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Following article has been revised from the manuscript in Japanese that will be published from National Institute of Advanced Industrial Science and Technology (AIST) in this summer 2004.

The holistic diagnosis and possible therapies to promote physical and mental health Bio­Complex system Research Institute Wasei MiyazakiRecently, diagnosis and treatment of the human being in holistic and

non­linear ways are more and more actively being tried, through the application of chaos analysis of brain waves (EEG) and electrocardiograms(ECG) for example, or by using stochastic resonance technology. However,except in the fields of neurological science, physiology, biophotonics [1] and so called, system biology [2,3], we are still just beginning to gain a mutual understanding between the current Newton’s reductive science and the new quantum or chaos­complexity science with respect to biological phenomena and the utilization of medical technology.

In this article I would like to report that we are about to open the door to a mutual deployment of both reductive and holistic sciences by using such methods, as we analyze the symmetry and other changes in chaos indicators obtained from attractors of time series data of vital signals, especially finger pulses. The second goal of this section is to show how these reductive and holistic sciences are integrated in the principle of life by showing how phenomena occurring at the atomic and molecular cluster levels are reflected in phenomena that occur at the cellular level and in the whole body as a complete entity. I wish that these medical technologies, by which we understand thehuman being as a complex system, be utilized in mutually compensatingways with current biochemical and molecular biological technologies, in diagnosis and treatment. I also wish to let it be known that, from the above mentioned aspect of biological phenomena, chemistry in biomedical science,or physics such as quantum theory, show very different and mysterious aspects from currently­held views, in some cases. It will be an unexpected honor for me if these new viewpoints of life bring some hints or suggestions to the industrial technologies that are now trying to mimic the vital system and also to computational science that supports these technologies. For those interested in various fields of science, in this section I will

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mention, at first briefly, what we found from analysis of the vital signals. Next, I will explain how we reached our conclusion that the vital signal reflects the oscillatory dynamism of the molecular clusters and cellular functions. At the same time I will present my reasoning that the chemical and physical states are a reflection of the dynamism of quantum states in information processing systems of life. Following a general consideration of these phenomena, I want to consider how these processes are integrated intolife from the level of the quantum state up to the level of the whole body. From this standpoint I will also argue how this technology relates to other non­linear and holistic standpoints contained, at least in part, in other technologies. These other technologies harness, at certain levels, holistic and non­linear approaches to understanding the human life and mind, that are useful to us in diagnosis, treatment or health maintenance. These holistic view technologies, at least in part, seem widely distributed in the various scales of information processing, from atomic and molecular levels to DNA, protein, cell and whole body levels. Examples of these technologies are the so­called Biophotonics, the study of biophoton measurement generated by bioactivities; Genomics, the basis of genetic diagnosis and gene therapy; Proteomics, the discipline concerned with protein function and ways to make proteins work for us; Bioinformatics, that brings support to othertechnological fields by concentrating on information storage and processing and Stochastic Resonance technology. It can be said that each of the alternative and complementary medicines, such as oriental medicine, homeopathy, thalassotherapy, aromatherapy, music therapy etc. are also individual technologies, from this standpoint. Although it can be thought that all data of biological phenomena, at levels ranging from molecule to whole body, demonstrate the relationship “the part and the whole” of systems, I think this relationship is very difficult to explain in terms of the current biochemistry and physics. It is, however, possible to explain using the theory of chaos­complexity although the data is based upon biochemical phenomena, so I recommend to you the reading of this article as the real view of substances in chaos­complex systems. Even so, I feel afraid, in view of the range and specialism of each of the fields of study, that there may be shortcomings in my explanation of the relationships between biological phenomena and physical phenomena by using signal processing analysis and pattern analysis. One of the reasons is the limitation of the number of

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pages and another is the deficiency in my understanding over all specialist fields of study with which this article is concerned. Nevertheless, my main purpose in this article is to elucidate the common basis upon which it ispossible to discuss substance levels with readers from all disciplines, from biology to quantum mechanics, so I would appreciate your generosity in bearing with any tedious explanations that you may find in this article.

1. What could I find by finger pulse analysis? It is well known that we can observe many biochemical or physical oscillations, including fluctuations in biological phenomena. These oscillations, for example, in glycolysis, some enzymes, cellular cyclic AMPand calcium ions etc. are attributed to the chaos fluctuation in signal transduction pathways. Such chaos fluctuations are caused, in a non­equivalent state, by non­linear feedback or cross­talk pathways, the so­called interference effects [4]. Mandell et al. insist that such fluctuating systems in a non­equivalent state can be defined as life and the adaptation ability of life can be measured by chaotic fluctuation [5,6]. They gave the following examples in which there are some differences of chaos fluctuations in a normal, healthy state and an abnormal, ill state [5]. 1) Fluctuation in change of cell number with time in haematological

disorders2) Stimulant drug­induced abnormalities in patterns of the behaviour of

brain enzymes, receptors, and animal explorations of space 3) Cardiac interbeat interval patterns in a variety of cardiac disorders4) The resting record in a variety of signal sensitive biological systems

following desensitization5) Experimental epilepsy6) Hormone release patterns correlated with the spontaneous mutation of a

neuroendocrine cell to a neoplastic tumor7) Brain wave patterns useful for the prediction of the rejection of heart

transplants8) Brain wave patterns under neurodegenerative disorders9) Neuroendocrine, cardiac, and electroencephalographic changes with

aging10)Electrocardiographic patterns at the time of imminent ventricular

fibrillation

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These fluctuation processes are signal transduction pathways of the biological functions, the so­called neuro­immuno­hormonal systems. Entire regulatory functions of life are included in these pathways and the data shows that the oscillatory character or patterns of the systems, at cellular and whole body levels, reflect a soundness of those systems. However, it is unclear as to what extent these phenomena reflect entire life phenomena and also what could be the physiological, biochemical, molecular biological, chemical and physical bases. A more serious observation is that, having made such recognition of the nature of life processes, the therapies that are given on the basis of current medical technologies do not take account of this nature and so, are far from being the mutually compensated utilization ofboth reductive and holistic sciences.

Why does such information of oscillating vital signals (finger pulse) reflect the soundness of the systems and what does it essentially mean? I will discuss at first, in this and the next section, the results of finger pulse analysis and the dynamism of the molecular clusters and cellular functions. Then, in Section 3, I will argue the essential meaning of these phenomena.

