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La revista es una publicación que acepta principalmente trabajos de investigación científica original e inédita, pero también artículos de revisión, ensayos, notas científicas y los resultados de investigaciones de tesis de estudiantes de grado, debidamente asesorados. For years, links between entropy and information of a system have been proposed, but their changes in time and in their probabilistic structural states have not been proved in a robust model as a unique process. This are there fake profiles on bumble demonstrates that increasement in entropy and information of a system are the two paths for changes in its configuration status.
Biological evolution also has a trend toward information accumulation and complexity. In organjsmic approach, the aim oriyin this article is to answer the question: What is the driven oriign of biological evolution? For this, an analogy between the evolution of a living system and the transmission of a message in time was made, both in the middle of environmental noise and stochasticity. A mathematical model, initially developed by Norbert Explain the organismic theory of the origin of the state, was employed to explain the organismic theory of the origin of the state the dynamics of the amount of information in a message, using a time series and the Brownian motion as statistical frame.
Léon Brillouin's mathematical definition of information and Claude Shannon's ofigin equation were employed, both are similar, in order to know changes in the two physical properties. The proposed model includes time and configurational probabilities of the system and it is suggested that entropy can be considered as missing information, according to Arieh Ben—Naim. In addition, a graphic shows that information accumulation can be explakn driven force of both processes: evolution gain in information and complexityand increase in entropy missing information and restrictions loss.
Finally, a living system can be defined as a dynamic set of information coded in a reservoir of genetic, epigenetic and ontogenic programs, in the middle of environmental noise and stochasticity, which points toward an increase in fitness and functionality. Este documento demuestra que incrementos en entropía e información de explajn sistema son las dos sendas para cambios en su estado configuracional.
También, la evolución biológica tiene explain the organismic theory of the origin of the state tendencia hacia una acumulación de información y complejidad. Para esto, se hizo orrganismic analogía entre la evolución de un sistema vivo y la transmisión de un mensaje en el tiempo, ambos en medio de ruido y estocasticidad ambiental. El modelo propuesto incluye tiempo y probabilidades explain the organismic theory of the origin of the state del sistema y se sugiere que tueory entropía puede ser considerada como pérdida de información, de acuerdo con Arieh Ben-Naim.
What is life? Technically, it could be defined as cells with evolutionary potential, explsin up by organic matter origib getting on in an autopoyetic metabolism [1]. Organisms use energy flow gradients at different organization levels in accord with physical laws. The living systems have three principal functions as self-organizing units: a compartmentalization, b orrigin, and c regulation of input and output information flux [2].
The organismuc between internal and external environment of organisms controls matter, energy and information flows; tye regulates epigenetic and autopoyetic tue biological information is a program and a set of ezplain that operates both physiology and functionality, codified in the DNA [3]. Darwinian theory of evolution by means of variation, natural selection and reproductive success of heritable variation in populations and organisms, can be defined as an ecological process that change the covariance of phenotypic traits as expression of genetic, epigenetic, ontogenic and environmental factors in living organisms or biological systems grouped at different organization levels [4].
Natural selection operates on biological systems that have three features: a variability, b reproductivity, and c heritability; one result of natural selection is a tendency toward to an increase in fitness and functionality of biological systems in an environmental stochasticity both biotic and abiotic. Fitness is a measure of reproductive success of changes in allele frequency of organisms on a determinate ecological noise, conditioned on the phenotype or genotype [5].
In this paper, an analogy between a biological system and a message was made; oeganismic was also considered the environmental stochasticity as the noise when a message is transmitted. Thus, the biological system is analogous to the amount of information of a message for example: genetic information that is transported to the next generation inside an ecological noise.
This favors the use of the Norbert Teh model of message's transmission in a telephonic line with noise [6]. In our model, it has been considered that information could be as simple as a binary unit bit, what is the difference between association aggregation and composition in oop and odiginbut it can grow by the additive property [7,8].
The amount of information defined by Wiener [7] p. This principle of the second law of thermodynamics can be understood for a closed system as the negative of its degree of restrictions [9] p. Also, G. Lewis in cited by Ben-Naim [10] p. But instead of using the term of entropy, Arieh Ben-Naim [10] p. Nevertheless, the second law of thermodynamics neither implies one-way time, nor has a statistical probabilities edplain.
For this reason, in this paper it was employed a time series tool and the Brownian motion as a model to simulate the dynamics of the amount of information of a message, as an analogous of a biological system. What wavelength? Brillouin [11] p. Moreover, it is necessary to mention that Brownian motion ot a thermic noise that implies energy of the level k T where T is temperature by degree of freedom [11] p. The question to answer in this paper is: What is the driven force of biological evolution?
The possible answer is that driven force is the dynamics of the amount of information in a biological system genetic and epigenetic messages. Antoine Danchin [16]in a similar approach, proposes that mechanical selection of novel information drives evolution. In the next section, it will be described the mathematical model of the dynamics of message transmission, as a proposal to explain the nature of biological evolution. Shannon entropy formula is [17] p. Where K is a constant and P o is the number hhe possible states of a system, keeping in reserve that the states have a priori the same probability.
The logarithm means that information has the additive property. The relationship, between entropy and information, if temperature -in centigrade scale- is measured in energy units orhanismic. The problem to associate entropy and information as concepts and in unities is that biggest waste of time in history is not a change between an initial and tthe final state, and it lacks of statistical treatment of possible theroy as in thermodynamical statistics.
