User:Edzevallos

Continue from: http://en.wikipedia.org/wiki/Entropy_biology

Health and disease

Hypothetically, if a biological system at any instant matches its Smax it would collapse. This makes plausible that the biological system encounters a state of total equilibrium of all its elements, at all degree of freedoms, and of course, instantaneously. Under this hypothetical status, at Smax, the biological system halts because no more events are possible. This means that the biological system is unable to produce useful energy, thus it cannot produce work so it cannot produce more information. In Chaos Theory terms, it is like falling into the “infinitesimal infinite” or zero [44, 45]. We are not quite aware of any known pathological condition associated with perfect equilibrium at Smax. No research has been done on this particular issue. Smax-like conditions may occur at ultrastructural levels, or as “hyperfunctional” levels. Disseminated intravascular coagulation may follow such thermodynamic mechanism; in fact at some point, most or all coagulation factors can be interacting at a Smax-like status. Atherosclerosis might be also some kind of Smax-like condition. Well, if a biological system reaches a deleterious Smax status, what is next? Since it cannot sustain itself any more, it would blend with the Suniverse. However, pathological conditions may primarily follow the opposite thermodynamic route, which is the tendency to merge with the Suniverse. Diseases may start as by increasing Entropy at ultrastructural sub-cellular level in an organ. For instance cancer and autoimmune diseases may follow this entropic pattern. The increasing Entropy disseminates into a cell, then from cell to cell, then to the whole organ, then to the system, and finally to the whole body. Clinical manifestations such as pain, fever, malaise, etc. may be associated with increasing Entropy. When the entire biological system reaches certain high Entropy level, being unable to keep itself, it dies. Another possibility is that pathological conditions represent alternating phases of high/low Entropy, resembling different directions in relation to space state attractors (different Lyapunov exponents) [44]. The immune system can be better understood through Chaos Theory, because it is complex, self-assembled, interactive, fractal, evolutive, and capable of memory. There is a balance between Th1 and Th2 immune responses, thus the immune system can be conceptualized as a self-referential network revolving “strange attractors”, which in turn are very important in tumorigenesis and cancer treatment [46]. Brú, et al, have found that malignant tumors show a clustering fractal dimensional growth at their contour, which renders this microenvironment more acidic. Space competence with the host is apparently the main factor in the growth of tumors. Brú’s results contradict the classical oncology tumoral kinetics, and, moreover, challenge the foundations of chemotherapy and radiotherapy; besides, he conjectured that neutrophils by resistance to acidosis and competing for space might play an important role to inhibit tumoral expansion [47, 48]. In fact, the same group has reported the cure of a terminal hepatocellullar carcinoma by inducing patient’s neutrophilia with granulocyte colony-stimulating factor [49]. Understanding boundaries may be extremely important, as all systems may follow the holographic principle as way of expression of Information (Entropy) “living” at boundaries [50 – 53]. Goldberger, by the analysis of heart rate plotted against time has conceptualized a similar model of heart disease. Healthy hearts exhibit rates of nonlinear dynamics and possible chaotic behavior, which reflects the physiological flexibility and adaptability. Contrarily, in heart disease rates became periodic or totally random [54]. Possibly, the periodic rate indicates abnormal tendency to Smax, in the sense that the functional pathways are being reduced by an equilibrium imposed by the generic attractor of the system.

Life and evolution

Looking for “the cause of death” in an autopsy appears a bit odd, as thermodynamically speaking death is a whole process, besides the entire body dies. Death is a dynamical process, therefore cannot be localized. The conjecture here is why doctors, then, do not look for “the cause of life”? Seriously, it will be more beneficial.

