Correlative Variation of The Essential Amino Acids

Under the expression correlative variation Charles Darwin understands that the whole organization is internally connected during growth and development, and when weak variations occur in one part and are cumulated by natural selection, the other parts are modified. Modifications in the structure recognized by taxonomists for a very important, can depend solely on the laws of variation and correlation [1]. For example, Phyto enosis [2] has at least three fundamental properties: first, the correlative variation in the values of parameters in time and space; second, the correlation depends on the genotypic properties of the plant species; third, the variation is due to phenotypic properties, as well as the cycles of solar activity [3], the rotation of the moon around the Earth and our planet around itself [4].

Thus, we adopted the basic hypothesis that typing, and the classification has no effect on biotech «based on mastery of life» laws. Therefore, the variation (the set of deviations from the Darwin correlation) depends on the «human factor», i.e. on the quality of measurements of soil properties [6] and plants [4].
Soil according to VV Dokuchaev [7] is a living organism.
Therefore, the principle of Darwin's correlative variation should provide high adequacy of the revealed regularities [6]. Similarly, a priori we will consider experiments with essential amino acids [8] to measure their concentration in different types of products for humans and animals highly correlative. weak factor connections may be stronger for other combinations of the studied objects. As a result, there is a mathematical tool [9][10][11][12] (identification method) for comparison of different natural and artificial (technical) objects [13].
The coefficient of correlative variation is taken into account for many factors of the physical object of study, that is, biological, Functional connectivity is a universal property of matter. For example, internal correlation variation is observed in the results of agrochemical analysis of soil samples [6], as all agrochemical parameters are measured on the same sample. Sampling sites do not affect the internal connectivity of biochemical and other reactions, that is, the same interactions between chemical elements and their compounds are observed on Earth. Such a community is called an ecosystem [2] or a biosphere superposition.
The strongest correlative variation over 0.999 is observed in the set of genes [13][14][15]. Slightly less, but more than 0.99, as will be shown in this article, such a variation exists in the group of essential amino acids.

Essential Amino Acids
These are essential amino acids for animals that cannot be synthesized in the body, in particular, human. Therefore, their intake from food is necessary (Table 1).

Rating of Influencing and Dependent Factors
To determine the coefficient of correlative variation of nine amino acids among 22 types of products it is necessary to conduct a factor analysis [13]. Due to the absence of a measured value of glycine content in one cell of Table 1 in the row «Shiitaki mushrooms», factor analysis was first carried out [13] nine factors and 21 products. The coefficient of correlative variation was equal to 0.9985. All binary 9 2 -9 = 72 relations are characterized by the exponential law (Table 2) shows the correlation matrix of binary relationships and the rating of eight factors excluding glycine for 22 products according to Table 1.   The coefficient of correlative variation is 0.9727, which is significantly less than 0.9985. In the future, it turned out that in addition to the indicative law, the biotechnical law [4-8,10-13] of the stress excitation of amino acids depending on each other is additionally taken into account (Table 3) where y -amino acid content in the product as an indicator (g per 100 g of product), x -amino acid content of the product as an influencing variable (g per 100 g of product),  Neutral type appears only without shiitake mushrooms, that is, when the amino acid content changes from 0 to 2 (maximum 2.009 for beef) ( Table 4).
The maximum concentrations of nine amino acids without shiitaki mushrooms are in two products-beef and chicken meat. In the amino acid content range from 2 to 7 in Table 1 there are no types of products (except mushrooms). Therefore, it is necessary to add new products to the list and Table 1.
In the concentration range from 0 to 7, two types of behavior c. These two types provide optima for the interaction of essential amino acids.