The holistic images of a non­linearity in neural systems and chaos analysis of finger pulses are already reported in many articles [7­8] and we usedmethods that are similar to those used by Tsuda et al. [8] for finger pulse measurement and attractor construction. Briefly, we embedded time series signals of finger pulses in 4 dimensions using Takens’s embedding theory in order to obtain numerical values of the attractor’s complexity in the dynamic system. We calculated the Lyapunov exponent ( λ ) and Shannon’s information entropy (E) from these attractors[8­9]. In Lyapunov exponents, we called the largest one: largest Lyapunov exponent (λ1) . This is a numerical indicator by which the individual trajectory tends towardsconvergence or divergence with the lapse of time, and E is an indicator of the disorder of each trajectory’s distributions. They are shown in the following equations.

The right side of equation (1) shows the extent of the adjacent two trajectories diverging with the lapse of time, and Pi in equation (2) shows the

)1(         )|)()(|

ln(1lim0,

tttt

xx

)2(        log1

i

N

ii PPE

5

existence probability of the trajectories at a cross section of the attractor. E is the sum total of this probability along the whole attractor. The Entropy term used in thermodynamics is the quantity by which the Bolzmann constant, K, is multiplied. By using these indicators, we compared the finger pulse measurements, taken over 1­3 minutes, of approximately 40 diseased patients (having various diseases) and almost the same number of normal subjects. The classification method of patients and normal subjects is the difference of the normalized values obtained by dividing the absolute values of λ1 and E by each maximum value as shown in the next equation. From this equation we obtain Mirror Values [9].

I have shown the comparison between normal subjects and patients using these Mirror Values in Fig. 1.

ミラーバリュー(1)

-0.1-0.08-0.06-0.04-0.02

00.020.040.060.080.1

Norm

al

Term

inal

Canc

er

Depr

essio

n

Schi

zoph

reni

a

Asth

ma

Ulce

rativ

eCo

litis

図 5.3­1

ミラ ー バ リ ュー 　（１）Mirror  Value (1)

Fig. 1

Fig. 1 Mirror Value (1)

We can find differences in the mean values of the patients and healthysubjects in Figure 1 but, as can be seen in the figure, the differences are not significant statistically due to the uneven numbers of individuals in each patient group and due to the small overall case numbers. I have shown in Fig. 2 the extent of the data inconsistency of the individual subjects within the normal subjects, depression patients and schizophrenia patients. In this figure the vertical lines show Mirror Values that were obtained using

Mirror Value =  1max1 ­ EE max                     (3)

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equation (3), from λ1 and E. In this case we took one­minute finger pulse measurements, 2~3 times successively, per subject, and drew the 4 dimensional attractor using Takens embedding theory. From these attractors, we calculated λ1 and E using equations (1) and (2). The horizontal bars indicate the results obtained for each subject and, as stated above, depending on the subject, some measurements were repeated 2 or 3 times. Therefore, the results (one, two or three) obtained for the same subject are grouped together and are separated from the results of another subject by a space. The figure shows that Mirror Values are distributed broadly in the plus area in the cases of normal subjects, except in the case of one athlete heart normal subject but, with respect to depression and schizophrenia patients, Mirror Values are distributed broadly in the minus area. In the cases of two depression patients that were observed for a few months, values for one of these patients moved to the plus area, heralding aclinical improvement. However, values for the other patient did not move into the plus area, indicating that there was no clinical improvement over the observation period.

-1.5

-1

-0.5

0

0.5

1

1.5

1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153

-1.5

-1

-0.5

0

0.5

1

1.5

1 9 17 25 33 41 49 57 65 73 81 89 97 105 113

-1.5

-1

-0.5

0

0.5

1

1.5

AK1

AK2

IS1

IS2

SM1

SM2

TK1

1_13

1_14

1_15

2_16

2_17

2_18

3_19

3_20

3_21

　　　　　健 常 者：　ほ とんどプラスの 領 域（スポ ー ツ心健 常 者：　ほ とんどプラスの 領 域（スポ ー ツ心臓 者 のみマ イナ ス領 域）臓 者 のみマ イナ ス領 域）うつ 病：マ イナ ス 領域うつ 病：マ イナ ス 領域 が 多いが 多い分 裂 病：マイ ナス 領域 が多い分 裂 病：マイ ナス 領域 が多い

ミラーバリュー（２）

　健常 者うつ病

分 裂病

図5.3­2

Mirror  Value (2)

NormalDepression

Schizophrenia

Fig. 2

Normal: almost plus area (except one athlete’s heart patient)

Depression: most of minus area

Schizophrenia: most of minus area

Fig. 2 Mirror Value (2)

Next, we calculated fractal dimension of these subject’s time series data of finger pulses. Fractal dimension is used as the indicator of geometric complexity. Vital signals are expected to show multi­fractal character, so

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we calculated 2 fractal dimensions using Higuchi’s method [10]. Briefly, the

calculation was made as follows,

In equation (4), if the moving distance ?t follows to the power law of t for a certain time scale t, we call this power D, the Higuchi’s fractal dimension. In the case of different power laws depending on the time scales, we obtain D1 and D2 as short and long time scales respectively. Thus we get,

F­Constant = (D2 + D1)/ (D2 – D1) (6)

Using this F­Constant, I have shown the comparison of normal subjects with various disease patients in Fig. 3. As shown clearly in the figure, F­constants are lower in depression, schizophrenia, Alzheimer and glycogenosis patients compared to normal subjects.

3.00

3.20

3.40

3.60

3.80

4.00

4.20

4.40

4.60

4.80

5.00

F-c

onst

=(D

2+D

1)/(

D2-

D1)

F constant

健 常 者 うつ病G1,2　 分 裂 病 ア ル ツハ イマ ー

糖 原 症 病 名不 明

図 5.3­3

4.73

4.06 4.04

3.703.85

3.70

4.31

Fig. 3

Normal Depression G1,2

Schizoph­renia

Depression G1,G2

Alzhei­mer

Glyco­genosis

Unide­ntifed

Next, I have tried to distinguish between the normal, depression and

)4(          )( Dttt

)5(            |))1(()(|1

)(/)(

1

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i

tk tikXitkX

ML

t

8

schizophrenia patients using λ1 and E obtained from equation (2) and (3). I have shown the results in Fig. 4 in which the vertical axis represents the weighted λ1 [9] and the horizontal axis represents E. Each point within this figure represents one subject and a diamond shape signifies a normal subject, a square signifies a depression patient, and a triangle, a schizophrenia patient. The majority of results obtained for normal subjects fall into the upper circle in this figure while those for the mentally ill patients such as the depression or schizophrenia patients are almost all contained within the lower circle. However, amongst the total of 77 subjects, 9 of the normal subjects are categorized as having depression and 3 of the depression patients are categorized as being normal, so it can be said that this method distinguished the normal and mentally ill patients with 83 % accuracy. Nevertheless, when I combined these results with F­constant data, the 9 normal subjects wrongly categorized as having depression returned to the normal category with respect to their F­constant data and the 3 depression patients wrongly categorized as normal subjects returned to the depression category. Therefore, the accuracy of the method in distinguishing between normal subjects and mentally ill patients improved to 97 % by subsequently incorporating this further analytical method. Also, we found that the capacity to accurately distinguish between these two categories improved to 100 % by using the neural net algorithm.

