Human did not evolve by random cell alterations and selection

Dr. Alfred Schurmann - computer scientist and mathematician

See also:  1)   Multicellular animals did not evolve from unicellular organisms   (line added on June 15, 2009)
               2)   Adaptive immune system did not evolve by mutations and selection   (text added on June 15, 2009)
            3)   Idea of the anti-Darwin theory of developing of lifein German, only summary in English (text added on june 15, 2009)
              
4)   Das System Mensch   (Zeile eingetragen am 03.10.2010)


Summary. The following question is considered: did human brain evolve by random alterations of germ cells and selection from an ape like progenitor brain?. First are noted (in Sec. 3) the main neuronal circuits in human brain, which realize the algorithms for learning and using a language, for counting and arithmetical operations and for abstract reasoning, which were not stored in brains of progenitors of human and apes. Then (in Sec. 4) it is shown that random alterations of germ cell functions did not build neuronal circuits realizing said algorithms, because there was only negative and no positive selection. Hence follows the first theorem that human did not evolve by random alterations of germ cells and selection. At the end is given the theorem that Darwin`s theory of life evolution by random alterations and selection is not true.

1. Introduction.
            The nucleotide sequences of genomes of a human and chimpanzees are more than 98 percent identical, however only 29% of human genes produce aminoacid sequences which are identical with the appropriate aminoacid sequences produced by chimpanzee genes (s. T.A. Brown [BRO], Sec. 18.4). This means that genes of human and chimpanzee function in more than 70% in different way, which does not explain the similar functioning of human and chimpanzee bodies, if we exclude they brains. Thus comparing molecular genome sequences and its products may lead to false conclusions about the similarity of two different organism systems. T.A. Brown ([BRO], Sec. 18.4) came to the conclusion that the differences between human and chimpanzee cannot be explained by comparing coding DNAs but how they are expressed. This supports the idea that we should interpret genes as nucleotide sequences in which are implemented meta-programs, where a meta-program in a gene is a system which builds (e.g. assembles) a program ( it may be empty if the meta-program (gene) is blocked (reppressed)) from regulation, coding and intron sequences of this gene; some meta-programs assemble a simpler meta-program from gene segments of different genes. I interpreted genes as meta-programs in [SC1] http://www.home-a-schurmann.de/eukar.html and in [SC2] "Adaptive immune....".
The main and essential difference between human and apes are in their brains; as we know human brain can perform algorithms (e.g. learning and using a language, abstract reasoning) which an ape brain cannot.
            Below I consider the question "could human evolve from a common ancestor (the progenitor) of homonoids and chimpanzees by random alterations of cell functions and selection?". Because all morphological features and functions, without brains, of apes and humans are more or less similar, I formulated the above question as follows:
Problem hba. Could the new algorithms of human brain evolve from brain of the common ancestor of human and apes by random alterations of germ cell functions and selection?
            First I characterize the essential cognitive algorithms which the progenitor of human and apes cold perform. Then (in Sec. 3), I note the main algorithms, implemented in human neuronal brain circuits, which an ape brain cannot perform. At the end I show that random alterations of germ cell functions of progenitor of human and apes, and selection did not establish neuronal brain circuits realizing algorithms for learning and using languages, for learning and performing arithmetical operations and for abstract reasoning.

2. Brain algorithms of the progenitor of humans and apes
          The progenitor of humans and apes had implemented in neuronal brain circuits only algorithms that were not more complex than the algorithms in chimpanzee brain. Thus, the most complex algorithms, implemented in neuronal brain circuits, which the progenitor could perform are the following:
ap1.         Algorithms for perceiving objects by means of taste, hearing, sight, smell and touch. We do not know how the neuronal circuits realizing these algorithms look like; but we know neural networks (algorithms) implemented in computers, which build visual, olfactory and acoustic patterns of objects and recognize objects by these patterns (s. e.g. B. Baddey et al. [BHF], 2000 ).
ap2.        Algorithms for building models of perceived objects. The said (in (ap1) taste, visual, olfactory, acoustic and touch patterns of an object, O, are features of this object model (denoted by M(O)) of the object O. Object model has also other non-abstract features like "it is dangerous", "it is friendly", "it cannot move" etc. (of course, these features were not formulated by means of a language).
ap3.        Algorithms for sending and perceiving communication signals. These signals are assigned to situations causing emotions such as danger, pain, a member of the community comes back, defense situation e.t.c. These system of communication signals is not a language as some people suggest, because these signals do not build even a simple sentence.
ap4.        Algorithms for simple concrete reasoning about solving the following concrete problem (I formulated it in an other way in [SC3], 1998). Progenitor, Pr, is in situation Sa in which are objects O1,...,On. Pr wants to achieve situation Sg (in which e.g. Pr can get food) which does not differ very much from the situation Sa. The problem of Pr: "can I apply some objects from O1,...,On so that I could achieve the desired situation Sg by activities which I can perform?". If situation Sg can be get from situation Sa only by pushing and applying not more than three objects (from O1,...,On) in a simple way, then Pr (and chimpanzee) can solve this problem. Thus, the progenitor Pr has algorithms (implemented in neuronal brain circuits) for very primitive and concrete reasoning, although Pr cannot formulate the said question "can I apply....".