Correlation matrix
From Table 2, choose a binary relationship with a correlation coefficient of at least 0.99 ( Table 5).
The neutral behavior of amino acids receives only 15 binary bonds at the level of superstrong adequacy (or 100 15 / 72 = 20.83%).
We will do the same with the data in Table 3 (Table 6). Table 5 shows a significant increase in the number of super strong links, 33% or 45.83%. The matrix remained complete-eight rows and columns.  However, the behavior of biological objects is characterized, in addition to the trend (2) vibrational adaptation [6,12,14,15]. To identify the wavelet signals from the amino acid behavior under mutual influence, it is necessary to significantly expand Table 1 also with interchangeable amino acids. Especially it is necessary to pay attention to the types of products that give a concentration of 2 to 7. The effect of each essential amino acid on the concentration of other amino acids is shown in four graphs, which are arranged in figures in descending correlation coefficient. Of the 72 graphs in the article shows a total of 32 graphics (Figure 1-8       On the charts, the positive behavior of essential amino acids is shown as a convex curve, and the negative behavior is shown as a concave curve. On the effect of histidine and methionine it is possible to estimate the minimum interval of the concentration of essential amino acids with neutral type. As can be seen from the graphs in figure 3, the neutral behavior type is from 0 to 0.5.

Binary Relation Graphs
The convexity or concavity of the graph has a different length along the abscissa axis. It is obvious that the addition of the list from Table 1 with additional products having amino acid concentration in the range from 2 to 7, will allow to specify the parameters of the model (2) and add a wave function. However, in our opinion, the nature (design) (2) of the expansion of the list of products will not change.    Model parameters (2) are given in (Table 7). The positive form of the equation (2) is used in the recording. Then the negative type is easily determined by the negative sign before the model parameter 3 a .
Apparently, for the most complete amino acid system, the total sum of codes will approach zero. As a variable on the positive effect in the first place -phenylalanine, and among the dependent indicators -lysine.

The Quality of the Source Data and Ratings of Products
A tabular model is a good-quality and relatively complete table of input data for statistical modeling by identification of stable laws and regularities. The quality factor is understood as the accuracy of the numbers, the primacy of the indicators (the factor analysis is not initially permitted secondary received, calculations, parameters), consistency of the description of the object of the studies take into account many parameters.
As a result, we get the distribution of places in descending order (Table 9).
Let's check the initial data of s for the quality factor.
Any of the factors is the vector orientation and the two possible behaviors: Table 9: Rating of products by content of essential amino acids. b) it is better to lower, so the rank is given to a minimum, and the ranking is performed by increasing the values of the factor.
From the hypothesis -the greater the content of any essential amino acid in any product, the better -the first option of a vector of behavior is accepted.
Three places in the ranking took products: mushrooms, beef and chicken.
The rating in Table 8 is determined in places I . The best theoretical first place is obtained if . Then it turns out that shiitake mushrooms in a variety of products according to the Table   1 is theoretically possible in the first place. For the second place the sum of ranks is 12. Next, we can take places I for the explanatory variable, and the sum of ranks R ∑ from Table 8 for the indicator.
After identification of the general trend formula [9], we obtained ( Figure 9) formula

Ranking Distribution of Amino Acids
For statistical modeling, the ranks must start from zero, with the rank distributions subject to the exponential law (growth or death).
-rank distribution of tyrosine -ranking distribution of lysine (11) The sum of the squares of deviations from the equations of binary and unary (by ranks) relations between eight essential amino acids is written in the dispersion matrix (Table 10).

Conclusion
We have extended the principle of correlative variation not only to Charles Darwin organisms, but also to populations (in the article population of eight amino acids) and even to any biological, biotechnical and technical systems [13]. This principle allows to compare heterogeneous systems on one or some set of factors by functional connectivity.
The coefficient of correlative variation, as a generalized criterion for comparing different sets of homogeneous biological objects, gets a very high value. For example, populations of genes [14,15] obtained the correlative coefficient of variation not less than 0.9999.
In the example of this article, the level of adequacy for the set of eight essential amino acids is not less than 0.99. This makes it possible in the future to create the most complete table of contents and other indicators for a system of 20 amino acids and hundreds of objects, including products.
Functional connectivity between essential amino acids was super strong and it is subject to a simple formula of two-term trend containing exponential and biotechnical laws. The absence of the second term determines the neutral type of behavior, and signs in the presence of the second member characterize the positive (+) or negative (-) type of behavior of amino acids in the studied system of products. To identify the effect of oscillatory adaptation of essential amino acids to each other in some sets of products need more accurate (with measurement error, less than an order of magnitude) data.