format_quote Originally Posted by
Mahmoudov
The details are there : the nuclear fusion ,explaining the intelectual rooting (the
successive theories)
witch led to the "Nuclar fusion" would be a physics course ,any one can find this in the internet .
what you are saying is there is no evidence of nuclar fusion ?oh my friend it's a very havy statement .
this witch?
lead to 'nuclear fusion' and that means there is no God?
I don't understand what you are writing!
Nuclear fusion is a reaction where nuclei combine and in the process release massive energy.. would you like to tie this in for us with the topic and the ready made conclusions?
It seems you are having a soliloquy by yourself on the side where you not only have all the answers, but have made assumptions for others that I assume you wish to engage?.. I am neither your friend nor have I said that there is no evidence of nuclear fusion.
This is how you write a physics paper on evolution:
http://arxiv.org/ftp/q-bio/papers/0603/0603005.pdf
THE MODERN THEORY OF EVOLUTION FROM THE VIEWPOINT OF
STATISTICAL PHYSICS
ALEXEY V. MELKIKH
Ural State Technical University
19 Mira St., 620002 Yekaterinburg, Russia
mav@dpt.ustu.ru
The problem of the rate and mechanisms of biological evolution was considered. It
was shown that species could not be formed due to undirected mutations in
characteristic times of about one million years. A mechanism of deterministic
molecular evolution assuming a directed change of the genome was proposed.
Key words: undirected mutations; deterministic evolution; probability of new
species origin
Introduction
Views on biological evolution have changed considerably in recent decades
since much success has been achieved in the study of the structure and functions of
the genome (adaptive mutations, mobile genetic elements, epigenetics, the
horizontal transport of genes, etc.). Many scientists speak about the change of the
evolution paradigm and new genetics (see, for example, [1-3]), while other hold, as
before, to neo-Darwinian views [4-12]. Does all this new knowledge about genes
lead to a new theory of evolution or represent just an improvement of the
Darwinian theory of evolution?
Modern theories do not provide evidence that some other, rather than
Darwinian (undirected changes of the genome, selection and the genetic drift),
mechanism of evolution operates. Models of the evolution process are needed so as
to make estimates and show what mechanism is in play exactly.
Such estimates are absent in both the neo-Darwinian theory [10] and models
of the horizontal transport of genes [13-15], epigenetic processes [16-17], adaptive
mutations [18-20], etc. Although there is a great number of studies dealing with
simulation of evolution of species, neither author calculated the probability that
new species can appear by way of undirected changes of the genome. For example,
numerical calculations were made in Eigen's model of quasi-species for short
nucleotide chains only. The applicability of the model to real genomes (which are
about 109 long) has been postulated as something obvious. However, this is just the
main problem: the number of possible combinations of nucleotides rises with the
growth of their number in the genome, while the number of organisms, on the
contrary, generally decreases (on transition from protozoa to higher organisms).
From the physical viewpoint, Darwinian evolution represents an analogue of
the Brownian motion in the space of attributes of organisms. In this sense, it is
important to estimate characteristic times of evolution. Can new species appear by
diffusion in the space of attributes?
1. Evaluation of the Probability of New Species Formation in Terms of
the Synthetic Theory of Evolution
According to the modern theory of evolution, the main mechanisms by
which new species appear are mutations and the horizontal gene transport. It is
assumed that mutations in a genome can be not only casual, but frequently
represent rearrangements of blocks already available in the genome. At the same
time, the basic proposition of the modern theory of evolution is its nondirectivity.
That is, whatever mutations may be, they are not directed a priori to creation of
"good" combinations of nucleotides. The same reasoning applies to processes of
the horizontal transport of genes: while being largely non-casual, they are not
oriented to creation of "good" genes.
Otherwise, aprioristic information about the structure of the genome of
future species would be needed for directed changes in the genome. According to
STE, such aprioristic information is unavailable.
