cortex. These analyses demonstrate that modern humans captured unique genome-wide combinations of regulatory sequences, divergent subsets of which are highly conserved in distinct species of six NHP separated by 30 million years of evolution. Concurrently, this unique-to-human mosaic of genomic regulatory patterns inherited from ECAs was supplemented with 12,486 created de novo HSRS. Genes encoding VIPs appear to represent a principal genomic target of human-specific regulatory networks, which contribute to fitness of Homo sapiens and affect a functionally diverse spectrum of biological and cellular processes controlled by VIP-containing liquid-liquid phase-separated condensates.3


Shiho Endo Search for Human-Specific Proteins Based on Availability Scores of Short Constituent Sequences: Identification of a WRWSH Protein in Human Testis November 21st 2019

Little is known about protein sequences unique in humans. Here, we performed alignment-free sequence comparisons based on the availability (frequency bias) of short constituent amino acid (aa) sequences (SCSs) in proteins to search for human-specific proteins. Focusing on 5-aa SCSs (pentats), exhaustive comparisons of availability scores among the human proteome and other nine mammalian proteomes in the nonredundant (nr) database identified a candidate protein containing WRWSH, here called FAM75, as human-specific. Examination of various human genome sequences revealed that FAM75 had genomic DNA sequences for either WRWSH or WRWSR due to a single nucleotide polymorphism (SNP). FAM75 and its related protein FAM205A were found to be produced through alternative splicing. The FAM75 transcript was found only in humans, but the FAM205A transcript was also present in other mammals. In humans, both FAM75 and FAM205A were expressed specifically in testis at the mRNA level, and they were immunohistochemically located in cells in seminiferous ducts and in acrosomes in spermatids at the protein level, suggesting their possible function in sperm development and fertilization. This study highlights a practical application of SCS-based methods for protein searches and suggests possible contributions of SNP variants and alternative splicing of FAM75 to human evolution.

The human species has unique traits among animals. It is well known that morphological and physiological traits such as erect bipedalism, speech and language, and long reproductive period are very different from those of other primate species. Only humans have high intelligence that fosters sophisticated communications and complex societies. This intelligence is related to continuous brain development after birth in humans, which is not observed in  great apes, including chimpanzees. The simplest hypothesis to explain human uniqueness is that it originates from the uniqueness of constituent molecules (i.e., genes and proteins) themselves. In this “constituent hypothesis,” humans have unique genes and proteins that do not exist in chimpanzees. A contrasting hypothesis is that constituent molecules are similar between humans and chimpanzees, but they are regulated differently in these species. That is, in this “regulatory hypothesis,” a similar set of proteins may be produced but at different times (heterochrony), in different locations (heterotopy), in different amounts (heterometry), and in different usage (heterotypy). 

One line of support for the regulatory hypothesis comes from genomics and developmental expression studies. Following the announcement of a human genome release, the genomes of great apes were sequenced. Comparisons of DNA sequences between humans and chimpanzees have revealed that nucleotide differences are only 1.23% in aligned sequences, and most of these differences are thought to be functionally insignificant. Further rigorous comparisons throughout these genomes have revealed that nucleotide differences are 4% and that they are mostly located in noncoding regions. The expression patterns of some genes are different between humans and chimpanzees during development. Differences in transcriptomes have revealed that species differences in expression patterns are tissue-dependent and that testes have the greatest difference. It has been speculated that the accumulation of small expression or regulatory differences leads to large phenotypic differences between humans and chimpanzees. RNA-mediated mechanisms for novel genes have been proposed together with the “out of the testis” hypothesis, in which testis is considered a tissue for experimenting with new genes. Comparisons among transcriptomes in primates have revealed that many genes for spermatogenesis in testes, which likely inhibit apoptosis when mutated, are positively selected.

Although sequence alignment methods are powerful and probably the most important in comparison studies, sequences that do not contain relatively long regions of similarity cannot be compared well. In other words, short sequences that do not extend to longer similarities are discarded as noise. Although this strategy is highly successful, it assumes that nonaligned short sequences are not important, which may not always be true. There may still be important differences undiscovered where alignments are not possible.

Our SCS-based approach identified FAM75, a WRWSH-containing protein, as a candidate human-specific protein. Its uniqueness in humans may be acquired not only by a point mutation for WRWSH but also by novel alternative splicing. Together with FAM205A, FAM75 is likely expressed in human testis, and its possible expression in acrosomes suggests its potential function in fertilization and thus in human speciation.