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　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　＜ λ 1＞ ：平 均重 み付 け をした値

　　　　　　　　　　　　　　　　　　　　＜E＞ ： ３点 の平 均値

　　　　　　　　　　　９人の 健常 者 が 下の枠 内 に 入り、３人の うつ病 患 者が 上の 枠内 に 入 っている

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　図　５．３－４図　５．３－４　　

　　　　　　　　　　　　　　　　

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3.4 3 .6 3.8 4 .0 4.2 4.4 4.6 4.8 5 .0 5.2 5.4 5.6 5.8Entropy <E>

We

ight

ed

<λ1

Normal DP SP

EE－ λ １－ λ １ による健常 者と精神 疾患 患 者 の 識 別による健常 者と精神 疾患 患 者 の 識 別Classifications of normal and psychosis by λ 1 and E

Mean weighing valuesMean of 3 points

9 points of normal are in below  and 3 points of DP are in  upper area

Fig.4

Fig. 4 Classifications of normal subjects and psychosis patients byλ1and E

From these results we can expect that, if we can gain an understanding of how the complete patterns of finger pulses are integrated from each individual waveform, this will pave the way to an understanding of how whole body systems are integrated from each of their constituent working elements. This may also mean that the soundness of the element distribution or order in a system can be estimated from the vital signal’s time series patterns within that system. In other words the relationship “thepart and the whole” with respect to time is intimately connected with the relationship with respect to space. This may give insight into the question“Why does such a thing happen in life?” I will mention in the next section.

2. The relationship between the events that take place in a part of the system and the oscillation patterns of the whole system There are many ways to stimulate or affect a system from the outside, such as environmental changes, physical, chemical and electric stimulations, sensuous and psychological stimulations etc. I will describe, in this section,how a system can respond to these stimulations or changes and how we can

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measure the responses of the system. We have estimated, so far, the effects of drugs and functional food intake stimulations, environmental changes such as temperature change, thalassotherapy and music therapy by using finger pulses chaos analysis. Responses to these stimulants have been estimated using λ1 ,E and F­Constant in recent years. In this article, I will focus on drug intake stimulations from which we have been able to observe the system’s dynamism of molecular and cellular states. We cananalyze the effects of drugs and functional food intake in almost the same manner but here I want to focus on the effects of a particular molecule, MX­1,also called K­76COOH, which I had been studying for many years.

K­76 is a small molecule, classified as a sesquiterpene, which is produced by a certain fungus, apparently in order to regulate its own sporulation. MX­1 (K­76COOH) is a compound of molecular weight 440, which is made by oxidizing one of the aldehydic groups in K­76 to produce a chemically stable carboxylic acid [11­13]. MX­1 shows many biological actions and effects on animal disease models and human subjects [14­20]. It elicits responses in awide variety of animal models and clinical tests such as those for diabetes, various cancers, influenza infections and circadian rhythms [15,19]. Moreover, this agent shows various biological actions such as the regulation of gene expression, immune function and hormonal action in vitro and in vivo over the very wide concentration range of 310 ～ 1310 M (0.44 mg/ml～44

pg/ml) [15,20]. Both in vivo and in vitro actions do not reveal ordinal linear dose­response effects like many other drugs, but show characteristic administration­interval­dependent effects towards each disease [16,18,19]. Therefore, it may be said that MX­1 mainly acts with respect to the information processing systems of life. However, I did not understand this clearly until I was able to understand the essential features of the solution state of MX­1, its mode of action towards molecular clusters and cellular functions, and also the influence of MX­1 on vital signals, which I will nowdescribe:

(1) The existence state of MX­1 in solutionLife may be described as a non­equilibrium open system. On the other

hand, in test tube experiments, it is desirable to have a closed system at equilibrium, or similar such environment. However, MX­1 does not exhibitthe ideal diluted solution state, even when present in physiological saline. I

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think many biomolecules, such as proteins, DNA and lipids etc. display similar characteristics to MX­1, and believe that, in many in vivoexperiments, one of the reasons we do not get the results we see in in vitroexperiments, may be attributed to this sort of solution structure. Moreover I think the main reason is that the weak interactions of bio­molecules, which support the bio­system in holistic and non­linear ways, almost do not operate in the circumstances of a diluted ideal solution. MX­1 shows a slightly variable fluorescent absorption spectrum, depending upon the buffer that is used as solvent and depending on the extent to which it dissolves in the buffer. In the absorption spectra, changes are seen in the position and height of the peaks at 260~280 nm, attributed to MX­1’s benzene ring πelectrons, and at approximately 340 nm, attributed to aldehydic group on the benzene ring , both depending on the MX­1 concentration and the variety of buffer (Fig. 5) [19]. For example, in the case of dissolving MX­1 in the cell culture medium RPMI­1640, the position and the height of the peak attributed to the aldehydic group gradually moves to shorter wavelengthwith increasing time, shortly after the molecule begins to dissolve (Fig. 5). These kinds of spectral changes occur in the mixing of MX­1 with a basic amino acid such as arginine or lysine which contain an ε­amino group but do not occur when the amino acid does not contain anε­amino group, nor do they occur when using MX­1 derivatives in which the aldehydic group is not present. Briefly, as limited pages do not permit a more thorough explanation, this interaction is the weak interaction betweenε­amino group and the aldehydic group of MX­1, which can be accounted for by the tunneling effect of the electrons[21]. Practically, the very weak interaction, which is lost even in dialysis, becomes very strong upon formation of Schiffbases, using a reductive reaction [22]. Many in vitro MX­1 actions change clearly in their patterns upon adding a basic amino acid such as arginine or lysine, so it can be said that this is the essence of MX­1 activity [19].

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400 350 300 250

MX­1  100

μg/ml  in  RPMI­1640

0 m in

25 m in

45 m in

図 5.3-5

Fluorescence Absorbance

Fig. 5

Fig. 5 Fluorescent absorption spectrum of MX­1

It is well known that basic amino acid motifs and amphiphilic motifs (BAA motifs), in which basic and hydrophobic amino acids alternately range, are important components of the signaling pathways of life. In particular, BAA motifs undertake the role of cross­talk point between the different signaling pathways and interact with calmodulin, protein kinase and acidic lipid. We investigated the interaction of MX­1 and one BAA motif using NMR, and found this interaction continued to change for more than 10 days [23]. MX­1 seems to have the appropriate solution state characteristics to interact weakly with signaling molecules and I think that many bio­molecules andinterfering molecules having such characteristics may operate in a similarway in other mechanisms. For instance, I chose two agents and found that these agents both took a long time to reach equilibrium in physiological conditions [19]. What happens if I add the weakly interacting agent to non­equilibrium signaling pathways containing water molecules and bio­molecules? I will mention in the next section.

