Insulin can be represented by two different forms, ie, a discrete form and a sequential form. In the discrete form, a molecule of insulin is represented by a set of discrete codes or a multiple dimension vector. In the sequential form, an insulin molecule is represented by a series of amino acids according to the order of their position in the 1AI0 chains. Therefore, the sequential form can naturally reflect all the information about the sequence order and length of an insulin molecule. The key issue is whether we can develop a different discrete method of representing an insulin molecule that will allow accommodation of partial, if not all, sequence order information? Because a protein sequence is usually represented by a series of amino acid codes, what kind of numerical values should be assigned to these codes in order to optimally convert the sequence order information into a series of numbers for the discrete form representation? This section discusses how biochemical function of the molecule is determined by cybernetic information principles.
Expression of insulin code matrix 1AI0
The matrix mechanism of insulin, evolution of biomacromolecules and, especially, biochemical evolution of the insulin language have been analyzed by the application of cybernetic methods, information theory, and system theory, respectively. The primary structure of a molecule of insulin is an expression of the exact specification of its atomic composition and the chemical bonds between those atoms.
R6 insulin hexamer (d1ai02)
The structure 1AI0 has a total of 12 chains. Of these, two are sequence-unique identical chains of BFHJL ().
Number of atoms in insulin B chain.
This positioning is of key importance for understanding the programmatic, cybernetic, and information elements of this protein. The scientific key for interpretation of biochemical processes is the same for insulin as for other proteins and sequences in biochemistry. The first amino acid in this example has 23 atoms, the second 19, the third 17, etc. They have exactly these numbers of atoms because there are many codes in the insulin molecules, analog codes, and other coded features. In fact, there is a cybernetic algorithm in which it is “recorded” that the first amino acid has to have 23 atoms, the second 19, the third 17, etc. The first amino acid has its own biochemistry, as does the second and the third, etc. The obvious conclusion is that there is a concrete relationship between quantitative ratios in the process of transfer of genetic information and qualitative appearance, ie, the characteristics of the organism.
Bio insulin code
The bio insulin code is an area of biomacromolecular processes in biochemistry (chemical engineering, bioprocess engineering, information technology, biorobotics) that treats signals as stochastic processes, dealing with their biosignal properties (eg, frequencies, mean, and covariance). In this context, biocodes are modeled as functions consisting of both deterministic and stochastic components. A simple example and also a common model of many bio systems is a signal Bio (t) that consists of a deterministic part x (t) as a white biocode ().
Bio (t) = [X1,2,3,n(t) – X1,2,3,n(t)] X (t) = Biocode 1,2,3,n
(A,B,C) – (B,C,D) = biocode 1
(B,C,D – C,D,E) = biocode 2
(D,E,F – E,F,G) = biocode 3 (1) etc.
where A, B, C, and n represent the connection of group amino acid positions, 1,2,3,n
Thus, the union of these amino acids generates the number 29895, which is a code representing one of the quantitative characteristics of the given genetic information. In a similar way we can calculate biocodes for other groups of amino acids, which are connected by various biocodes and analog codes, as well as other quantitative features. Connection is one of the numerical expressions that connect various corresponding features in biochemistry. It has a very prominent place in our mathematical understanding of all processes in biochemistry. This is a recently discovered phenomenon, the role and significance of which will hopefully be clarified in the future. Those bioprocesses are well correlated, and the autocorrelation function is a biocode:
Bio I = (X1,2,3,n(t) – X1,2,3,n(t)) = [(−)Y ↔ (+)Y]
[(−)Y = (+)Y] (2)
where Y represents the result as a functional biocode.
Insulin B chain
Mathematical evidence is provided here to prove that in the biochemistry of insulin there really is a programmatic and cybernetic algorithm in which it is “recorded”, in the language of mathematics, how the molecule will be built and what will be the quantitative characteristics of the given genetic information.
Bio(t) = X1,2,3,n(t) – X1,2,3,n(t)
X(t) = biocodes 1,2,3,n
Bio 1 = (A,B,C) – (B,C,D) = biocodes 1
Examples are presented in .
The biocodes presented in are calculated using the relationship between corresponding groups of amino acids. These are groups with different numbers of amino acids. There are different ways and methods of selecting these groups of amino acids, and it is hoped that science will soon determine which method is most efficient. Some biocodes have a positive numeric value and some have a negative one. gives these codes (see also ).
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 (more ...)
The formula for calculating of biocodes.