Mainá Bitar Genes with human-specific features are primarily involved with brain, immune and metabolic evolution 22 November 2019 2

Here we critically update high confidence human-specific genomic variants that mostly associate with protein-coding regions and find 856 related genes.Functional analysis of these human-specific genes identifies adaptations to brain, immune and metabolic systems to be highly involved. We further show that many of these genes may be functionally associated with neural activity and generating the expanded human cortex in dynamic spatial and temporal contexts.

Functional differences between humans and primates are evident in major morphological features such as the skeleton (e.g. jaws and hands), hair (humans have thinner hair) and muscle tissue, and global functions including speech and language, changes in the brain have presumably had the most significant impact on the human lineage. The size of the human brain is triple. Comparative neuroanatomy has revealed a specific expansion of both the neocortex, with increase in size and neuronal interconnectivity during hominid evolution and the right side of the human brain compared to chimpanzee. While this expansion is believed to be important to the emergence of human language and other high-order cognitive functions, its genetic basis remains largely unknown.

Number of cells in the human body, and synapses in the human brain

https://reasonandscience.catsboard.com/t2597-calculations-number-of-cells-in-the-human-body-and-synapses-in-the-human-brain

Claim: Initial sequence of the chimpanzee genome and comparison with the human genome
01 September 2005
More than a century ago Darwin1 and Huxley2 posited that humans share recent common ancestors with the African great apes. Modern molecular studies have spectacularly confirmed this prediction and have refined the relationships, showing that the common chimpanzee (Pan troglodytes) and bonobo (Pan paniscus or pygmy chimpanzee) are our closest living evolutionary relatives. 11

Brain Evolution 
Ralph L. Holloway, Department of Anthropology, Columbia University, New York, NY
The size of the hominid brain increased from about 450ml at 3.5 million years ago to our current average volume of 1350ml. These changes through time were sometimes gradual but not always.

Differences and similarities between human and chimpanzee neural progenitors during cerebral cortex development  Sep 26, 2016 12
The expansion of the neocortex during primate evolution is thought to contribute to the higher cognitive capacity of humans compared to our closest living relatives, the great apes, and notably the chimpanzees 

The Human Brain in Numbers: A Linearly Scaled-up Primate Brain
An informal survey with senior neuroscientists that we ran in 2007 showed that most believed that the number of cells in the human brain was indeed already known: that we have about 100 billion neurons
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/

Cellular scaling rules for primate brains
Here we examine the cellular scaling rules for primate brains and show that brain size increases approximately isometrically as a function of cell numbers, such that an 11× larger brain is built with 10× more neurons and ≈12× more nonneuronal cells of relatively constant average size.
https://www.pnas.org/content/104/9/3562

My comment: Now let's make a little calculation. The human brain has 100 billion neurons. According to the above claim, the hominid brain of our ur-ancestor, 3,5mio years ago, had a brain, a third of the size of homo sapiens today, that is 33 billion neurons approximately. ( chimpanzees have 28 billion ) That means there was an increase of 67 billion brain neurons in 3,5Mio years 

Bonobos and chimpanzees reach sexual maturity between 10 and 13 years of age. So let's suppose the average age to start breeding was 10 years. That means that there would have been 350 thousand generations in 3,5mio years.
That means, there would have had to be an increase of 190450 neurons in each generation, 

In computing terms, the brain’s nerve cells, called neurons, are the processors, while synapses, the junctions where neurons meet and transmit information to each other, are analogous to memory. These synapses are not " just so" interconnected. The connections process and store information and must be the correct one..... like a computer network.

One neuron can have 100,000 connections. 
https://jonlieffmd.com/blog/how-many-different-kinds-of-neurons-are-there?utm_content=bufferaffcf

In each generation, there would have had to be an increase of 19 billion new synapse connections

So how could natural selection, genetic drift, or gene flow have produced the correct 19 billion new synapse connections per generation? The task would be to specify EACH new cell precisely through a master program which, coordinates, instructs, and defines each neuron. Now, there are different kinds of Neurons.  Some generate action potentials. Some perfectly good neurons have no processes, some vertebrate neurons do not generate action potentials. There are sensory neurons, motor neurons, interneurons,

Cell in regard of its:

1. Cell phenotype
2. Cell size
3. It's specific function,
4. Position and place in the body. This is crucial. Limbs like legs, fins, eyes etc. must all be placed at the right place.  
5. How it is interconnected with other cells,
6. What communication it requires to communicate with other cells, and the setup of the communication channels
7. What specific sensory and stimuli functions are required and does it have to acquire in regard to its environment and surroundings?
8. What specific new regulatory functions it acquires
9. When will the development program of the organism express the genes to grow the new cells during development?
11. Precisely how many new cell types must be produced for each tissue and organ?
10. Specification of the cell-cell adhesion and which ones will be used in each cell to adhere to the neighbor cells ( there are 4 classes )
11. Programming of  time period the cell keeps alive in the body, and when is it time to self-destruct and be replaced by newly produced cells of the same kind
12. Set up its specific nutrition demands

Just a comparison of the processing power of the human brain, compared to the fastest supercomputers made by man:
The brain is a deviously complex biological computing device that even the fastest supercomputers in the world fail to emulate. Well, that’s not entirely true anymore. Researchers at the Okinawa Institute of Technology Graduate University in Japan and Forschungszentrum Jülich in Germany have managed to simulate a single second of human brain activity in a very, very powerful computer. It took 40 minutes with the combined muscle of 82,944 processors in K computer to get just 1 second of biological brain processing time. 9

The prevalence of low-level function in four such experiments indicates that roughly one in 10^64 signature-consistent sequences forms a working domain. Combined with the estimated prevalence of plausible hydropathic patterns (for any fold) and of relevant folds for particular functions, this implies the overall prevalence of sequences performing a specific function by any domain-sized fold may be as low as 1 in 10^77, adding to the body of evidence that functional folds require highly extraordinary sequences. 10

Does it seem plausible that evolutionary mechanisms had this sort of power to evolve the human brain ?


There are 37.2 Trillion Cells in Your Body. That is 37,200,000,000,000 Cells
Each contains 2,3 Billion ( 2,300000000)  Proteins
That sums up to 85560000000000000000000 Proteins. That is 8,556^21 Proteins.
That is 8,5 Vigintillion Proteins.




Paul Nelson, Evolution, or design ? 

I transcribed it for who is too lazy to see the video, but its worth to watch, a great speech:

https://reasonandscience.catsboard.com/t2310-paul-nelson-evolution-or-design

And the founders of textbook evolutionary theory said, lest assume, that micro, or small scale evolution, and macro, or large scale evolution, are the same. Just add time to micro, and you get macro. Dobzhansky, who is one of the founders of textbook theory, in the mid-thirties, said : We can't observe macro evolution , it took place over a long time, so what we have to do, is take micro evolution and extrapolate that over a long time, and he said, we do this reluctantly, we put a reluctant sign of quality between micro and macroevolution, and we push our investigations as far as we can, on basis of that assumption, what he called a working hypothesis.

micro equals macro, just given enough time, well, that's not true, and its well known within evolutionary biology, infact John McDonald, the geneticist at Georgia tec, called it a great Darwinian paradox. He said, we've known this for twenty years, that micro does not scale to macro, well, this was published when i was a undergraduate, and many in the room were not even born.

If you want a fruit fly at all, you cannot perturb its early development. The problem is for macro-evolution to occur is that is exactly the place where the mutations have to take place. So you have this paradox. Hence you have this Darwinian paradox: In order to macro-evolve a species, if you will, you need to have early acting viable mutations. Thow those are the ones that are by far the most destructive. Which means that natural selection cannot operate. Natural selection thow it is a natural process, it is powerless to effect macro-evolution because the kind of variation that it needs is too destructive to animals.

If it was an animal, it would have gone through a development process, to change it into a anthropod, or a chordate, in other words, one of these different architectures would require the disrupting of its early development

Hair color, height, eye color, you name it, homo sapiens has a lot of variations, Those genes do not affect the overall body plans. What does not vary ? The genes that build your brain stem. The genes that give your heart, that are connected to the lungs, genes that give you a spinal colon, and so forth, variations on those most fundamental systems don't happen. because they have to happen early , and when they happen early, the bad effects cascade downstream, and the organism is destroyed. 

Research on evolution done within the darwinian framework in the past 30 years has actually discovered that that framework is actually false.

1. https://www.intechopen.com/chapters/70145
2. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2886-2
3. https://www.liebertpub.com/doi/10.1089/dna.2019.4988
4. https://www.biorxiv.org/content/10.1101/848762v3.full
5. https://www.uchicagomedicine.org/forefront/news/most-human-chimp-differences-due-to-gene-regulation--not-genes