To estimate characteristic times necessary for appearance of new species, we
shall consider a genome having the following properties (in accordance with main
provisions of the modern theory of evolution):
1. A genome represents an ordered set X with elements (nucleotides) X1, …,
XN (N being the total number of nucleotides in the genome). Each element can take
one of four values (A, T, G, C). The following operations Pi are applicable to the
elements: replacement of individual elements, replacement of blocks of elements,
and any rearrangements of blocks and elements. All these processes have different
probabilities.
2. The operations cause appearance of a new organism whose survival
probability is determined by the genome structure and properties of the
environment. An ecological niche (occupied by one species) represents a region of
the phase space where a small number of organisms increases in quantity (the
reproduction rate is positive). Let us label the set of genomes of organisms in the
niche as L0. At the initial moment of time each niche is surrounded by k empty
nearest niches (the corresponding sets are labeled L1…Lk). Neighboring ecological
niches are separated by a region where the reproduction rate is negative.
3. The nearest neighboring species have N1 different nucleotides (the
Hemming characteristic distance between nearest neighboring sets is Li). We shall
refer to nucleotides, which belong at least to one of the sets L1 … Lk, as "good"
ones. The characteristic distance between organisms of one species is labeled N2
(the intraspecific distinction) (Fig. 1).
4. Whatever the operations Pi may be, they are not oriented a priori to
creation of new species adapted to the environment. Organisms do not know the
location of nearest ecological niches, while positions of "good" nucleotides in the
genome are unknown a priori.
N2
N1
L1
L2
Lk
L0
These properties coincide in many respects with properties of the quasispecies
model [10], but the probability of species formation was not evaluated in
terms of the last model for real sizes of genomes. In this sense, the property 4,
which forms the basis of STE, has the principal significance.
Let the probabilities that two arbitrarily chosen nucleotides Xk and Xl are
"good" (belong to one of neighboring sets L1 … Lk) be equal to pk and pl
respectively. Does the probability that they are "good" simultaneously depend on
the numbers of these nucleotides (their positions in the genome)? If such
dependence exists, it will mean the availability of the aprioristic knowledge which
nucleotides are "good" and which are not. This is in conflict with the property 4
and changes of the genome prove to be directed. Then the numbering order of
Fig. 1. Ecological niches (colored grey)
nucleotides is not important for calculation of the probability that they are good.
Obviously, the notion of "a block" becomes senseless for calculations of this
probability: since boundaries of blocks of a neighboring species are unknown a
priori, it is not important whether any nucleotides change under the action of
operations in a mutually correlated pattern or not.
Since nearest species differ by N1 nucleotides, it is necessary to determine
the probability that they will be exactly N1 (considering the distinction between
organisms within one species). Furthermore, these nucleotides should be correctly
distributed in the genome. Notice that if random walks occur in the space of
attributes and the position of neighboring niches is not known beforehand (the
darwinian mechanism of evolution), the position of mutated nucleotides in the
genome and their structure are unknown either. If such aprioristic knowledge is
unavailable, the probability of N1 nucleotides hitting "right" places is
( )
3 1
1
!
! ! 1 1
1 N N
W N N N −
= . (1)
The formula (1) is deduced as follows. The probability that the first mutated
nucleotide is "right" (i.e. not only hits the place of mutation, but also is the one
needed) equals
N
N
3
1
.
The probability for the second mutated nucleotide is
3( 1)
1 1
−
−
N
N
.
And for the last
( )1 3
1
N − N .
The general probability is the product of these probabilities and, hence, we
have the formula (1).
If an error, which is possible at this stage (the intraspecific distinction), is
taken into account, we have:
( )
3 1 2
1
!
! ! 1 1
N N N
N N N
−
−
. (2)
Let us estimate this value using Stirling's formula for large numbers:
( ) ( ) ( )
ln ( )ln 3
ln ln
3
1
!
ln ln ! !
1 2
1 1 1 1
1 1
1 1 2
N N N N
N N N N N N
N
W N N N N N
− − −
= − − + −
−
= − .