(2) Patterns of molecular gathering (cluster) The processes of RNA and protein production from DNA occupy prime

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positions over all the signaling pathways of life and one of these processes can be easily recreated in a cell­free system by using DNA fragments and transcription factors (TFs) [14,19]. This is attained by the reaction of commercially available oligonucleotides (DNA fragments), containing the binding region of TFs, and cellular extract containing TFs. We investigated the amount of cluster formation of DNA­TF by adding a weakly interacting agent like MX­1, and then visualized the result using radioautography in which we exposed X­ray films after performing electrophoresis in acrylamide gel. In this experiment, the more clusters that are generated the more RNA and protein is formed. This popular method is called a “gel shift assay” in that it can separately quantify the generated clusters by using the principle that a large cluster moves faster than a small cluster in an electrophoresis gel column. I have shown the cellular signaling pathways in Fig. 6.

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図 5.3­6

　

Fig. 6

Fig. 6 Signaling pathways in a cell

I have shown, in the lower part of Fig. 6, the interaction stage of DNA and TFs, and also I have underlined the molecules seemingly related to MX­1 actions. When we tested MX­1 actions in the systems described in Fig. 6, we were able to observe MX­1 action pattern changes by adding basic amino

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acids but no pattern changes were observed in non­aldehydic group­containing MX­1 derivatives [19]. Therefore it can be said that, as mentioned in the previous section, the creation of weak interactions withinthe signaling processes are the essential actions of MX­1. I have showntypical examples in Fig. 7 and Fig. 8.

2001901801701601501401301201101009080706050403020100

min10 20 40 60

5 10 20 40 60min

Percentage

pM nM

図 5.3-7

MX­1 concen tration (M )

1 10 100 1 10 100 1 10 100 1000

Effects of  MX­1 on CRE­DNA  In teraction

Fig. 7

Fig. 7 The oscillation pattern of CREB­DNA interface (1)

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percentage

2001901801701601501401301201101009080706050403020100

min m in20 10 5 1 0 1 5 10 20 40

pM nM M

図 ５．３－8

MX­1 concentration (M)

Effects of MX­1 on CRE­DNA  Interaction

1     10     100                         1     10     100        1    10     100     1000

Fig. 8

Fig. 8 The oscillation pattern of CREB­DNA interface (2)

The vertical axis of these figures represents the relative amounts of DNA­TFs clusters and the horizontal axis represents the power law indented MX­1 concentrations. As shown in this figure, the generation of clusters isincreased or suppressed depending on the MX­1 concentration. However,Fig. 7 and Fig. 8 show considerably different features especially not only in waveform but also in periodicity and frequency. The lower oscillation patterns in both figures are the combined graphs of dissociation and association patterns of DNA­TFs along the time course. I simulated the upper oscillation patterns indented by MX­1 concentrations, from the lowerones. In the combined graph of left side association pattern and right side dissociation pattern in Fig. 7, the time directions are from the left to the right in both patterns. However, in Fig. 8, the time directions move out in opposite directions from the pattern center. In the same experimental conditions, we tested many TFs other than the previously mentioned CREB, for example, NF­κB, AP­1, USF, GRE, SP­1 and NF­1, which have different DNA binding sites to each other. We tested two agents other than MX­1, which show slow dissolving kinetics [19]. However, despite the different agents and different TFs, we obtained almost the same patterns as those in Fig. 7 and Fig. 8, which we can easily separate into two types of patterns by

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simulating the dissociation­association patterns. Remarkably enough, AP­1 and NF­κB, which have opposite direction actions in vivo and compete with each other for the same binding site of DNA, show the symmetry patterns upside down and this relationship is the same despite them being different agents. These phenomena were observed in the patterns of cellular functions as shown in Fig. 9.

Some chaotic rules of gene expressionregulating activities by MX­1

NF­kB

40%

AP­1MMTV

MMTVAP­1

AP­1

10 ­3

20% 40%

20%

­20%

­40%

MMTV

M10 10 10 10 10

図 ５．３－ 9

MX­1 conc.

­4 ­5 ­6 ­7 ­8

10 ­3 10 ­8

percentage

M

Fig. 9

Fig. 9 The oscillation patterns of cellular functions

CREB, AP­1 and USF are said to have almost the same binding style with DNA and in our experiment, these patterns actually become very similar compared to other TFs. This suggests that the oscillation patterns of interacting bio­molecules can be seen as the indigenous interface oscillations between primary molecules despite the different interfering agents. In the next section, I will mention how these oscillations present themselves in living cells.

(3) The patterns of cellular functionsI have shown in Fig. 6 the cellular signal transduction pathways formed in

non­linear and network fashion, which practically show cellular information

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processing pathways by molecular chain reactions and molecular clusters. Now we are looking into the outstanding natural processes by which these intricate complex systems finely respond to diverse environmental changes and how these systems quickly decide their own most suitable state in that environment. For this purpose, we chose 4 systems including the previously mentioned gene expression systems and calcium distribution dynamism in cells etc., though, in this article, I only mention the oscillation patterns of the gene expression information processing pathways. Stimulation to cells from the outside environment exists in a wide variety of ways, for example the bio­molecule’s mediation of neuro­immune­hormonal actions. These molecules bind to cell surface receptors, transmitting the information all along the signaling pathways. However, the routes are intricate networks and so it is impossible to elucidate linearly the law of causality relating the original causes and their effects. Nevertheless it is the essence of life, that cells continuously conclude, moment by moment, as to whether they are being imposed upon by a ‘nuisance’and then to act upon that conclusion, so it should be high on our agenda to discover how information processing works, how the state of soundness can be measured and how the system can be fixed in cases of disorder.