As per our conjecture, the basis for what is life may be at the core of the equilibrium between the Smax and Suniverse inside the biological system. Entropy, as a universal principle with different aspects, renders integrity and functionality to all systems, including biological systems. Integrity and functionality are sides of the same coin, ultimately. This approach has many points in common with the universal law “Tao” of Lao-Tzu (400 B.C.) [55]. The Yang and the Ying are comparable to Smax and Suniverse, and similarly to the Second Law of Thermodynamics and Entropy, this can be extended to the whole universe. “Ying exists within Yang; Yang exist within Ying”, is an expression that implies binarity. Lao-Tzu in the opening of his book “Tao Te Jing” says “The Tao that can be told is not the eternal Tao, The name that can be named is not the eternal name”. We assume that this uncertainty of Tao depends on its extreme complexity. Entropy might be dual with the “eternal” Tao. Similarly, at dimensional extremes Entropy also falls into extreme uncertainty. According to Schrödinger’s famous book “What is life” [56], life basically covers two aspects “order to order” and “disorder to order”. “Order to order” is akin to reproduction or replication of the organism. In biological systems usually this is through DNA, which is essentially a macromolecule (polymer) with high binary code capacity. The immense probability of base combinations makes DNA extremely uncertain, therefore likely having high Shannon’s Entropy. Knowing the sequences of DNA, although a breakthrough, may have reduced almost insignificantly such uncertainty as the informational power of this polymer depends vastly more on its functional expressions (variety) than that of its sequence. “Disorder into order”, this means that biological systems can exchange energy and matter with the media. In animals the source is basically matter exchange. We uptake matter with relatively low Entropy, and subsequently the programmed enzymatic metabolism reduces even more the Entropy of this uptake. Metaphorically, animals bite the bits of vegetables, in the sense that animals nourish with the Information taken from the vegetables. Salthe has evoked a similar concept of transference-degradation of energy between “gradients and consumers”, where the consumers are also “gradients” [57]. As an extension of this hypothesis, he has proposed that users of energy do not increase, locally, the overall Entropy of the system; however, they do shape the distribution of Entropy. In this context, Evolution can be conceptualized as introduction of new levels of consumers to optimize the management of Entropy, which ultimately restrain the tendency for dissipation, and simultaneously the Entropy production is smoothed (linearized) and kept under regulation. Salthe metaphorically, concludes, “Evolution, then, is the Universe’s devious route to its own negation” [21]. The main aim of regulatory biological mechanisms is to prevent abrupt changes of entropy among boundaries, and in this manner maintain homeostasis. “The second law implies that the free energy of an isolated system is successively degraded by diabatic processes over time, leading to entropy production…The formulations from classical thermodynamics can be applied to non-equilibrium systems which are not isolated (e.g., Prigogine 1962)… For these systems, the Second law then takes the form of a continuity equation, in which the overall change of entropy of the system dS/dt is determined from the local increase in entropy within the system dSI/dt and the entropy flux convergence dSE/dt (i.e., the net flux of entropy across the system boundary): dS/dt = dSI/dt + dSE/dt…A non-equilibrium system can maintain a state of low entropy by “discarding” high entropy fluxes out of the system” [58]. An elaborated mathematical and statistical form of non-equilibrium Entropy has been proposed [59].