For example, at N = 3×109, N1 = 0.01N and N2 = 0.001N (for example, see
[21-24]) we have
8
1 lnW = −0.066N ≈ −2 ×10 .
Then for W1 we obtain
( 8 )
1 W ≈ exp − 2×10 .
Thus, the probability that mutated nucleotides prove to be exactly the ones
needed for formation of a new species is vanishingly small: new species of
organisms could not appear due to undirected mutations.
Let us estimate separately the probability that the number of mutated
nucleotides will fall within the interval
2 2
2
1
2
1
N N N N N X − < < + .
Let the number of mutated nucleotides NX have the Gaussian distribution.
Since this number is large, this distribution may be viewed as continuous:
( ) ( )
−
= − 2
2
2
1
2 2
exp
2
1
m
X m
m
X N
N N
N
f N
π .
Here N2m represents the characteristic width of the distribution. The
probability that the number of nucleotides falls within the specified interval is
( )
∫
+
−
−
= −
/ 2
/ 2
2
2
2
1
2
2
1 2
1 2 2
exp
2
1 N N
N N
X
m
X m
m
dN
N
N N
N
W
π .
What parameters of this distribution should be for W to be a maximum? It is
easy to show that the maximum W2 (equal to unity) will be realized when
1 1 N N m =
and N2m tends to zero. In this case, the distribution of nucleotides represents a δ-
function. Of course, this assumption is unrealistic and in the nature any such
distribution (even if nucleotides are not independent) has a final dispersion.
Therefore, if the real distribution of nucleotides is taken into account, the
probability of hitting the specified interval decreases still more. Let us make the
upper estimate, i.e. assume that exactly
2 2
2
1
2
1
N N N N N X − < < +
nucleotides change as a result of mutation.
How many attempts are required so that at least one organism reaches any of
the neighboring ecological niches? Since positions of elements belonging to the
sets L1 … Lk are unknown, the distance between the sets remains (on the average)
the same (according to the property 4, all the operations are not directed) after the
first attempt, i.e. can decrease or increase with an equal probability. Therefore, the
total number of attempts can be counted taking the number of organisms of a given
species ever living on the Earth. They may be both descendants of one organism
(transition in a great number of small steps) and descendants of other organisms
(parallel transition).
Therefore, we shall multiply the probability that at least one organism gets
into a neighboring niche by the number of organisms of the given species ever
living on the Earth. Thus, all possible attempts (whatever their factor of
multiplication between niches be, the real number of organisms is limited by
natural resources) are taken into account. If some organisms do not survive the
action of selection, the number will be still smaller.
Even the very probability of the first hit represents a combinatorially small
number. It is obvious therefore that real dimensions of populations of any
organisms cannot considerably increase this probability.
Finally, the obtained probability is multiplied by the number of nearest
ecological niches adjacent to the given niche. This operation gives (considering the
assumptions made) the upper estimate of the probability that at least one organism
reaches at least one neighboring ecological niche:
( )
3 1 2
1
!
! ! 1 1
N N N
k N N N
T
W m t −
−
= . (3)
Here T is the lifetime of an organism, t is the characteristic time of formation of a
new species, m is the number of a population of organisms, and k is the number of
ecological niches adjacent to the given niche. Let us also take into account the
known relations N1 = 0.01N and N2 = 0.001N. The W1 estimate (2) suggests that
the number W is negligibly small too ( W ≈ 10−57000000 ).
The estimates disregarded processes of the further conversion of information
in the genome (the genome regulation, alternative splicing, etc.). It can be shown
however that the probability of formation of new species is of the same order of
magnitude if one compares sequences of amino acids in proteins.
Thus, species could not appear due to undirected mutations. Therefore, a
considerable part of mutations and operations of the horizontal transport of genes
are directed to creation of organisms a priori adapted to new ecological conditions.
In this case, the very mechanism of evolution changes drastically: it turns from
casual to deterministic.