Tracking cellular systems is the so­called CAT­system using the reporter gene, which one of our members had studied for a long time [14,24]. Briefly, this is the information processing system that decides finally whether incoming signals should enhance or suppress DNA synthesis, which are transmitted by the long intricate signaling pathways from outside of cells. We can simulate the system, for example, in such a way that the system includes all the necessary tools, using a real virus like MMTV, or a system that includes only the necessary parts required to observe the activities of AP­1 and NF­κB. In this system, we can observe the processes from RNA to protein synthesis by way of the reporter gene synthesis. I have shown in Fig. 9 the regulation patterns of MX­1 in MMTV­CAT, AP­1­CAT and NF­κB­CAT systems. In these 4 figures, the vertical axes indicate the relative amounts of gene expression regulated by MMTV, AP­1 and NF­κB and the horizontal axes show the same exponentially indented scales of MX­1 concentrations. MX­1 continues to change for a long time as the micelle structure gradually decays to become smaller and smaller and so the horizontal axis contains the same time element in each point. I show the

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patterns of MMTV and AP­1 together because they show almost the samepatterns. I have chosen 4 typical curves and presented them in one figure. As clearly shown in this figure, there are 2 different types of wave periodicity and the long period waves have reverse symmetry from the top to bottom alternately. In the case of short period waves, AP­1 and MMTV have almost the same patterns but NF­κB show almost the upside down symmetry similar to AP­1 and MMTV. The regulatory molecules, that influence MMTV and AP­1 are quite different in these transcription processes and the MMTV system has tremendous complexity compared to the AP­1 system, the resulting amounts of gene expressions show the same type of oscillation pattern in MX­1 concentration indent. Also, we conjecture that oppositelydirected AP­1 and NF­κB patterns reflect the fact that, as generallyconsidered, two TFs compete with each other for the same DNA binding site and act to oppose directed gene expression regulation in living cells. We could observe the same upside down symmetry patterns, which seemingly reflect the same phenomena, in the wave patterns of molecular clusters. The patterns that we observe using different interfering molecules in molecular cluster experiments are completely the same as cellular function patterns. Furthermore, it is the same at both molecular and cell levels that the two different periodicity patterns can be simulated by different­directed­time patterns. From the above mentioned results, we can conjecture that the oscillation patterns of signaling pathways can be attributed to a common principle from molecular to cell levels and the original oscillations can be traced to the interface of weakly interactingbio­molecules. Next, we look into the vital signals that reflect those oscillations.

(4) The actions to the whole system and the oscillation patternsIn this section, I discuss how the action of a weakly interacting agent, like

MX­1, is reflected at the whole body level information processing system. Following on from the discussion in Section 1, I have shown, in Fig. 10, the sequential data of λ1 and E measured by finger pulse before and after the 20mg MX­1 intake.

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Day 1 day2 day3 day4

and E

E

. E

MX­1

図 　5 .3-10Fig.10

Fig. 10 Oscillation Pattern ofλ1 and E by MX­1 administration

The horizontal axis in this figure shows observation time of finger pulse from 1 day before to 3 days after MX­1 intake. We took measurements either 2 or 3 times per day (3 times per day at 9:00, 13:00 and 16:00, one day before and the day of MX­1 administration, and then 2 times per day at 9:00 and 16:00 after that). For example, one measurement, say at 9:00 am, consisted of the recording of the finger pulse over a period of 100 seconds, which was thenrepeated 5~7 times. The vertical axis shows the values ofλ1 (solid line) and E (dotted line) calculated from the 100 seconds measurement. As shown in this figure, the 4 sets of λ1 and E values, from one day before to one hour after the intake, show changes within almost the same range but, at 4 hours after intake, λ1 becomes higher than E and next, E becomeshigher thanλ1. That is, the 2 indicators come to oscillate vigorously. I think that the oscillation of chaos indicators arises from the same mechanisms by which MX­1 interferes with the oscillation patterns of the information processing pathways of molecular clusters and cells, because the other agents I chose also showed almost the same pattern changes in in­vitroand in­vivo experiments. (data is not shown) Judging from these results, I can say that the classification of the healthy subjects and patients obtained

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from chaos analysis of vital signals mentioned in Section 1 reflects the dynamism of the information processing systems at the interface of bio­molecules and also in cellular signaling pathways. If the total state of a life ultimately reflects the oscillation patterns at a bio­molecule’s interface, the methods which can analyze the relationship of “the part and the whole”in terms of its signaling patterns must make these data clearer. At first, I will mention the discrete wavelet analysis ofλ1 and E. Briefly, this is ananalysis by which we can produce numerically, the rough similarity oftesting complex waves to the free scale changing unit waves, and can draw the result as a 2 dimensional picture of changing scale and time in terms of color gradation, or we can display the whole picture in separate levels. This transform manipulation is said to be very similar to the human senses. Ihave shown in Fig. 11 the separate level display of Fig. 10 by using discrete wavelet analysis.

図 　５．３ー１１Fig. 11

Fig. 11 Wavelet analysis of λ1 and E

In this figure, the original λ1 data is presented at the top of the left column and the original E data at the top of the right column. The wavelet analysis results are under these. I have shown the average motion portion (approximation) in the left column and tiny motion portion (detail) in the

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right column. The upside down symmetry is made more outstanding bystacking up each of the level’s λ1 and E. In this representation, we can observe the effect of MX­1 in Fig. 10 as the enhancement of symmetry inλ1 and E in the D3 level (shown by the arrow in the 4th level in the right column) among the A1~A5 (left) and D1~D5 (right) levels. The oscillation patterns of molecular clusters initiated by MX­1 can be divided into waves of the 2 different periods, like the cellular function pattern, by using unit wavein rough classification. It can be said that the common ground betweenthese 2 different manipulations is the pattern classification by sorts of coarse graining. I want to argue the reasons of outstanding symmetry in a particular level at a future date. Next, we compare the self­similarity parameter α before and after MX­1 intake, which can quantify the fractal or self­similar character of the randomly fluctuating signal waves by using, so called, Detrended Fluctuation Analysis (DFA). This is the method used to compare the 2 standard deviation values of the enlarged fluctuation signal and the original fluctuation signal [25]. Briefly, as shown in equation (7), we can get the whole trend y(k) by subtracting the mean values from i th data and summing up along the whole partition,

Then we subtract the partition trend )(kyn from the whole trend, and sum

up as follows,

We get the slope in the graph of log F(n) and log n as the self­similarity parameter α. This α relates, in a certain manner, to the autocorrelation function C(τ). In the range of α=0.5~1.0, this signal is judged to have long­range correlation, in the range of α=0~0.5, anti­correlation and at α=0.5, no correlation, also called white noise. At α=1.0, the signal is called 1/f fluctuation and music corresponds to this fluctuation. Furthermore, atα?１, there is no power law while at α=1.5, the signal is called brown noise. By this method, we compared the before and after values ofλ1 and E in Fig.

(7)                 1

k

i

aveBiBky

(8)         1 2

1

N

k

n kykyN

nF

22

10 and, as shown in Fig. 12, we obtained the ‘before’ value of 0.427, giving us almost no correlation, and the ‘after’ value of 1.01, almost the 1/f fluctuation and also, at these points, the E value becomes 0.944 from 0.414.

