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molecules encoded by genes, less than m in number due to alternative splicing
(Myer and Vilardell 2009; Will and L˝hrmann 2006), and the columns represent
n metabolic pathways ( n numbering around 200 in the budding yeast cell). The
interior of the matrix contains the node numbers, N i , where an RNA molecule is
located in a given ribonic spectrum, where N i is the ith node in the r
r principal
grid (see Sect. 12.8.1 ) with r ranging from 5 to 20. In Table 12.6 ,r
¼
10 and
¼
N
100.
The following features of the “ribonic matrix” are noteworthy:
1. If a given RNA molecule participates in more than one metabolic pathway, the
number appearing in the last column of the ribonic table will be greater than 1.
2. The number of different RNA molecules (i.e., with different ORF's)
participating in a given metabolic pathway appears in the last row of the table.
3. Of the total of more than 6,000 RNA molecules, about 4,000 RNA molecules
have known functions which number about 200. Hence the average number of
RNAmolecules supporting one function or one pathway is a ¼
20.
4. Although the entities on the horizontal and the vertical margins of the table are
independent of experimental perturbations Y, the node numbers in the interior
(yellow shading) of the table are sensitively dependent on Y, which would make
the ribonic matrix a useful tool for characterizing cell states, both normal and
diseased (Chaps. 18 , 19 ).
5. Based on the frequency of occurrence of each node in the interior of the ribonic
matrix, a histogram can be generated by plotting the frequency of a node
occurrence (which is equal to the number of RNA molecules occupying
that node) as a function of node numbers. Such a histogram will be referred to
as the “total ribonic spectrum” (TRS) of known RNAs. The “total ribonic
spectrum” of unknown RNAs is given in Panel h in Fig. 12.12 . By comparing
these two kinds of “total” ribonic spectra generated with a variety of different
ViDaExpert parameters (e.g., different stretching and bending coefficients and
principal grid sizes), it may be possible to identify the biological functions of
unknown RNAs (see Chaps. 18 and 19 ) .
4,000/200
¼
12.9 Structural Genes as Regulators of Their
Own Transcripts
Completing the sequencing of the human genome in 2003 was not the end (as many
might have thought) but only the beginning of our long journey toward understand-
ing the functioning of the genome and hence the living cell on the molecular level.
From a nonequilibrium thermodynamics perspective (Prigogine 1977, 1980;
Kondepudi and Prigogine 1998; Kondepudi 2008), we can readily identify the
nucleotide sequences of the human genome as equilibrium structures or equilibrons
and their biological functions as dissipative structures or dissipatons (see Sect. 3.1 ).
Functions are dissipatons because functions imply processes and processes entail the
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