Production of vitamin D, reduction of melanin in skin, and reduction of retinoic acid in retina are few of the reactions in animals that involve direct usage of solar radiation. Contrarily, in vegetables the solar radiation is the main substrate source through the process of photosynthesis. A flaw of anthropomorphism is that DNA is the starting source or origin of life. Contrarily, DNA is just another biological polymer, and as any other polymer in nature it also has Entropy, and, therefore carries information. Besides, Schrödinger’s dichotomization of “what is life” may be arbitrary, since DNA is a molecule produced by the biological metabolism. Therefore, ultimately life can be summarized as just “disorder to order”, and reproduction is an entropic shortcut in this process to reassure preservation of the systems. It should be clear that in thermodynamics and informatics, terms such “order” and “disorder” must be avoided, because they are subjective, and hence entail anthropomorphic biases [1]. Therefore the question as to “what is life?” can be answer as maintaining lower Entropy than that of the environment (Suniverse), and higher entropy than that of Smax, by exchanging matter and/or “energy” with the environment. Aging process can be thermodynamically explained as a progressive increase of Entropy (“error accumulation), and, therefore dissipation of the system. Interestingly, Gladyshev has formulated a similar model using concepts of Gibbs and Helmholtz functions, which are placed in the context of the so-called “law of temporal hierarchies” (Gladyshev’s law) and the so-called “principle of the stabilization of chemical substances” (Gladyshev’s principle) [60]. This model is essentially dual with the Smax/Suniverse presented here, since quantities such as internal energy, enthalpy, Gibbs free energy and Helmholtz free energy (“thermodynamic potentials”), as well as Entropy can be obtained based on statistics arguments. Briefly, in these terms Entropy can be expressed as S = U – F/T, where U is internal energy, F is Helmholtz free energy, and T the absolute temperature, which means, that when F is minimized then S is maximized. Entropy can be also expressed as S = (U + PV) - G/T, where U is internal energy, P is pressure, V is volume, G is Gibbs free energy, and T is the absolute temperature; additionally, PV is work (W) , and (U + PV) is enthalpy (H). Similarly, this means that when G is minimized then S is correspondingly maximized. According to Gladyshev’s model for biological systems G and F are minimized through a hierarchic gradient sequence of environment/system, which apparently is intimately related with the “law of temporal hierarchies”. Specifically, this law establishes that “a biological system consist of the given organism’s cells, the organism itself, and the population formed by these organisms (i.e., fragment of the hierarchic sequence of biological structures). Identifying the average life-span (life time) of structures makes it possible to assert that the average life-span (t) of a cell (cel) in the organism is much less than the average life span of the organism (org), which, in its turn, is much less than the life-span of the population (pop): <<tcel <<torg << tpop <<…” This assertion is a natural fact. For instance, in the intestine, of course, a cell lives less than villi, the villi lives less than the intestine itself, the intestine less than the animal, the animal less than all the animals, all the animals less than the whole ecosystem, the whole ecosystem less than the planet, the planet less than the galaxy, the galaxy less than the universe, etc. Therefore, by itself this “law” apparently does not have too many consequences if it is not place into the context of a higher physical law or principle. This apparently time hierarchy is perhaps the consequence of not accounting a relativistic time frame. Therefore, this may be indirectly an anthropomorphic bias, because this assessment is done through an