Download Pharmacy Research Day Poster (1)_04222014

Experimental Evidence for the Existence of the Cell Force and Cell Metabolic Field
Introduction
The foundation of physics relies on the theory of fundamental forces that explain physical
phenomena observed in the universe. Physicists have developed a framework of mathematical
equations to describe these forces and their underlying fields at work in all natural processes.
We propose the existence of similar concepts in the biological sciences, referred to here as the
“cell force” [1] and “cell metabolic field” [2], based on experimental evidence obtained from
analysis of human cancer cells. By developing a mathematical and theoretical framework for
cell biology and pharmacology, we aim to further our understanding of biological processes
and the deviations from these processes that result in disease.
Fundamental Forces of Nature
Gravitational force
Gravitons
Electromagnetic force
Photons
Strong force
Gluons
Weak force
W+, W-, and Z bosons
‘Cell force’ [1]
‘Cytons’ p[1]
Figure 1. The fundamental forces of nature and their mediators. The proposed cell force is also added,
that is postulated to control cellular enzymes that catalyze chemical reactions and gene expressions.
Figure 2. The atom-cell isomorphism postulate
(ACIP). Two types of particles constitute the atom and
the living cell. Hadrons are heavy particles such as
protons and neutrons, and leptons are light particles
including electrons and muons. Cytons, first invoked in
[1] are the hypothetical physical entity operating inside
the cell (analogous to bosons in physics) that mediate
the interactions between equilibrons and dissipatons.
Equilibrons are stable under normal conditions (e.g.,
DNA sequences), while dissipatons are unstable (e.g.,
membrane potentials), requiring continuous dissipation
of free energy to be maintained.
David Yao and Sungchul Ji*
Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy
Rutgers University, Piscataway, N.J.
*[email protected]
Using mRNA measurements before and after doxorubicin treatment, we constructed frequency histograms showing the distribution of mRNA
levels for each protein family. These frequency histograms were divided into bin sizes such that the entire range of mRNA concentrations
observed were partitioned into 50-60 bins of equal size. We then modeled each of these frequency histograms with the so-called Blackbody
Radiation-Like Equation (BRE) which is given below:
BRE:
where a, b, A, and B represent specific numerical constants [5]. The BRE was previously found to fit the single-molecule turnover times of
cholesterol oxidase enzymes suggesting that the Gibbs free energy levels of enzymes are quantized [5]. Our application of the BRE to mRNA
data from human cancer cells was based on the hypothesis that this energy quantization would be observable in all enzymes in living cells,
including human breast cancer cells.
100
BRE
60
40
20
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
-20
120
100
80
Frequency
BRE
60
40
20
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
-20
After our initial analysis, we noticed that we could obtain an optimal BRE fit to the mRNA data with multiple sets of parameter values.
To further investigate this finding, we calculated parameter ratios for each BRE fit to see whether these ratios changed along with the
parameter values. Interestingly, the parameter ratios stayed constant even as the parameter values themselves changed. This phenomena
remained true for every protein family analyzed before and after doxorubicin treatment.
Biopsy
Normal Tissue (N)
Tumor Tissue Before
Drug Treatment (BE)
Tumor Tissue After
Drug Treatment (AF)
Figure 4. The preparation of the 3 samples for mRNA measurements.
We selected ten
protein families of
interest to analyze
(Table 1). Each
protein family was
selected on the criteria
of having at least 50
open reading frames
(ORFs) included in
the microarray data.
Table 1. Protein families studied and their associated cellular functions.
Protein Family
Cellular Function
Cluster of Differentiation (CD)
Cell Signaling & Adhesion
Kinase-Binding Protein (CGI)
Telomere Uncapping & Elongation
Electron-Transferring Flavoprotein (ETF)
Fatty Acid Oxidation
Heat Shock Protein (HSP)
Stress Response
Interferons (INFR)
Immune System Activation
Integrins (IPA)
Signal Transduction
Unknown Proteins (KIAA)
Unknown
Mitogen-Activated Protein Kinase
Cell Proliferation & Survival
Sterol Carrier Protein (SCP)
Fatty Acid Oxidation
Zinc Finger Protein (ZFP)
DNA Transcription
Figure 8. Averaging
leads to loss of
information, as shown
when the averaging of
individual pixel colors in
the photo on the left
results in a blank gray
photo on the right when
these colors are lost.
120
Figure 5. The fitting to BRE of the CGI mRNA levles before (top) and after (bottom) doxorubicin treatment and associated
parameter values and ratios. The BRE fitting of the data was performed by the Solver program in Excel.
The microarray data for 4,740 genes from 20 human breast cancer patients measured by
Perou et al. [3] were analyzed in this study [4]. mRNA transcript levels were measured with
microarrays in three different samples -- normal (N) human breast tissue culture, and the
tumor biopsies taken before(BE) and after (AF) doxorubicin treatment:
Patient X
To explain the inconsistency in statistical significance of parameter ratios before and
after doxorubicin treatment within the same protein family, we turn to the principle that
averaging leads to loss of information. Because individual patients have varying
responses to chemotherapy [4], reflected in the dramatic variation of mRNA
concentrations before and after doxorubicin treatment, these responses may in effect be
“averaged” when looking at an aggregate analysis of patients as was done in our
experiment. Therefore, the observed statistical insignificance may not be an indicator of
actual insignificance, but rather the loss of individual patient information as a result of
averaging the results over all 20 patient data sets. Further analysis of BRE parameter
ratios for individual patient data sets may lead to a better characterization of changes in
individual mRNA concentrations due to chemotherapy.
