Molecular biocoding of insulin

Figures & data

Figure 1 Number of atoms in insulin B chain.

Notes: And in that dimension we can find an explanation for the given empirical reality. Aforementioned amino acids are positioned from numbers 1 to 30. Numbers 1,2,3,n... present the position of a certain amino acid.

Figure 2 Fragment of insulin B chain.

Notes: The connection of the groups of these amino acids gives us number 29895 as a result. That was the code that represent one of the quantitative characteristics of the given genetic information.

Figure 3 Examples of biocodes.

Notes: The connection of the unions of these amino acids gives us number (−) 681061 as a result. That was the code that represent one of the quantitative characteristics of the given genetic information.

Figure 4 The formula for calculating of biocodes.

Notes: Aforementioned amino acids are positioned from number 1 to 21. The first amino acid in this example has 10 atoms, the second one 22, the third one 19, etc…. Why do they have exactly this many atoms? There is a program-cybernetic algorithm in which it is “recorded” that the first amino acid has to have 10 atoms, the second one 22, the third one 19, etc… Mathematical expression for this algorithm are biocodes 1,2,3,n.

Figure 5 Examples of the connection of numbers of atoms in group of three amino acids of the insulin chain.

Notes: Biocodes 1,2,3,n, represent one of the quantitative characteristics of the given genetic information.

Table 1 Overview of positive and negative values of biocodes for insulin chain B showing some of the quantitative characteristics of the insulin molecule and the exact mathematical balance between its components. Schematic representation of the biocode processing (1)

Biocodes 1,2,3,n	Biocodes 1,2,3,n	(1–2) (+)	(1–2) (−)
1	2	3	4
592056	205610	Bio 1 = 386446	–
205610	561056	–	Bio 2 = −355446
561056	105619	Bio 3 = 455437	–
105619	561954	–	Bio 4 = −456335
561954	195465	366489	–
195465	546514	–	−351049
546514	651455	–	−104941
651455	145510	505945	–
145510	551069	–	−405559
551069	106917	444152	–
106917	691758	–	−584841
691758	592056	99702	–
SUM		(+) 2258171	(−) 2258171

Figure 6 Examples of the analog codes of insulin chain A.

Notes: The analog expression for number 19 20 14 is 41 02 91, and number 14 17 14 is 417 141. Analog code is, actually, a discrete code that protects and guards genetic information coded in biochemical processes.

Figure 7 Group of chains E,F,G,H.

Notes: Aforementioned amino acids are positioned from number 1 to 306. Numbers 1, 2, 3, n... present the position of a certain amino acid. This positioning is of key importance for understanding of programmatic, cybernetic and information principles in this protein. The scientific key for interpretation of biochemical processes is the same for insulin and as well as for the other proteins and other sequences in biochemistry.

Table 3 mathematical balance between positive and negative values of analog codes of insulin chain A. Schematic representation of biocode processing (3)

Analog bio impulse 1,2,3,n	Analog bio impulse 1,2,3,n	(1–2) (+)	(1–2) (−)
1	2	3	4
912201	410291	Analog bio (+) 501910	–
410291	417141	–	Analog bio (−) 6850
417141	414122	Analog bio (+) 3019	–
414122	24222	Analog bio (+) 389900	–
24222	719122	–	Analog bio (−) 694900
719122	714142	Analog bio (+) 4980	–
714142	912201	–	Analog bio (−) 198059
SUM		(+) 899 809	(−) 899 809

Figure 8 A schematic diagram to show the positive and negative output biocodes.

Figure 9 Groups of chains (A,B,K,L), (C,D,I,J) and (E,F,G,H).

Notes: Each of these three unions of chains has an identical number of amino acids, identical number of atoms and identical sum of position numbers for the said amino acids.

Table 2 Overview of the mathematical balance between the positive and negative values of the biocodes of the insulin A chain. Schematic representation of biocode processing (2)

Biocodes 1,2,3,n	Biocodes 1,2,3,n	(1–2) (+)	(1–2) (−)
1	2	3	4
102219	192014	–	Bio 1 = (−) 89795
192014	141714	Bio 2 = (+) 50300	–
141714	221414	–	Bio 3 = (−) 79700
221414	222420	–	Bio 4 = (−) 1006
222420	221917	Bio 5 = (+) 503	–
221917	241417	–	Bio 6 = (−) 19500
241417	102219	Bio 7 = (+) 139198	–
SUM		(+) 190001	(−) 190001

Figure 10 Distribution of amino acids according to the criteria of mathematical similarity or difference.

Notes: The sum of the atoms in these amino acids, we, as a result, are given the number 36, or 6². That was the code that represents one of the quantitative characteristics of the given genetic information.

Figure 11 Groups of amino acids connected by the discrete code 11.

Notes: In the aforementioned examples, amino acids are connected by the code 6, and their position numbers are connected with code 11. As it can be observed, quantitative characteristics of the biochemistry of insulin apply to the position number.

Figure 12 Discrete code 11 connects groups of amino acids.

Notes: These groups of amino acids are connected by the discrete code 11.

Figure 13 Connection of the rank of amino acids G and N.

Notes: In this example we connected the position number of amino acids G and N, and got number 121 as result.

Figure 14 Connection of the rank of amino acids V and Y.

Notes: In this example we connected the position number of amino acids V and Y, and got number (11 × Y) as result.

Figure 15 Connection of the rank of amino acids Q and E.

Notes: In this example we connected the position number of amino acids Q and E, and got number (11 x Y1) as result.

Figure 16 A schematic diagram to show the positive and negative output biocodes of the insulin A chain.

Figure 17 A schematic diagram to show the positive and negative output analog biocodes of the insulin C chain.

Figure 18 Frequency of insulin.

Notes: Insulin is composed of amino acids with various numerical values. These numerical values are in irregular order. For example, the first one has 10 atoms, the second one 22. Their 10, 12, (−)3, 0 etc… … Frequency is the measurement for establishment of intervals of numerical values of amino acids in proteins.

Figure 19 Bio frequency (+), (−), and zero.

ChouKCPrediction of protein cellular attributes using pseudo amino acid compositionProteins20014324625511288174

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