apparently external observer only. Moreover, these “temporary hierarchies” may vanish if they are considered as eigen (physiological) times, which together with the metabolic rate can be unified using a broad concept of Entropy, which combines the external and internal observations. Perhaps, in biological systems, the trend to minimize G and F may be related to their tendency towards Smax. Thus, this so-called “law of temporal hierarchies” may be likely a consequence of Smax. Regarding the so-called “principle of the stabilization of chemical substances” (Gladyshev’s principle) it can be deduced that the G of simple molecules such as H2, N2, O2, CO2 and H2O is much more compared to the G of macromolecules. In other words, macromolecules (polymers) have more Entropy than that of simple molecules, but correspondingly less Entropy compared to that of their nearby media (Smax). This assertion is another consequence of the Smax/Suniverse model presented here. Subsequently, because macromolecules are more uncertain for an external observer they carry more information than simple molecules. Similarly, Trambouze has compared complexity with information [61]. Again, we think that the relativistic position of the observer, either internal or external, must be better set up. The decreasing complexity, found by this author, is perhaps an internal perspective and, not surprisingly Information (a la Shannon) simultaneously increases because Entropy is being transferred from inside the system to the outside environment, therefore an external observer experiences more uncertainty. Trambouze concludes that “When modifying a system made up of many components, information input can reduce the random complexity of the system and therefore increases its degree of organization…We suggest calling this information, which modifies the complexity of a system, structuring information, because, when introduced into a given system, it enables its structure to be change”. The term “information input” seems to be analogous to the process of energy-driven-Entropy, as Information cannot physically appears out of nothing. If this fact is not accounted, then the fallacy of a priori stability of systems is created (i.e. creationism). Besides, his “structuring information” smells and tastes as “self- organization”. Moreover, this author’s statements that “Spontaneous evolution of a close system if its free energy decreases” and “Any irreversible transformation is accompanied by an increase in entropy” are akin to our above conjecture that in complex systems (biological systems), the trend to minimize G and F may be related to their tendency towards Smax, but the resulting “current Entropy” of the system is continuously less than the Suniverse. Organized and regulated reactions inside complex systems (metabolism) are intimately related to self-organization. Therefore, an aspect of self-organization is likely buffering G (tending to decrease G and H). For instance, metabolism is an orderly succession of reactions and regulations preventing outbursts of energy (heat), and simultaneously preventing extreme gradients of Entropy. Similarly, Chang Ying-Fang has hypothesized that self-organization, through fluctuation and self-interaction, “may form a lower entropy” [62]. Igamberdiev’s concept of “internal quantum sate” (IQS) is essentially similar to state phase invariant(s). IQS behaves as cellular automata, and it is “concatenated within the 3D space as a molecular computer (MC).” Enzymes are MCs operators, while error corrections are effected by RNA (short term) and DNA (long term). The error corrections do not affect the IQS [63]. In the context of Smax/Suniverse model, IQS can be the thermodynamic status that depends on the instant interactions of all invariants. These invariants interaction is in turn focused on fulfilling constrains imposed by the disturbances arising from the media. Besides, such media can be internally related to Smax, and externally related to Suniverse.

