Biology Reference
In-Depth Information
living cell. In retrospect, it is not surprising that it took over two decades for the cell
force concept to be tested against experimental data, because the relevant whole-
cell metabolic kinetic data did not become available until 2004 when Garcia-
Martinez et al. (2004) measured the time-dependent changes in the genome-wide
RNA levels and transcription rates in budding yeast (Sect. 12.2), and it was not until
2009 when the fitting of the whole-cell RNA kinetic data to BRE was discovered (Ji
and So 2009). Realizing the connection between the fitting of the genome-wide
RNA kinetic data to BRE and the
cell force
concept invoked two decades earlier
was not immediately obvious and took another couple of years to occur as a result
of writing this topic. The recognition of the cell force-BRE connection was made
possible by a qualitative application to cell biology of the renormalization group
theory described in Huang (2007) (see below).
In addition to the yeast RNA kinetic data, BRE was found to fit (after
renormalizing the four parameters, a, b, A, and B) the data from single-molecule
enzymic catalysis, and protein folding as summarized in Table
12.10A
.
It is interesting to note that the numerical values of the four parameters of BRE,
i.e., a, b, A, and B, increase in the following order as indicated by the a/b ratios:
Blackbody radiation
<
Single-molecule enzymology
<
Transcription
<
RNA level control
<
Protein stability
(12.37a)
The order revealed in Inequality (12.37a) appears to coincide with the
energy
scales
(and hence
distance scales
) characterizing each process, since the interaction
energies are expected to decrease in the same order, i.e., from electronic energy
levels involved in blackbody radiation (i.e., covalent bond energies of ~100 kcal/
mole) to non-covalent energies involved in protein folding (1-5 kcal/mole).
When the logarithms of the a and b values of the five processes are plotted in
what may be called the “BRE parameter space,” a continuous nonlinear trajectory is
obtained that passes through Processes 1, 2, and 5, with Processes 3 and 4 deviating
from it (Fig.
12.26a
).
The nonlinear trajectory shown in Fig.
12.26a
is reminiscent of the
renormalization
group (RG) trajectory
discussed in physics, i.e., in quantum electrodynamics (QED)
and quantum chromodynamics (QCD) (Huang 2007). The concept of renormalization
was discussed in Sect. 2.4 in connection with bionetworks (e.g., metabolic pathways)
viewed as
renormalizable networks
. A more detailed characterization of the concept
of renormalization is given by Huang (2007, pp. 217-225):
...
Renormalizability is not just a property of QED, but of all successful theories in physics.
The important point is that a renormalizable theory describes phenomena at a particular
length scale (
e.g., nuclear, atomic, molecular, cellular, etc.; my addition
), in terms of
parameters that can be measured at that scale. . . . .For example, we can explain the
everyday world using thermodynamics, without invoking atoms. Properties such as specific
heat and thermal conductivity, which really originate from atomic structure, can be treated
as empirical parameters. At a smaller length scale atoms appear, and they can be described
by treating the nucleus as a point. Similarly, at the scale of nuclear structure we do not need
quarks (
i.e., protons and neutrons are sufficient; my addition
). Renormalizability is a
closure property that makes physics possible. We would not be able to understand the
world, if we had to understand every minute detail all at once.
(12.37b)