After modeling the frequency distributions of each protein family with BRE, we obtained specific parameter values (a, b, A, B) for each
family before and after doxorubicin treatment:
Frequency
Methods
Discussion & Conclusions
Data, Analysis & Results
80
Figure 3. A more detailed network representation of the
atom-cell isomorphism postulate (ACIP). The claim of
ACIP that the structures and functions of the atom and the
cell share a common set of principles and features thought
to be reflected in the symmetry between the topologies of
the two networks: Although the labels of the nodes and
edges are different, the two networks are topologically
identical. It is interesting to note that, since Mattergy and
Ergons are synonymous, the mattergy tree (i.e., atomic
physics) is enfolded in the gnergy tree (i.e., cell biology)
which makes the topology self-similar or recursive.
Fitting of mRNA data to BRE in Figure 3 supports ACIP.
We also conducted statistical analyses to test whether the differences in parameter
ratios between different protein families before and after doxorubicin treatment were
significant. This analysis produced mixed results: between protein families, the
differences in parameter ratios were large enough to be statistically significant. However,
comparing the parameter ratios before and after treatment within protein families yielded
less clear results: the differences were only significant in 50% (5 out of 10) of the protein
families studied, perhaps indicating that not all pathways contribute equally to tumor
formation (Figure 6).
Figure 6. Statistical analysis for parameter ratios obtained from BRE analysis of the CGI, MAPK, ZFP, CD, and ETF protein families before and after
doxorubicin treatment.
Figure 7. Demonstration
of parameter ratio
invariance vs. parameter
value variance. Figures
provided from CGI
protein family analysis.
The discovery of set parameter values within the BRE models of mRNA levels supports
the notion that the Gibbs free energy levels of enzymes are quantized [5], similar to the
energy quantization of electrons in atoms as demanded by the blackbody radiation
equation discovered by Max Planck in 1900. Since mRNA leveles are determined by
cellular enzyme activity, the BRE parameters that model mRNA levels indicate specific
Gibbs free energy levels that enzymes operate at, consistent with the conformon
hypothesis of enzyme catalysis [1, 5].
The invariance of BRE parameter ratios suggests another interesting characteristic of
these energy levels – namely, that the difference between these levels is invariant of their
absolute value. This characteristic, known as a gauge invariance in physics, is analogous
to the earth’s gravitational field, whereby the same amount of energy is needed to lift an
object by a certain distance, regardless of the object’s starting position relative to a given
framework of measurement. In this way, we can compare the gauge invariance of the
gravitational field to that of the cell metabolic field. The corresponding force needed to
achieve energy level transitions, termed gravitational force in describing objects falling in
the gravitational field, would be the cell force, our proposed term to describe the Gibbs
free energy changes necessary to maintain homeostasis in the cell. By further
investigating and characterizing the cell force through mathematical analysis, we can
perhaps better understand the disturbances in this force that result in catastrophic diseases
such as cancer.
References
[1] Ji, S. (1991). Manifolds, Fractals, Fiber Bundles, and Gauage fields: The Role of Genetic Information
in the Theory of the Cell Force. Molecular Theories of Cell Life and Death. Rutgers University Press,
New Brunswick. Pp. 90-122. PDF available at conformon.net under Publications <Proceedings and
Abstract.
[2] Smith, H. A. and Welch, G. R. (1991). Cytosociology: A Field-Theoretiic View of Cell Metabolism.
In: Molecular Theories of Cell Life and Death (Ji, S., ed.), Rutgers University Press, New Brunswick.
Pp. 282-323. PDF availabel at conformon.net under Liks < Biology.
[3] Perou, C. M., Sorlie, T., Eisen, M. B., et al. (2000). Molecular portraits of human breast Tumors,
Nature 406(6797): 747-52.
[4] Ji, S. , Cheng, L., Szafran, W. and Carmona, R. (2012). Proceedings of the 2012 IEEE International
Conference on Bioinformatics and Biomedicine. Poster #180.
[5] Ji, S. (2012). Molecular Theory of the Living Cell: Concepts, Molecular Mechanisms, and Biomedical
Applications, Springer, New York. See Sections 11 and 12. PDF available at conformon.net under
Publications < Book chapters.
Acknowledgment
I gratefully acknowledge the guidance of Dr. Sungchul Ji and
contributions of other students in the Theoretical and Computational
Cell Biology Lab at the Ernest Mario School of Pharmacy at Rutgers
University toward developing the methods described in this poster.
Special acknowledgment goes to Kenneth So and Larry Cheng for
first describing the method of fitting the BRE equation to mRNA
concentrations measured in human breast cancer cells.