Living organism by evolution are thermodynamically open (to matter and energy) systems far from equilibrium. Otherwise life would not be possible. Here, the key is “far from equilibrium” to explain life. How living organisms acquired such property? Chemical reactions naturally can occur “far from equilibrium”. Therefore, when the odds of a natural phenomenon or event (i.e. a macromolecule or biological polymer such a protein or nucleic acid) are statistically or probabilistically analyzed, a reductionist analysis is totally naive. This means just breaking down the biological polymer into the number and different types of moieties, followed just by a conventional probabilistic calculation of the odds of reassembling it exactly as the initial macromolecule. This is not only terribly obsolete, but also misleading. It is like breaking down a cathedral into bricks, counting the bricks, and making a statistical calculation how the bricks can be reassembled by themselves into a cathedral again. Obviously, this approach is incorrect. But there is also another “little” difference, a cathedral is usually built in few years, instead a natural event had already taken about four billions years to happen, and, moreover, with the advantage that energy constantly flowed through the system. Besides, systems cannot be built from nothing. Definitions in the context of complex systems are always incomplete, skewed and biased. After all, when a pizza can be “defined” as a pizza? By the recipe, by the ingredients solely, by the ingredients together, before introducing into the oven, during the cooking, when is out of the oven, or while is being eaten or later? As the case of the pizza, every natural phenomenon implies a continuum process. The term “life” does not have a definition, and it can be just a formality. Any definition of life, unfortunately, falls into the anthropomorphic semantic rhetoric as a “property” of living organisms…and living organisms live as they posses this “property” of life; however as such this “property” remains obscure. On this regard we partially agree with Nasif. However, this author involuntarily also falls into this anthropomorphic trap, because he tries to push a definition of life, which can be considered as a reductionist attempt. Dynamical complex systems are irreducible and impredicative, therefore defined terms cannot be clearly differentiated from defining terms. The term life is just provisional, which implies a summarized conceptualization of many complex functions. Contrarily, we conceptualized but not define life. Nasif appeals a great deal to energy disregarding the concept of Entropy, and also dichotomizes considerably energy and matter. Besides, for this author the quality of life resides at the cytoplasm of cells related to chemical reductions. We consider life as a whole process that cannot be related to any particular locus, molecule or process, otherwise it will fall again into anthropomorphic terms. In this context the same author says, “… complex self-replication pattern can occur in inert self-replicating templates, for example in prions, viruses, autocatalytic proteins and ribozymes, and all of these structures cannot be considered as living dissipative thermodynamic systems because the postmanipulation of energy for self-replication occurs spontaneously, not autonomously as it occurs in living dissipative thermodynamic systems” [64]. What does he mean by “spontaneously” and “autonomously”? If we could ask a virus, a prion, or even a peptide if it is dead or alive, we are sure that any of them will answer that they are alive, and, in fact, from their own perspective (as internal observers) we may be inert. A cell, an organ, and even a whole plant or animal, including human species are not autonomous. Moreover, nothing in the universe can occur “spontaneously”. Igamberdiev`s model of living systems has many similarities to our Smax/Suniverse model, as both models establish a relationship with thermodynamics, chaos theory, evolution and self-organization. Admittedly, Igamberdiev`s model is mathematically elaborated. Moreover, this model elegantly explains evolution as memory kept inside of a reflective mathematical loop and series. Then, this author emphasizes the intricate interrelation of DNA with structure and function. “The reflecting control in genome is realized by tools (molecular addresses) organizing combinatorial events. Thus, the molecular addresses establish the set of rules for language game corresponding to such hierarchical organization…During this strategy the error-correction is realized, and this takes place in the potential field. We can suppose that the whole organism possesses the ability to forecast the splicing result before is actualized, i.e. it can realize error-correction in the potential field by eliminating wrong potential possibilities, by implicating error-correcting codes. This means that living systems realize computation at quantum level, the process maintaining their dynamic stability at the macroscopic time level” [63]. Huang et al had studied about 12600 genome expressions in the cellular differentiation process of neutrophils, and by means of dynamical approach they have found that a subset of 2773 genes converge in state space to a stable attractor associated with phenotype. According to these researches, “Thus formal network architecture considerations as experimental observations of cell fate behavior also support the idea that the genome-scale regulatory network can act as an integrated entity and give rise to coherent, higher-order dynamic patterns, such as stable high-dimensional attractors” [65]. Regulation can be related to quantum entanglement and “instant communication” [66 – 68]. An EPR (Einstein-Podolsky-Rosen)-pair and entanglement mechanism must also occur inside biological systems, reassuring their stability, and consequently involved in health status. Melkikh has proposed a quantum model computation of biological evolution [25], which despite some criticisms done before, it is very similar to the core of Igamberdiev’s and our models, as he uses quantum mechanics, as well as reduction of Entropy through a “parametric or force control” operating as a “quantum demon”. However, this author does not further elaborate the emergence of such “parametric or force control”, which according to our model, perhaps it is equivalent to Smax of the system. In addition, we explain in the context of Entropy, Information, and Chaos how this Smax might be generated.