図５．３－１２MX­1投与前後のα値の変化

Mx­1

intake

α（ E）=0.414

α（ E）=0.944α(λ１）＝1.01

α(λ１）＝0.427Fig. 12

αvalue change before and after

MX­1 intake

Fig. 12 α value change before and after MX­1 intake

From these results, we can say that, in this vital signal analysis, MX­1 actions brought about oppositely directed fluctuations ofλ1 and E and furthermore, these actions could also be captured as the fractal dimension of the vital signals, that is, an enhancement of the correlation of “the part and whole”. These α values are different in normal subjects and, for example, depression patients but, accompanying a recovery in symptoms, these values also recover. Being the same as theλ1, E and F­constant, I think the αvalue also reflects the dynamism of the bio­information processing system.

3. A discussion of the oscillation integration principles If the core of life exists in the integration processes of the bio­molecules’interface oscillations, I want to approach life from the aspect of how the physical and mathematical laws make life possible. In the first instance, I think the soundness of the oscillation integration processes should satisfy,inevitably, the necessary requirements both in space and time simultaneously. In a previous section, I mentioned the oscillation patterns

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of the molecular clusters and cellular functions caused by the concentrations of the interfering agents. However, these arguments for such patterns wereinitially unwillingly employed for the reason that the time series patterns were too complex to categorize. However, we have been able to obtain excellent reproducible experimental patterns and therefore are able to present a holistic argument. I had found, by purely lucky chance, this way after a difficult and repeated trial and error period of about 5 years. I could not imagine that we could get such reproducible experimental results and gain such an understanding of the experimental data for almost 15 years, in which period we did a desperate MX­1 action mechanism study from the standpoint of reductive biochemistry and pharmacology. What I have done with regard to classifications of molecular clusters and cellular function­experiment­patterns is to identify the space related data (concentration indented) with the time series data (time indented) and to try to explain 2 aspects of the phenomena by one common principle. Thejudgment of human health by the symmetry ofλ1 and E in vital signals could show, from the oscillation in time, the state of the system at a certaintime as, for example, in depression or ulcerative colitis etc. Giving a familiar example, someone can judge the state of an engine from the engine sound [3]. The common aspect mentioned here is the fact that the oscillation patterns in time can be mutually substituted by the space distributing patterns. These things are clearly mentioned by the following phrases of Josiah Willard Gibbs [26]. “ We may imagine a great number of systems of the same nature, but differing in the configurations and velocities which they have at a given instant, and differing not merely infinitesimally, but it may be so as to embrace every conceivable combination of configurations and velocities. And here we may set the problem, not to follow a particular system through its succession of configurations, but to determine how the whole number of systems will be distributed among the various conceivable configurations and velocities at any required time, when the distribution has been given for some one time… … ” This is called “ ensemble approach” or “ ergodic theory”, and I think this is seemingly the starting point of the “dissipative structure theory” of Ilya Prigogine [26]. In the case of the above­mentioned data, we cannot get a meaningful insight of the system by following a particular system in the

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course of time. We can get the distribution, however, with the patterns indented according to MX­1 concentration, and also, these distribution patterns can be meaningfully separated into 2 types by combining different time­directed unit pattern. In the case of the vital signal analysis, the individual time­dependant pattern can serve as a basis upon which to categorize the whole system’s distributions, normal subjects and diseasedpatients. In these instances, the difference between the state of an individual system in the course of time and the state of the whole system’s distributions becomes quite obscure. What can the laws of physics possibly make of such things ? I want to argue this question next. Recently, it has become clearer that signaling pathways such as cell growth, metabolism and enzyme activity can be affected by magnetic fields of around more than 100 microtesla ( μ T) [27­29]. Many biological experiments have been done in magnetic field environments. For example, Harkins et al. showed clear evidence of the action of an external magnetic field upon the enzyme activities of vitamin B12 –dependent ammonia lyase. In this experiment they analyzed the electron dynamics of coherent (interference) spin moment generation on radical species present at theenzyme active site and made clear the relationship between the electron dynamics and enzyme kinetics [29]. They explained their results by suggesting a mechanism in which the spin coherence of the radical pair generated in the transition state of the enzyme action, was held for 0.1~100 nanosecond and in this period, if the coupling magnetic field, which can couple to the coherent electron state, is applied from the outside, the recombination of the radical pair is disturbed by the Pauli exclusion principle, thus affecting the enzyme activity. In the case of a weak externalmagnetic field such as 0.1~50 mT, it is mainly the electron spin and nuclear spin that react to such a magnetic field, but in the case of a strong magnetic field such as 0.1~1 T, so called spin­orbit coupling, which is the quantum interference reaction of the spin moment of the electron and its orbital motion around the nucleus, occurs in response to the magnetic field. These 2 interference reactions have the oppositely directed effects on the enzyme transition state, so the enzyme reaction takes on biphasic fashion. From these experimental data, the so called radical pair mechanism (RPM) model is theorized, by which we can predict the oscillation of the enzyme activity by the oscillating magnetic field or the change in oscillation patterns by the

25

combination of static and oscillating magnetic fields. The combination ofoscillating and static magnetic fields brings about a variety of effects in the enzyme’s action cycle period, in some cases, it becomes the same as the outside magnetic field, and in some cases there is sudden period doubling or simple summation of the outside magnetic fields. In view of these enzymeactivity changes, the enzyme can be said to take on the role of ‘frequency sensor’ of outside stimulations. Biological organizations might transmitinformation by this frequency sensor, in which the information is carried as the oscillation patterns between the superimposed or entangled quantum state (coherent) and the decomposed or dismantle state (decoherent or classical state) [27­29]. It is my view that these mechanisms are very similar to quantum information transmission, like single electron transistor or quantum computation [30]. Although there are some differences, I think that the imposition of an external magnetic field and the imposition of a chemical species (MX­1 has weak and continuous interactions to the target electron states [19,21,22]) as perturbations on a biological system, have some common features. One of these is that, in the case of RPM, the directions of enzyme action are regulated oppositely, depending on the strength of the magnetic field and, in the case of MX­1, molecular cluster and cellular functions are regulated oppositely, depending upon MX­1 concentration. It is thought that this biphasic action causes the 2 types of period doubling patterns [27]. There are many articles in which the oscillation of molecular clusters or molecular interfaces in the signal processing pathways, including enzyme systems, are suggested to be the oscillation of coherent and decoherent quantum states and also biological tunneling effects, seemingly related to the kinetics of the coherent­decoherent states [27­29, 31­34]. The principle of pattern classification in molecular clusters and cellular functions, is consistent with the thought that the change from coherent state to two types of decoherent state, one of which contains time in opposite directions and the other containing time in the same direction. As mentioned in Section 2 (3), the oscillation patterns at the interface of 2 interacting molecules and the long range signaling pathways involving many molecules, are almost not so different from each other, and this suggests the basic oscillation pattern is maintained after oscillation integration. Finger pulse oscillation signals are the final link in this chain. These results are consistent with the

26

thought that the enhanced dynamism or enhanced oscillation integration by MX­1, both in vitro (molecular and cellular levels) and in vivo (λ1 and E levels) can be attributed to the enhanced and widened coherent­decoherent oscillation fluctuation by MX­1’s tunneling effects. The relationship in which, ifλ1 become large, E become small, reflects the relationship that, if the attractor diverges more and more, E should become lower and lower,namely, higher and higher in order because λ1 expresses the divergence of the trajectory. On the contrary, if the divergence becomes lower and lower, disorder become higher and higher. This relationship, in my eyes, corresponds to the relationship of position and momentum of quantum in Heisenberg’s uncertainty principle.