3. Algorithms implemented in human brain, which are not in the progenitor brain
         Below I give the most essential algorithms (implemented in neuronal brain circuits) which make human so different from the progenitor and apes.
ah1.          Algorithms, realized in neuronal circuits, assigning names (symbolic acoustic patterns, called words) to classes of objects, objects and classes of activities - these are abstraction operations, because they classify objects and activities into distinct categories and build for each such class internal models. Thus, to each class of similar objects and class of similar activities is assigned a symbol name which represents and denotes this class. Such symbolic name differs from visual or acoustic patterns (features generated by an object), because the symbolic name is an abstraction not a feature. Note, when after learning, a dog responds to a spoken word then it is only an emotional signal and feature, for the dog, and not an abstract name.
ah2.         Algorithms for learning, building and recognizing sentences by means of symbolic names of object and activities (said in (ah1)). We do not know how these algorithms are functioning; we know only the results - we can learn and use language. These algorithms are very complex and are realized in several neuronal circuits located mainly in the left frontal cortex of human brain (s. Purves et al. [PAF], 2008, Chap. 27).
ah3.        Algorithms, realized in neuronal circuits, for learning and perceiving numbers, and counting; numbers and counting are of higher level of abstraction than symbolic names of classes of objects and activities said in (ah1).
ah4.        Algorithms, realized in neuronal circuits, for learning and performing arithmetical operations addition, subtraction and multiplication. We do not know these algorithms realized in neuronal brain circuits. We know only the algorithms for counting and arithmetical operations which are implemented in computers.
ah5.       Algorithms, realized in neuronal circuits, for reasoning and solving the following problem (formulated first in a modified way in [SC3], 1998): human, H, has stored (in his brain) object models M(O1),...,M(On) named N1,...,Nn, respectively; the reasoning problem of H: "could I achieve goal situation Sg if I would be in situation Sp in which are objects named N1,...,Nn by means of activities A1,...,Am?". These algorithms for abstract reasoning differ very much from simple concrete reasoning algorithms (ap4).