In summary

, biological systems can be placed into the Universal Entropy/Information context. Function and structure of systems are ultimately dual. Terms such as life, death, metabolism, evolution, health and disease, then acquire a profound and dynamical significance when are framed by Entropy/Information.

REFERENCES http://drshukla44.wordpress.com/files/2006/12/z_theory_model_biolog_entropy.doc COMMENTS & VIEWS http://einstein.phys.uwm.edu/forum_thread.php?id=5098 http://drshukla44.wordpress.com/2006/12/23/entropy-in-biological-system/#comments

Life and evolution

Looking for “the cause of death” in an autopsy appears a bit odd, as thermodynamically speaking death is a whole process, besides the entire body dies. Death is a dynamical process, therefore cannot be localized. The conjecture here is why doctors, then, do not look for “the cause of life”? Seriously, it will be more beneficial.

As per our conjecture, the basis for what is life may be at the core of the equilibrium between the Smax and Suniverse inside the biological system. Entropy, as a universal principle with different aspects, renders integrity and functionality to all systems, including biological systems. Integrity and functionality are sides of the same coin, ultimately. This approach has many points in common with the universal law “Tao” of Lao-Tzu (400 B.C.) [55]. The Yang and the Ying are comparable to Smax and Suniverse, and similarly to the Second Law of Thermodynamics and Entropy, this can be extended to the whole universe. “Ying exists within Yang; Yang exist within Ying”, is an expression that implies binarity. Lao-Tzu in the opening of his book “Tao Te Jing” says “The Tao that can be told is not the eternal Tao, The name that can be named is not the eternal name”. We assume that this uncertainty of Tao depends on its extreme complexity. Entropy might be dual with the “eternal” Tao. Similarly, at dimensional extremes Entropy also falls into extreme uncertainty. According to Schrödinger’s famous book “What is life” [56], life basically covers two aspects “order to order” and “disorder to order”. “Order to order” is akin to reproduction or replication of the organism. In biological systems usually this is through DNA, which is essentially a macromolecule (polymer) with high binary code capacity. The immense probability of base combinations makes DNA extremely uncertain, therefore likely having high Shannon’s Entropy. Knowing the sequences of DNA, although a breakthrough, may have reduced almost insignificantly such uncertainty as the informational power of this polymer depends vastly more on its functional expressions (variety) than that of its sequence. “Disorder into order”, this means that biological systems can exchange energy and matter with the media. In animals the source is basically matter exchange. We uptake matter with relatively low Entropy, and subsequently the programmed enzymatic metabolism reduces even more the Entropy of this uptake. Metaphorically, animals bite the bits of vegetables, in the sense that animals nourish with the Information taken from the vegetables. Salthe has evoked a similar concept of transference-degradation of energy between “gradients and consumers”, where the consumers are also “gradients” [57]. As an extension of this hypothesis, he has proposed that users of energy do not increase, locally, the overall Entropy of the system; however, they do shape the distribution of Entropy. In this context, Evolution can be conceptualized as introduction of new levels of consumers to optimize the management of Entropy, which ultimately restrain the tendency for dissipation, and simultaneously the Entropy production is smoothed (linearized) and kept under regulation. Salthe metaphorically, concludes, “Evolution, then, is the Universe’s devious route to its own negation” [21]. The main aim of regulatory biological mechanisms is to prevent abrupt changes of entropy among boundaries, and in this manner maintain homeostasis. “The second law implies that the free energy of an isolated system is successively degraded by diabatic processes over time, leading to entropy production…The formulations from classical thermodynamics can be applied to non-equilibrium systems which are not isolated (e.g., Prigogine 1962)… For these systems, the Second law then takes the form of a continuity equation, in which the overall change of entropy of the system dS/dt is determined from the local increase in entropy within the system dSI/dt and the entropy flux convergence dSE/dt (i.e., the net flux of entropy across the system boundary): dS/dt = dSI/dt + dSE/dt…A non-equilibrium system can maintain a state of low entropy by “discarding” high entropy fluxes out of the system” [58]. An elaborated mathematical and statistical form of non-equilibrium Entropy has been proposed [59].