2. Genome as a Neurocomputer
There is a variety of papers, in which a genome is treated as a network of
interconnected genes analogous to a neuron network (see, for example, [25-28]). If
an analogy is drawn between a genome (or several genomes) and a neuron
network, it should be noted that a fundamental property of a neuron network,
without which it cannot work, is the presence of two modes: recording and pattern
recognition. The work of a neuroprocessor starts from presentation of a pattern.
The pattern presentation (input of a primary set of attributes) is realized as follows.
At the initial moment of time signals that activate some elements are transmitted
via external links. A presented pattern is maintained for some time, during which
the links "learn" (in other words, conductivity of current-carrying links decreases).
After the learning procedure, the processor can recognize objects by relating them
to some class of objects it learned.
Thus, a neuron network (or similar structures) will not work without a
priori standards (an initial set of attributes or reference patterns)! In this case, it has
nothing to compare with a received signal and the decision as to the "good" or
"bad" signal is impossible. If some special assumptions on properties of a
"genome" neuron network are not made, this network cannot process signals from
the environment and adequately react to them.
In this case, environmental effects will have the character of a random
process (in the aforementioned sense) and cannot accelerate evolution. That is, the
model proposed in [2] is a particular case of neo-Darwinism.
If it is assumed that a program of genes control appeared casually, we
again encounter a contradiction since the probability of formation of this program
proves to be vanishingly small. If, thanks to random processes, a genome forms a
sequence encoding proteins, which are responsible for shuffling of genes or their
parts, there are no reasons to assert that new genes will be "good". The probability
of this process should be estimated using the formula (3) for random processes,
which gives vanishingly small probabilities of such events.
3. Main Ideas of the Deterministic Theory
Of course, the construction of the deterministic theory requires a special
discussion, but even now it is possible to formulate basic ideas of this theory (see
also [29, 30]).
1. Random processes, which cause changes of the genome, may take place
concurrently with deterministic processes representing the purpose-oriented work
of molecular machines. Such a controlled change of the genome essentially
approaches the morphogenesis. Consequently, new species of organisms will not
appear casually, but will result from a deterministic process.
2. Information cannot appear from nothing (such a process would contradict
the second law of thermodynamics). Therefore, aprioristic information about new
species should be encoded in some structures. Conformational degrees of freedom
of proteins presumably may serve as the storage of this aprioristic information.
These degrees of freedom represent an additional information resource since only
the sequence of protein amino acids, but not the spatial structure of the protein is
encoded in genes.
3. Laws of functioning of molecular machines are general for different cell
processes. From the viewpoint of statistical thermodynamics of irreversible
processes, the work of these machines consists in an efficient conversion of one
form of energy to its another form – the cross effect (see also [31]). Operations on
genes, which can be a result of the work of molecular machines, may be reduced to
several elementary operations, such as identification of a DNA fragment (a
protein), cutting of a macromolecule, cross-linking, etc.
Conclusion
Thus, this study showed that undirected evolution of organisms takes a too
long time for appearance of new species (including the case where genes form a
complex network). A mechanism of deterministic evolution was proposed. The
essence of this mechanism is that possible species of organisms are predetermined
by properties of proteins and nucleotides. The structure and chemical properties of
nucleotides, amino acids and other substances essential for life are such that
changes in a genome, which lead to appearance of new species, become
controllable. The formation of new species represents a deterministic process
approaching the morphogenesis.
References
1. J.A. Shapiro “A 21st Century View of Evolution,” J. Biol. Phys. 28 1-20
(2002).
2. E. Ben-Jacob “Bacterial wisdom, Godel’s theorem and creative genomic
webs,” Physica A 248 57-76 (1998).
3. Golubovsky M.D. Mutational process and macroevolution. Genetica. 1980.
V 52/53. P.139-149.
4. D. Eriksson and H.J. Jensen, Darwinian selection in a locally unstable
Boolean Network. J Stat. Mech.: Theor. Exp. P09001 (2004).
5. K.M. Page, M.A. Nowak, “Unifying evolutionary dynamics,” J. Theor. Biol.
219, 93-98 (2002).