4. Human brain did not evolve by random cell alterations and selection
          Before I formulate the problem, stated in Introduction, in a new way, I explain what I mean by alteration of cell functions, namely one of the following operations:
i.          a gene mutation in this cell, i.e. a mutation in a regulatory part or in an exon or in an intron of this gene ( more about gene mutatins is given in [SC1] "Multicellular animals ..." and in [SC2] "Adaptive immune ...";
ii.         an alteration of gene expression - a gene stores a meta-program which builds/assembles a program controlling the production of a molecule (e.g. a protein), which has a metabolic function or is interpreted as a signal or as a meta-instruction (i.e. transcription factor) for activating or suppressing other meta-programs (i.e. genes); thus alteration in a gene expression means a change in executing the meta-program stored in this gene; a meta-program may assemble a simpler meta-program (gene) from segments of different genes (e.g. in the adaptive immune system, see e.g. P. Snustad & M. Simmons [SMS] or A. Schurmann [SC2] http://www.home-a-schurmann.de/imun.html);
iii.        alteration of signals send by the cell to neighbor cells;
iv.        alteration of the amount of produced molecules having the functions said in (ii).
         Below I consider alterations in germ cells, i.e. cells from which develop an adult organisms; thus in germ cells of a chimpanzee or a human are all genes for developing an adult chimpanzee or a human, respectively, where, by the low of heredity, from an ape germ cell cannot develop a human. As stated in Sec. 1 and 3, the main essential difference between a chimpanzee and a human is the functioning of their brains. So we can formulate the problem hba as follows.
Problem hb. Could a finite sequence of random alteration of germ cell functions and selection transform germ cells of the progenitor of human and apes into human germ cells from which develop brain containing neuronal circuits realizing the algorithms (ah1),...,(ah5)?
         Below I show that the answer is "no". First I note in short the following main steps of developing a human brain (s. D. Purves et al. [PAF], 2008):
i.        female and male germ cells unite;
ii.       development of three germ layers ectoderm, mesoderm and endoderm;
iii.      development of the notochord (a distinct cylinder of mesodermal cells) and the neuroectoderm, which is part of the ectoderm which lies above the notochord;
iv.      notochord sends signals to the neuroectoderm cells and transform it into neuronal precursor cells (called neuronal plate);
v.       transformation of the neuronal plate into neuronal tube;
vi.      some neuronal tube cells develop into a rudimentary brain;
vii.     generation of distinct brain regions and developing neuronal classes;
viii.    building many neuronal circuits, e.g. which realize the algorithms for steering moving and perceiving acoustic signals;
ix.      building further neuronal circuits, e.g. which realize the algorithms said in (ap1),...,(ap4) and (ah1),...,(ah5) - the building of circuits (ah1),...,(ah5) continues also after birds.
       Let us note that the neuronal circuits realizing the algorithms (ah2) and (ah5) could not evolve before circuits realizing algorithms (ah1) were established, because algorithms (ah2) and (ah5) use the results of algorithms (ah1). For the same reason, neuronal circuits realizing arithmetic operations could evolve only after neuronal circuits (ah3) for learning numbers and counting were established.
       To solve the Problem hb, let us consider whether the neuronal circuits realizing algorithms (ah1) could evolve first by selection and random alterations of germinal cell functions, and afterwards could evolve the neuronal circuits realizing algorithms (ah2) for learning and using a language. Thus, could a sequence sn1,...,snv,sp1,...,spr of random alterations of germ cell functions transform some germ cells gp1,...,gpw of w progenitors of humans and apes so that:
a)      random operations /alterations sn1,...,snv transform germ cells gp1,...,gpw into germ cells gh1,...gh1u from which develop the neuronal brain circuits realizing the algorithms (ah1);
b)      random operations/alterations sp1,...spr transform germ cells gh1,...,gh1u into germ cells gh21,...,gh2a from which develop neuronal brain circuits realizing algorithms (ah1) and (ah2);
c)      only the random operations sn1,...,snv,sp1,...,spr made the significant alterations said in (a) and (b) so that all other body parts of the progenitors (including other brain regions) developed in humans in a similar way as in progenitors (because there is no significant difference between the said progenitor and human, apart from the brain regions said in (ah1),...,(ah5)).
         The algorithms (ah1) are probably not very complex; so let us suppose that random alterations of germ cells gp1,...,gpw could transform it into said germ cells gh11,...,gh1u (altthough I think this assumption is false, but I am not sure I could prove that it is false). But let us note that:
-      the neuronal circuits (ah1) established in a progenitor brain gave the so modified progenitor no advantage to other progenitors (because said abstract classes of objects and activities were useless, and the symbolic names could be used only as signals, but signals used also other mammals), so no selection occurred at this stage;
-      random alterations sp1,...,spj, for j<r, gave the so modified progenitor no advantage to other mammals and there was only negative selection, i.e. the modified brain could function wrong and the modified progenitor could be bad adapted to its environment. Only if the circuits realizing the algorithms (ah1) and (ah2) would be established in more than one progenitor, then they would have advantage, because they would be able to communicate more efficiently with each other.