4. The relationship to other holistic science and technology From here, I want to argue how the above­mentioned measurements and results are positioned with respect to other holistic science and technology. The essence of a life or an information processing system is, according to Alan Turing, the self­organized physical phenomena, which finally come to the phase transition in symmetry breaking, mediated by chemical agents and, according to Ilya Prigogine, it is the system, which can transform itself into different modes in response to environmental changes. It can be said that oscillation phenomena in vital signals such as brain waves, heartbeats, heartbeat intervals and gate interval fluctuation are ultimately the oscillation of the information processing system. If these mechanisms are essentially the same, it is not surprising that we can make some common observations concerning the different signal processing data from different oscillation sources. From this viewpoint, I have briefly indicated various typical examples in Table 1.

27

ref 5

表 5.3­1　解 析法の 比較 　

RR­interval Wavelet H ilbert Sleep Apnoea第 一次粗視化 第 二次粗視化

Wavelet SD 心 不全、SCD

ハ ースト指数 Wavelet うっ血性心不全（マルチフラクタル）

DFA 心 不全

DFA（２振 動引き 込みフラクタル）

０．８６（健常人）

Gate­interval DFA ハ ンチントン病、加齢

脳 波（波振幅 変化） SD ベ ルヌー イ・シフトハ ースト指数DFA 0.65  (H igh Alert)

Cantor set

脈 波 リアプノフ指数 ベ ルヌー イ・シフトDFA

0.65（健常人 ）Cantor set

エントロピー ベ ルヌー イ・シフトDFA

1.45(健 常人）Julia set

H iguchi D1, D2 D1/D2 4.7(健 常人）Feigenbaum定 数

水 、Diode, Transistor  4.3­4.7ヘ リウム 3.5

DNA配 列 DNA Walk 法（積分）

DFA 0.65(90% non­Codon, 10%Exon)Cantor set0.50(Exonの み）White Noise

　　　　　　

Table 1 Comparison of analysis

1st coarse graining 2nd coarse graining

Heart failure, CDHurst exponent Congestive heart 

failure,multifractalityHeart failure0.86(normal)

Huntington’s disease, aging

EEG(βwave amplitude) Bernoulli shift,Hurst exponent,DFA

Finger pulse

Lyapunov exp. Bernoulli shift

Entropy Bernoulli shiftDFA

Normal)

(Normal) Julia set

(Normal)4.7 (Normal)4.7 (Normal)4.7 (Normal) Feigenbaum constant

Water, Diode,     transistor 4.3­4.7Diode,

DNA sequence

DNA walkDNA walk

(ntegral)DFA 0.65 (90%  non­Codon, 10% Exon)

Cantor set, Exon only 0.50 White noise

Helium  3.5

The interval between each heartbeat is called the R­R interval and Ivanov et al. succeeded in diagnosing sleep apnoea by analyzing the time series data of intervals, at first, subjecting the data to Wavelet Transform and, next, analyzing the accumulated changes [35]. Thurner et al. could discern the heart failure patients and sudden cardiac death patients from normal subjects by multi­resolution wavelet analysis of heartbeat intervals [36]. Goldberger et al. [25] calculated α values by using DFA of gate intervals of young and old normal subjects and Huntington disease patients and showed these values to be 1.0, 0.5 and under 0.5 respectively. They showed in more subjects these α values have a high correlation with positron emission tomography in the diagnosis of Huntington’s disease severity [25]. Poupard et al. [37], in their chaos analysis of brain waves (EEG), calculated α values from the wavelet transform, Hilbert Transform and DFA using the standard deviation of β wave amplitude changes as time series data. When the level of alertness dropped, the α value was found to increase to almost 1.0 and long­range correlations grew rapidly in the system. In contrast, when the level of alertness was high, this value was low, for example, 0.65 [37]. In our finger pulse analysis, α value of λ1’s DFA was, in the case of normal subjects, 0.65 but, in depression patients, these values were 0.40 and 0.56, and so lower in correlation. As shown in Fig. 3, the mean F­constant

28

value for normal subjects is approximately 4.7 and the mean value for depression patients is around 4.0. This means that the correlation of bio­molecules or element units in depression patients is lower than normal subjects. It can be predicted from the α values of EEG that chronic high alertness of consciousness exists in depression. The phenomenon called stochastic resonance (SR) is well known as the technology that improves transmission ability by adding noise to thetransmission line and we can choose optimal noise to attain the besttransmission. This means that the phenomenon is regulated by oppositelydirected mechanisms and, in this sense, is the same as phenomena in life, and so, optimal noise enhances the pattern formation [38]. It is said that SR is possible at classical, semi­classical and quantum physical levels and, for semi­classical and quantum SR, thermal fluctuations, quantum tunneling effects and dissipative processes exist [38]. Especially, in the field of granular physics, Sinbrot et al. showed an entropy decrease by noise, which is indispensable for life and they proposed a model that can explain the formation procedures of complex patterns and orders [39]. Also Takatsukasa et al. showed λ1, obtained from the orbit calculated by the formula of chaotic tunneling effects, reduces as energy increases but in contrast, λ1 calculated as the classical orbit, increases along with the energy increase [40]. As I mentioned above, dynamisms, such as quantum chaos and SR, have the same features as the seemingly strange dynamisms of λ1 and E mentioned before and these dynamisms can be the causes of life’s existence. Collins et al. began a new therapy to improve declining somatosensory function and diminished motor performance in elderly people by applying noise by way of stochastic resonance [41]. The same authors showed that gene expression is regulated probabilistically by noise and they confirmed that cells continued to remain for a long time in 2 stable patterns under the influence of noise [42]. The reasoning behind these results is thought to accord with that of our own data concerning gene expression patterns. Many articles show that gene expression regulation experiments can be dealt with and explained in terms of fluctuations containing dissipative processes or quantum mechanics [43­45]. Shannon’s information entropy (E) that we use in this article has been shown to have a relationship with kinetic energy in fermionic (electron and proton etc.) many­body systems [46]. Information

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signaling processes such as molecular clusters in our experiments are expected to have the same kind of quantum state to transmit the information. Therefore, it is not surprising that this E’s uncertainty of position and momentum information is the same as our data mentioned in Section 3, consistent with Heisenberg’s uncertainty relations [46]. Also, Hobson et al. insists that human consciousness is dependent on to what extent, in the brain area, the oscillation of the firing of neuronal cells becomes coherent [47]. They think this is determined by the change of the relationshipbetween modularity and globality of the oscillation area brought about by outside stimulation, including noise. These relationships seem to me almost the same as those concerning coherent­decoherent states described by Walleczec and McFadden and those which Tegmark described regardingcoherent states produced by decoherence in a model of a coupled quantum oscillators chain [27­29, 31­34, 48].