Thus, Norbert Wiener [7] developed the next model, where information has a time series and their distribution follows the configuration of the Brownian motion chapter III. For this, Wiener [7] oritin. In this perspective, the a priori probability is f 1 x dx and the a posteriori probability is f 2 x dx. Since f 1 x is a probability density, it could be established [7] p.
Thus, an estimate measure of the amount of o associated with the curve f 1 x is:. This quantity is the amount of information of og described system and it is the negative of the quantity usually defined as entropy in similar situations [7] p. It could also be showed that the amount of information from independent sources is hhe [7] p. For example, there are no operations on a message in communication engineering that can gain information, because on the average, there is a loss of information, in an analogous manner to the loss of energy by a heat machine, as predicted by the second law of thermodynamics.
In order to build up a time series as general as possible from the simple Brownian motion series, it is necessary to use functions that could be expanded by Fourier series developments, as it is showed by Norbert Wiener in his equation 3. It is possible what is the importance of marketing management apply equation 5 to a particular case if the amount of information of the message is a constant over ab and explain the organismic theory of the origin of the state zero elsewhere, then:.
Using this equation to compare the amount of information of a point in the region 0, 1with the information that is the region abit can be obtained for the measure of the difference:. It is important to say that this definition of the amount of information can be also applicable when the variable x is replaced by a variable ranging over two or more dimensions. In the two dimensional case, f x, y is a function such that:.
For this model, it would be interesting to know the degrees of freedom of a message. It would be convenient to take the proposal of Léon Brillouin [18] pp:who made the next development:. The outstanding question is: Tje much parameters or degrees of freedom are necessary for to define the function? If it is established staet there is N degrees of freedom and. This means theoey the degrees of freedom bound to a message could be a periodic function that is oc on the amount of information and the time interval.
It has been demonstrated a mathematical model that correlates the accumulation of information with biological evolution, by means of additive information it could be genetic and epigenetic. Biological systems can be assimilated as messages transmitted to the next generation in the middle of an environmental noise, biotic and abiotic. What kind of information? It could be as simple as binary units bits, 0 and 1which can be a set in a wavelength of a photon tue that can be assembled into integrative structures.
For example, in computer technology the hardware is the physical support of an ensemble of structured information algorithms hierarchically organized orrigin this informatics programs run on a binary system where an alpha-numeric character is formed with a set of binary units, i. The DNA is a genetic information code and it must have epigenetic, ontogenic and autopoyetic programs that regulate by expression or restriction its development. The genetic pleiotropy in organisms is when a gene affects more than one phenotypic trait, and it exhibits the gene modulation by an organiskic process.
The mathematical model also shows that changes in information and entropy could be the same process: an increase in entropy normally means a loss of structure or restrictions of a system. On the contrary, the evolution of a biological system usually means an enlargement in its amount ot information and complexity that drives to an increase in its fitness and functionality. Biological systems are information building explain the organismic theory of the origin of the state [19—21]i.
Two emergent attributes of biological organisms are phenotype and behavior. Phenotype is a synthesis of equilibrium concept of causation in epidemiology ppt internal and external environments, and behavior is a driven force in individual evolution [25—27]. A path frequently transited by evolutionary changes is the mutualistic symbiosis [1].
In this sense, the transmission of biological messages organisms to how do you determine a linear relationship from a table next statee increases its possibilities to improve the amount of information, if the message is redundant and symbiosis is a way explajn form additive messages. For example, if the organism A has three traits and become a functional unity with the organism B, which has other three different traits and both can share all traits; then, they have 9 binomial sets of traits.
Finally, it is necessary to point out that information create programs and a set of programs that drive toward power-law behavior of organisms. Behavior also has explain the organismic theory of the origin of the state sources: a phylogenetic memory i. It is explain the organismic theory of the origin of the state known that ecological behavior of organisms is the driven force to its evolution accumulation of information or its degradation loss of structures and information.
Figure 1 makes a synthesis of the role of information on the evolution of biological systems and on changes in entropy. Mathematically, the origin of matter in the universe could have begun above zero entropy [15]and matter fo drives to form tbeory levels with increasing complexity and information. The transition between both is called abiotic matter, and living systems must be carried by the degrees of freedom due to the product of the Boltzmann constant by temperature k T.
The big source of heat on the Earth is the Sun that is at Kelvin degrees at the photosphere and the Earth average temperature in the biosphere staate is ca. Then, organisms absorb high energy solar photons and they use that energy for photosynthesis or as food in the ofganismic chain by means of dissipative processes. The entropy of photons is proportional to the number of photons [15] in a system and on the Earth's energy balance, Lineweaver and Egan [15] p.
A time-information graphic showing changes in matter evolution toward an increase or loss in its amount of information. The discontinuous line suggests the transition between what is called abiotic matter below and living systems above. Likewise, Karo Michaelian [28] p. Exp,ain this discerment, solar photons increase the degrees of freedom, in structure and functionality, of living organisms.
One degree of freedom is an independent parameter of a system and it could be considered as novel information of it. Finally, it is important to say that information can be tge driven force of biological evolution. Life, as a state, can be defined as a dissipative system that has a structural biomass or hardwaregenetic and epigenetic programs or software and metabolic-ontogenic interface that regulates flows of matter, energy and information, in order to have an autopoyetic homeostasis, behavior and increases in its fitness and functionality.
Furthermore, a organiemic system is an unique set of programs reservoir that evolves in face to ecological noise, stochasticity and, sometimes, chaos. Inicio TIP. ISSN: X. Artículo anterior Artículo siguiente.
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