         The considered alterations sp1,...,spr must be in cells of the developing brain of the embryo, when it is in said stage (vii) of development. In said altered, and may be in additional not altered, cells must be implemented super complex algorithms (let it be denoted by Aga) which generate/build, in stages (vii), (viii) and (ix), the neuronal circuits realizing the very sophisticated algorithms (ah2); these circuits consists of more than 800 neuronal cells, they are linked together and with the neuronal circuits (ah1), and cooperate efficient with each other (s. D. Purves et al. [PAF], 2008). Because of the complexity of said algorithms Aga, there must be more than 40 alterations (i.e. r>40). But so many random alterations, with negative and no positive selection, which satisfy conditions (a), (b) and (c) could not exist in reality - more than 40 random alterations of germ cells, with no positive selection, cannot build so sophasticated algorithms as Aga.
Conclusion 1. It is not true that the neuronal circuits realizing the language algorithms (ah2) evolved by random alterations of germ cell functions and selection.
Remark: according to the probability theory, there exist a sequence of random cell alterations which lead to the algorithms Aga, but such theoretical case is not scientific realistic.
          Now we consider, similar as above, the question whether the neuronal circuits realizing the algorithms (ah3) for learning numbers and counting, and algorithms (ah4) for learning arithmethical operations +, - and * could evolve by random alterations of germ cell functions and selection. Note that neuronal circuits (ah4) could not evolve before neuronal circuits (ah3) were established. The above question I write more precisely as follows:
Could a sequence nc1,...,ncm,ao1,...,aoz of random alterations of germ cell functions transform some germ cells gc1,...gcx of x progenitors of human so that:
a1)       random alterations of cell functions nc1,...,ncm transform germ cells gc1,...,gcx into germ cells gc11,...,gc1y from which develop the neuronal circuits realizing the algorithms (ah3) for learning numbers and counting;
b1)       random alterations of cell functions ao1,...,aoz transform germ cells g11,...,gc1y into germ cells gc21,...,gc2q from which develop neuronal circuits realizing algorithms (ah3) and (ah4);
c1)       the random operations nc1,...,ncm,ao1,...,aoz made only the significant alterations said in (a1) and (b1), so that all other parts of progenitor bodies (including other brain regions) developed from germ cells gc21,...,gc2q in a similar way as from the germ cells gc1,...,gcx of the x progenitors.
          Let us note that:
-      random alterations nci, for i<m, and aoe, for e<z, in early homo sapiens brain, gave the so modified homo sapiens no advantage to other mammals, so there was no positive selection, and there was only negative selection because the modified brain could function wrong and the so modified homosapiens could be bad adapted to its environment;
-       establishing neuronal circuits for realizing algorithms (ah3) and (ah4) did not give the early human (more than 15000 years ago) advantage to other mammals, because he did not use these algorithms (there were useless for him), so no positive selection occurred.
         The considered alterations nc1,...,ncm,ao1,...,aoz must be in cells of the developing embryo brain when it is in said stage (vii) of development. In said altered, and may be additional not altered, cells must be implemented super complex algorithms (let they be denoted by Aga1) which generate/build the neuronal circuits realizing said algorithms (ah3) and (ah4) in said stages (vii), (viii) and (ix); these circuits must consist of more than 150 neuronal cells, and they are linked together and cooperate very efficiently. Because of the complexity of said algorithms Aga1, there must be more than 30 said alterations (i.e. m+z>30). But so many random operations with negative and no positive selection, such that conditions (a1), (b1) and (c1) hold, cannot build so sophisticated algorithms (ah3) and (ah4).
Conclusion 2. It is not true that the neuronal circuits realizing the algorithms (ah3) and (ah4) for learning numbers and counting, and performing arithmetical operations evolved by random alterations of germ cell functions and selection.
         Similarly as conclusion 1 and 2, we can get
Conclusion 3. The neuronal circuits realizing the algorithms (ah5) for abstract reasoning did not evolve by random alterations of germ cell functions and selection.
          Each Conclusion 1, 2 and 3 proves
Theorem 1. Human did not evolve by random alterations of germ cell functions and selection from an ape like progenitor.
           In [SC1] http://www.home-a-schurmann.de/eukar.html  I proved that multicellular animals did not evolve by gene mutations and selection from unicellular organisms, and in[SC2] http://www.home-a-schurmann.de/imun.html  I proved that adaptive immune system did not evolve by mutations and selection. It is known (s. D. Snustad & M. Simmon [SMS], Sec. "Mutation:Usually a Random, Nonadaptive Process") that environmental stress does not direct or cause genetic changes. From these results and Theorem 1 follows:
Theorem2. Darwin`s theory of life evolution is false - some organism systems (including human) did not evolve by random alterations of cell functions (or genes) and selection, and environmental stress of living organisms did not cause or direct genetic changes of these organisms.

References

[BRO]    T.A. Brown: Genomes 3, Garland Science Publishing, 2007
[BHF]     R. Baddley, P. Hancock, P. Födiak (ed): Informationtheory and the brain; Cambridge University Press, UK, 2000
[PAF]      D. Purves, G.J. Augustine, D. Fitzpatrick, W.C. Hall, A.S. LaMantia, J.O.  McNamara, L.E. White: Neuroscience; Sinar Associates, Inc., Massachusetts, USA,2008
[SC1]      A. Schurmann: Multicellular animals did not evolve from unicellular organisms;  http://www.home-a-schurmann.de/eukar.html 
2008  
[SC1]    A.Schurmann: Adaptive immune system did not evolve by mutations and selection, http://www.home-a-schurmann.de/imun.html  , 2009
[SC3]     A. schurmann:  A simple Thinking Artificial Servant,  1998

[SMS]    D. P. Snustad & M. J. Simmons; Principles of Genetics; John Wiley & Sons, Inc., USA (2006).



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Alfred Schurmann                                                  
 
Copyright  March 23, 2009;    corrected March 27, 2009