5. My hope in the future It seems to me that the enhancement of symmetric fluctuation of λ1 and E in our finger pulse analysis has various meanings considering the facts I will now describe. I would like to take as an example the graphical data of Torres et al. who obtained the Lempel­Zip complexity (LZ­complexity), which is a more accurate type of Lyapunov exponent, by using the sliding windows technique in a certain period in time­series data of human R­R intervals, and a type of entropy, called approximate entropy, obtained in the same manner [49]. The graphs show no relationship. Also results following the addition of white noise are shown and it is stated that, by this procedure, the original patterns collapsed. However, I have laid one graph on the top of the other and surprisingly, although the 2 graphs before noise addition show no symmetry, after adding noise the 2 graphs show almost complete symmetry. An explanation may be that there are 2 indicators in life phenomena, which are connected with the same mathematical manipulations as life actuallyuse it (stochastic resonance manipulation). For me, it seems that this is the expression of life phenomena at the levels of molecule, cell and whole body and this is also the mathematical expressions of the symmetric patterns of molecular and cellular levels and λ1 and E level. Mandell et al. also reported conservation of entropy in neuronal systems [50]. We can easily understand the relationship of in vivo and in vitro, that the

30

power law change observed in α values after MX­1 administration can be seen, at the level of molecular clusters and cellular functions, as the power law of concentration indent. I have shown in Fig. 13 the data of MMTV­CAT activity at MX­1 concentrations 310 ~ 1410 M.

　　　　　　　

図 5.3­13Fig. 13

Fig. 13 Fractal of cellular function expressions

In the upper left of Fig. 13, I have shown the relative amounts of gene expression along the vertical axis and MX­1 conc. along the horizontal axis. In lower left, I show the part of the conc. 310 ~ 510 M of the upper left conc. 310 ~ 1410 M in the same indent. The lower right is the part of the

610 ~ 810 M of the upper left. The upper right shows the reproducibility of this experiment and I think this is a good reproducible fractal in spite of the final expression of complex signaling pathways. Next, I calculated E from 10 min finger pulse and made a time series E data by sliding the range of calculation one second by one second. Although not shown here, the resulting figure shows a rough symmetry between the first half and the latter half. To enhance this characteristic, in Fig.14, I show a wavelet transform figure of this one, as mentioned in section 2(4).

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図 5.3­15図 5.3­14　　　　Fig. 14

Fig. 14 Wide area symmetry of E

The horizontal axis of this figure shows the time path from left to right and the vertical axis shows the scales of change. This big pattern seems trianglar and so, it seems to me, that the time in Shannon’s entropy dynamism flows in opposite directions in the left and right half of this figure. This pattern seemingly shows that oscillation patterns in molecular and cellular levels can be simulated well by the opposite time directed unit patterns. It also seems that the oscillation of coherence­decoherence atmolecular level seems like that at whole body level and time structures incorporated in signaling pathways weave the various dynamisms of life. The mysterious appearance of time incorporated in this structure can be seen in Schneider et al.’s data [51]. They systematically analyzed the binding site sequence of the transcription factors in various species of DNA chain and proposed the expression way as Shannon’s information entropy. They called it sequence­logo and succeeded in making patterns of interfaces of the bio­molecules. Looking at these patterns, E patterns of binding sites have the symmetry of the first and last half as shown in Fig. 14 and, when proteins are synthesized from DNA, it can be said, for example, the film then turns around to reverse the direction in the middle of the movie. Natural

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binding sites have this symmetry but artificial binding sites do not have this symmetry [51]. They did not argue this point, so this is another example of mysterious dynamism of time structure from my viewpoint. At last, I want to tell you my dream. That is the so­called quantum gravity theory and Takeuchi [52], for example, said the degree of rotation angles, which repeats the topological pattern by quantum gravity is 720 degree, corresponding to twice the rotation. Almost half of this article is about the dynamism of bio­molecule interfaces, which always includes time direction so there should be 4 dimensions and also it always relates to topology (phase). In Fig. 9, I show, in the upper right, the different pattern by starting date of experiments from 1st to 4th day as the order from top to bottom. From these figures, it is clear that the 1st and 3rd patterns are the same and the 2nd and 4th patterns are also the same. This system, as shown in Fig. 6, is a simple reporter gene system and so we cannot think that thisgene works like a clock and sunlight regulates the rhythms because it is in an incubator. I reported this data in [15,16] but at that time I had been thinking it might enhance the circadian rhythms. However, now in another way of thinking, if this system is affected by the moon’s gravitation, topologically, the same pattern does not return after 24 hours but after 48 hours [52]. I haven’t mentioned here though, we observed the same 48­hour repetition of topological patterns in oscillations of calcium distribution in the cell regulated by MX­1. Our data suggested that circadian rhythms are not attributable to the sun but to the moon’s gravitation, which brings 48­hour period rhythms and topologically­opposite 24­hour period rhythms. I hope I can come to understand the true reasons for these phenomena in the near future.

6. Summary I have discussed holistic diagnosis and possible therapies of physical and mental health. I aimed to gain mutual understanding using both holistic diagnosis and therapy and current medicine based on our current understanding of life. To achieve this aim I gave a detailed explanation that, by holistic methodology, we were able to observe the oscillation patterns of quantum and classical states in signaling pathways integrated in a fractal way and also were able to estimate the soundness of the system upon theseconstruction principles. Further, I aimed at gaining a mutual

33

understanding of both reductive and holistic science by explaining the basis of holistic medicine in physics and mathematics. It is my wish in the near future to understand life and its physics and mathematics comprehensively, I went ahead and discussed fields in which I am inexperienced. I omitted, owing to limited pages, the more detailed data concerning differencesbetween life and mere molecular clusters and also the argument about set theory, giving greater meaning to these holistic methods.

I express my deepest thanks to my many mentors and practical collaborators,Professor the late Robert A. Good in the University of South Florida, Professor Emeritus, Arnold J. Mandell in UCSD, Professor Yoji Aizawa in Waseda University, Department of Applied Physics, Dr. Miao Tiejun in Computer Convenience Co., and I thank very much Otsuka Pharmaceutical Co., JIMRO Co., and CCI Co. for their contribution to the study of this theme over a long period.

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