6. M. Kolar, and F. Slanina, “How the quasispecies evolution depends on the
topology of the genome space,” Physica A 313, 549-568 (2002).
7. R. Garcia-Pelayo, “A linear algebra model for quasispecies,” Physica A 309,
131-156 (2002).
8. A. Orr, “A minimum on the mean number of steps taken in adaptive walks,”
J. Theor. Biol. 220, 241-247 (2003).
9. A. Pross, “The driving force for life’s emergence: kinetic and
thermodynamic consideration,” J. Theor. Biol. 220, 393-406 (2003).
10. M. Eigen and M. Schuster, “The Hypercycle - A Principle of Natural Self-
Organization,” Springer-Verlag, Berlin (1979).
11. Garske T., Grimm U., Maximum principle and mutation threshold for fourletter
sequence evolution. JSTAT. (2004). P07007.
12. Jain K., Krug J., Evolutionary trajectories in rugged fitness landscape.
JSTAT. (2005). P04008.
13. A.S. Novozhilov, G.P. Karev and E.V. Koonin “Mathematical modeling of
evolution of horizontally transferred genes,” Mol Biol Evol. Aug;
22(8):1721-32 (2005).
14. C.G. Kurland, B. Canback and O.G. Berg “Horizontal gene transfer. A
critical view,” Proc Natl Acad Sci USA 100: 9658-9662 (2003).
15. E. Pennisi, “Genome data shake tree of life,” Science 280: Issue 5364. 672-
674 (1998).
16. R.N. Tchuraev. A new method for the analysis of the dynamics of the
molecular genetic control systems: 1.Description of the method of
generalized threshold models. J. Theor. Biol., 1991. V.151.
17. Aurell E. Sneppen K. Epigenetics as a first exit problem, Physical Review
Letters 88, 048101, (2002).
18. Rosenberg, S.M. (2001.) Evolving responsively: adaptive mutations. Nature
2, 505-515.
19. Foster, P.L. (1993.) Adaptive mutation: The uses of adversity. Annu. Rev.
Microbiol. 47, 467-504.
20. Shapiro, J. A. (1984.) Observations of the formation of clones containing
araB-lacZ fusions. Mol. Gen. Genet. 194, 79-90.
21. J.A. Coyne and H.A. Orr “Patterns of speciation in drosophila,” Evolution
43 (2) 362-381 (1989).
22. J.A. Coyne and H.A. Orr “The evolutionary genetics of speciation,” Phil.
Trans. R. Soc. Lond. B 353 287-305 (1989).
23. C.D. Jiggins and N. Davies “Genetic evidence for a sibling species of
Heliconius charithonia (Lepidoptera; Nymphalidae),” Biological Journal of
the Linnean Society 64 57-67 (1993).
24. L.L. Cavalli-Sforza “Genes, Peoples and Languages,” New York: North
Point Press (2000).
25. T. Honma and K. Goto “Complexes of MADS-box proteins are sufficient to
convert leaves into floral organs,” Nature. 409 N6819. 525-529 (2001).
26. N.A. Kolchanov, N.A. Nedosekina, E.A. Ananko et al. “GeneNet database:
description and modeling of gene networks,” In Silico Biol. V.2 N.2. 97-110
(2002).
27. G. Theissen and H. Saedler “Floral quartets,” Nature. 409 N6819 469-471
(2001).
28. E. Abouheif E and G.A. Wray “Evolution of the gene network underlying
wing polyphenism in ants,” Science. 297 N 5579 249-252 (2002).
29. Melkikh A.V. Deterministic mechanism of molecular evolution. Proceedings
of the International Moscow Conference on Computational Molecular
Biology. 227-228. 2005.
30. Melkikh A.V. Could life evolve by random mutations? (Biofizika). 2005.
31. A.V. Melkikh and V.D. Seleznev “Models of active transport of ions in
biomembranes of various types of cells,” Journal of theoretical biology. V.
324, Issue 3. 403-412 (2005).
all the best