Production of vitamin D, reduction of melanin in skin, and reduction of retinoic acid in retina are few of the reactions in animals that involve direct usage of solar radiation. Contrarily, in vegetables the solar radiation is the main substrate source through the process of photosynthesis. A flaw of anthropomorphism is that DNA is the starting source or origin of life. Contrarily, DNA is just another biological polymer, and as any other polymer in nature it also has Entropy, and, therefore carries information. Besides, Schrödinger’s dichotomization of “what is life” may be arbitrary, since DNA is a molecule produced by the biological metabolism. Therefore, ultimately life can be summarized as just “disorder to order”, and reproduction is an entropic shortcut in this process to reassure preservation of the systems. It should be clear that in thermodynamics and informatics, terms such “order” and “disorder” must be avoided, because they are subjective, and hence entail anthropomorphic biases [1]. Therefore the question as to “what is life?” can be answer as maintaining lower Entropy than that of the environment (Suniverse), and higher entropy than that of Smax, by exchanging matter and/or “energy” with the environment. Aging process can be thermodynamically explained as a progressive increase of Entropy (“error accumulation), and, therefore dissipation of the system. Interestingly, Gladyshev has formulated a similar model using concepts of Gibbs and Helmholtz functions, which are placed in the context of the so-called “law of temporal hierarchies” (Gladyshev’s law) and the so-called “principle of the stabilization of chemical substances” (Gladyshev’s principle) [60]. This model is essentially dual with the Smax/Suniverse presented here, since quantities such as internal energy, enthalpy, Gibbs free energy and Helmholtz free energy (“thermodynamic potentials”), as well as Entropy can be obtained based on statistics arguments. Briefly, in these terms Entropy can be expressed as S = U – F/T, where U is internal energy, F is Helmholtz free energy, and T the absolute temperature, which means, that when F is minimized then S is maximized. Entropy can be also expressed as S = (U + PV) - G/T, where U is internal energy, P is pressure, V is volume, G is Gibbs free energy, and T is the absolute temperature; additionally, PV is work (W) , and (U + PV) is enthalpy (H). Similarly, this means that when G is minimized then S is correspondingly maximized. According to Gladyshev’s model for biological systems G and F are minimized through a hierarchic gradient sequence of environment/system, which apparently is intimately related with the “law of temporal hierarchies”. Specifically, this law establishes that “a biological system consist of the given organism’s cells, the organism itself, and the population formed by these organisms (i.e., fragment of the hierarchic sequence of biological structures). Identifying the average life-span (life time) of structures makes it possible to assert that the average life-span (t) of a cell (cel) in the organism is much less than the average life span of the organism (org), which, in its turn, is much less than the life-span of the population (pop): <<tcel <<torg << tpop <<…” This assertion is a natural fact. For instance, in the intestine, of course, a cell lives less than villi, the villi lives less than the intestine itself, the intestine less than the animal, the animal less than all the animals, all the animals less than the whole ecosystem, the whole ecosystem less than the planet, the planet less than the galaxy, the galaxy less than the universe, etc. Therefore, by itself this “law” apparently does not have too many consequences if it is not place into the context of a higher physical law or principle. This apparently time hierarchy is perhaps the consequence of not accounting a relativistic time frame. Therefore, this may be indirectly an anthropomorphic bias, because this assessment is done through an apparently external observer only. Moreover, these “temporary hierarchies” may vanish if they are considered as eigen (physiological) times, which together with the metabolic rate can be unified using a broad concept of Entropy, which combines the external and internal observations. Perhaps, in biological systems, the trend to minimize G and F may be related to their tendency towards Smax. Thus, this so-called “law of temporal hierarchies” may be likely a consequence of Smax. Regarding the so-called “principle of the stabilization of chemical substances” (Gladyshev’s principle) it can be deduced that the G of simple molecules such as H2, N2, O2, CO2 and H2O is much more compared to the G of macromolecules. In other words, macromolecules (polymers) have more Entropy than that of simple molecules, but correspondingly less Entropy compared to that of their nearby media (Smax). This assertion is another consequence of the Smax/Suniverse model presented here. Subsequently, because macromolecules are more uncertain for an external observer they carry more information than simple molecules. Similarly, Trambouze has compared complexity with information [61]. Again, we think that the relativistic position of the observer, either internal or external, must be better set up. The decreasing complexity, found by this author, is perhaps an internal perspective and, not surprisingly Information (a la Shannon) simultaneously increases because Entropy is being transferred from inside the system to the outside environment, therefore an external observer experiences more uncertainty. Trambouze concludes that “When modifying a system made up of many components, information input can reduce the random complexity of the system and therefore increases its degree of organization…We suggest calling this information, which modifies the complexity of a system, structuring information, because, when introduced into a given system, it enables its structure to be change”. The term “information input” seems to be analogous to the process of energy-driven-Entropy, as Information cannot physically appears out of nothing. If this fact is not accounted, then the fallacy of a priori stability of systems is created (i.e. creationism). Besides, his “structuring information” smells and tastes as “self- organization”. Moreover, this author’s statements that “Spontaneous evolution of a close system if its free energy decreases” and “Any irreversible transformation is accompanied by an increase in entropy” are akin to our above conjecture that in complex systems (biological systems), the trend to minimize G and F may be related to their tendency towards Smax, but the resulting “current Entropy” of the system is continuously less than the Suniverse. Organized and regulated reactions inside complex systems (metabolism) are intimately related to self-organization. Therefore, an aspect of self-organization is likely buffering G (tending to decrease G and H). For instance, metabolism is an orderly succession of reactions and regulations preventing outbursts of energy (heat), and simultaneously preventing extreme gradients of Entropy. Similarly, Chang Ying-Fang has hypothesized that self-organization, through fluctuation and self-interaction, “may form a lower entropy” [62]. Igamberdiev’s concept of “internal quantum sate” (IQS) is essentially similar to state phase invariant(s). IQS behaves as cellular automata, and it is “concatenated within the 3D space as a molecular computer (MC).” Enzymes are MCs operators, while error corrections are effected by RNA (short term) and DNA (long term). The error corrections do not affect the IQS [63]. In the context of Smax/Suniverse model, IQS can be the thermodynamic status that depends on the instant interactions of all invariants. These invariants interaction is in turn focused on fulfilling constrains imposed by the disturbances arising from the media. Besides, such media can be internally related to Smax, and externally related to Suniverse.

Living organism by evolution are thermodynamically open (to matter and energy) systems far from equilibrium. Otherwise life would not be possible. Here, the key is “far from equilibrium” to explain life. How living organisms acquired such property? Chemical reactions naturally can occur “far from equilibrium”. Therefore, when the odds of a natural phenomenon or event (i.e. a macromolecule or biological polymer such a protein or nucleic acid) are statistically or probabilistically analyzed, a reductionist analysis is totally naive. This means just breaking down the biological polymer into the number and different types of moieties, followed just by a conventional probabilistic calculation of the odds of reassembling it exactly as the initial macromolecule. This is not only terribly obsolete, but also misleading. It is like breaking down a cathedral into bricks, counting the bricks, and making a statistical calculation how the bricks can be reassembled by themselves into a cathedral again. Obviously, this approach is incorrect. But there is also another “little” difference, a cathedral is usually built in few years, instead a natural event had already taken about four billions years to happen, and, moreover, with the advantage that energy constantly flowed through the system. Besides, systems cannot be built from nothing. Definitions in the context of complex systems are always incomplete, skewed and biased. After all, when a pizza can be “defined” as a pizza? By the recipe, by the ingredients solely, by the ingredients together, before introducing into the oven, during the cooking, when is out of the oven, or while is being eaten or later? As the case of the pizza, every natural phenomenon implies a continuum process. The term “life” does not have a definition, and it can be just a formality. Any definition of life, unfortunately, falls into the anthropomorphic semantic rhetoric as a “property” of living organisms…and living organisms live as they posses this “property” of life; however as such this “property” remains obscure. On this regard we partially agree with Nasif. However, this author involuntarily also falls into this anthropomorphic trap, because he tries to push a definition of life, which can be considered as a reductionist attempt. Dynamical complex systems are irreducible and impredicative, therefore defined terms cannot be clearly differentiated from defining terms. The term life is just provisional, which implies a summarized conceptualization of many complex functions. Contrarily, we conceptualized but not define life. Nasif appeals a great deal to energy disregarding the concept of Entropy, and also dichotomizes considerably energy and matter. Besides, for this author the quality of life resides at the cytoplasm of cells related to chemical reductions. We consider life as a whole process that cannot be related to any particular locus, molecule or process, otherwise it will fall again into anthropomorphic terms. In this context the same author says, “… complex self-replication pattern can occur in inert self-replicating templates, for example in prions, viruses, autocatalytic proteins and ribozymes, and all of these structures cannot be considered as living dissipative thermodynamic systems because the postmanipulation of energy for self-replication occurs spontaneously, not autonomously as it occurs in living dissipative thermodynamic systems” [64]. What does he mean by “spontaneously” and “autonomously”? If we could ask a virus, a prion, or even a peptide if it is dead or alive, we are sure that any of them will answer that they are alive, and, in fact, from their own perspective (as internal observers) we may be inert. A cell, an organ, and even a whole plant or animal, including human species are not autonomous. Moreover, nothing in the universe can occur “spontaneously”. Igamberdiev`s model of living systems has many similarities to our Smax/Suniverse model, as both models establish a relationship with thermodynamics, chaos theory, evolution and self-organization. Admittedly, Igamberdiev`s model is mathematically elaborated. Moreover, this model elegantly explains evolution as memory kept inside of a reflective mathematical loop and series. Then, this author emphasizes the intricate interrelation of DNA with structure and function. “The reflecting control in genome is realized by tools (molecular addresses) organizing combinatorial events. Thus, the molecular addresses establish the set of rules for language game corresponding to such hierarchical organization…During this strategy the error-correction is realized, and this takes place in the potential field. We can suppose that the whole organism possesses the ability to forecast the splicing result before is actualized, i.e. it can realize error-correction in the potential field by eliminating wrong potential possibilities, by implicating error-correcting codes. This means that living systems realize computation at quantum level, the process maintaining their dynamic stability at the macroscopic time level” [63]. Huang et al had studied about 12600 genome expressions in the cellular differentiation process of neutrophils, and by means of dynamical approach they have found that a subset of 2773 genes converge in state space to a stable attractor associated with phenotype. According to these researches, “Thus formal network architecture considerations as experimental observations of cell fate behavior also support the idea that the genome-scale regulatory network can act as an integrated entity and give rise to coherent, higher-order dynamic patterns, such as stable high-dimensional attractors” [65]. Regulation can be related to quantum entanglement and “instant communication” [66 – 68]. An EPR (Einstein-Podolsky-Rosen)-pair and entanglement mechanism must also occur inside biological systems, reassuring their stability, and consequently involved in health status. Melkikh has proposed a quantum model computation of biological evolution [25], which despite some criticisms done before, it is very similar to the core of Igamberdiev’s and our models, as he uses quantum mechanics, as well as reduction of Entropy through a “parametric or force control” operating as a “quantum demon”. However, this author does not further elaborate the emergence of such “parametric or force control”, which according to our model, perhaps it is equivalent to Smax of the system. In addition, we explain in the context of Entropy, Information, and Chaos how this Smax might be generated.

IN SUMMARY, biological systems can be placed into the Universal Entropy/Information context. Function and structure of systems are ultimately dual. Terms such as life, death, metabolism, evolution, health and disease, then acquire a profound and dynamical significance when are framed by Entropy/Information.

Comments in Response to the deletion notesThe concept of entropy to explain the unexplained happenings in medicine and in a new way is well written and is referred well written articles of scientifc and philosophical importance from Noble laurets. Similar article or topic has not been even been touched in wikipidia, that justifies that this should be left intact, may be the references may be removed seperately with a link rather than deleting the whole article which will be a loss of knowledge and spread of knowledge. I dont think that this should be deleted at all.