Application Of Thermodynamics In Biological System Pdf

[Hilma Kolin]

Biologicalthermodynamics (Thermodynamics of biological systems) is a science that explains the nature and general laws of thermodynamic processes occurring in living organisms as nonequilibrium thermodynamic systems that convert the energy of the Sun and food into other types of energy. The nonequilibrium thermodynamic state of living organisms is ensured by the continuous alternation of cycles of controlled biochemical reactions, accompanied by the release and absorption of energy, which provides them with the properties of phenotypic adaptation and a number of others.

In 1935, the first scientific work devoted to the thermodynamics of biological systems was published - the book of the Hungarian-Russian theoretical biologist Erwin S. Bauer (1890-1938) "Theoretical Biology".[1] E. Bauer formulated the "Universal Law of Biology" in the following edition: "All and only living systems are never in equilibrium and perform constant work at the expense of their free energy against the equilibrium required by the laws of physics and chemistry under existing external conditions". This law can be considered the 1st law of thermodynamics of biological systems.

In 2006, the Israeli-Russian scientist Boris Dobroborsky (1945) published the book "Thermodynamics of Biological Systems",[3] in which the general principles of functioning of living organisms from the perspective of nonequilibrium thermodynamics were formulated for the first time and the nature and properties of their basic physiological functions were explained.

A living organism is a thermodynamic system of an active type (in which energy transformations occur), striving for a stable nonequilibrium thermodynamic state. The nonequilibrium thermodynamic state in plants is achieved by continuous alternation of phases of solar energy consumption as a result of photosynthesis and subsequent biochemical reactions, as a result of which adenosine triphosphate (ATP) is synthesized in the daytime, and the subsequent release of energy during the splitting of ATP mainly in the dark. Thus, one of the conditions for the existence of life on Earth is the alternation of light and dark time of day.

In animals, the processes of alternating cycles of biochemical reactions of ATP synthesis and cleavage occur automatically. Moreover, the processes of alternating cycles of biochemical reactions at the levels of organs, systems and the whole organism, for example, respiration, heart contractions and others occur with different periods and externally manifest themselves in the form of biorhythms. At the same time, the stability of the nonequilibrium thermodynamic state, optimal under certain conditions of vital activity, is provided by feedback systems through the regulation of biochemical reactions in accordance with the Lyapunov stability theory. This principle of vital activity was formulated by B. Dobroborsky in the form of the 2nd law of thermodynamics of biological systems in the following wording:

The stability of the nonequilibrium thermodynamic state of biological systems is ensured by the continuous alternation of phases of energy consumption and release through controlled reactions of synthesis and cleavage of ATP.

1. In living organisms, no process can occur continuously, but must alternate with the opposite direction: inhalation with exhalation, work with rest, wakefulness with sleep, synthesis with cleavage, etc.

2. The state of a living organism is never static, and all its physiological and energy parameters are always in a state of continuous fluctuations relative to the average values both in frequency and amplitude.

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We developed a model to represent the time evolution phenomena of life through physics constraints. To do this, we took into account that living organisms are open systems that exchange messages through intracellular communication, intercellular communication and sensory systems, and introduced the concept of a message force field. As a result, we showed that the maximum entropy generation principle is valid in time evolution. Then, in order to explain life phenomena based on this principle, we modelled the living system as a nonlinear oscillator coupled by a message and derived the governing equations. The governing equations consist of two laws: one states that the systems are synchronized when the variation of the natural frequencies between them is small or the coupling strength through the message is sufficiently large, and the other states that the synchronization is broken by the proliferation of biological systems. Next, to simulate the phenomena using data obtained from observations of the temporal evolution of life, we developed an inference model that combines physics constraints and a discrete surrogate model using category theory, and simulated the phenomenon of early embryogenesis using this inference model. The results show that symmetry creation and breaking based on message force fields can be widely used to model life phenomena.

The challenges facing humanity are related to complex systems such as human, social and ecological systems. A fundamental role of science is to predict the behavior of complex systems and to prevent problems from occurring. In biology, individual phenomena have been explained by causal mechanisms rather than by the governing equations of physics1,2,3. On the other hand, advances in machine learning techniques and computing power have made it possible to construct surrogate models that mimic the temporal evolution of biological phenomena from large amounts of data4. However, most of the surrogate models used in biomedical sciences are black box models, which means that the reproducibility and reliability of the models are affected by the bias of the data used for training5. To overcome this problem, it is necessary to incorporate physics-based constraints into the surrogate model in addition to the mechanistic constraints that have been identified in previous empirical studies in biology and medicine.

Boltzmann adopted the model of statistical mechanics, in which the system is composed of a vast number of moving particles, and explained the second law of thermodynamics in the form that the system moves toward a disordered state described by the largest number of microscopic states with the highest possible probability6. In the modern synthesis established in the 1940s, this Boltzmann interpretation and the spontaneous creation of order observed in biological evolution were considered inconsistent, and mechanistic rather than physics-based constraints were adopted as a way to explain biological phenomena7. In contrast, Schrdinger8 and Bertalanffy9 argued that the transformation from disorder to order, as observed in biological systems, does not violate the second law of thermodynamics as long as such systems produce enough entropy to compensate for their own internal entropy reduction. Prigogine showed that entropy production in a system is minimized in a nonequilibrium steady state with constant total thermodynamic quantities10.

Organisms are open systems driven far from thermodynamic equilibrium. Jarzynski extended Clausius' inequality to conditions far from equilibrium11. Crooks characterized the probability of breaking the second law by the fluctuation theorem12. England presented thermodynamic constraints on the behavior of systems far from equilibrium. This is the principle that when energy is poured into a system from outside in the presence of thermal fluctuations, most of the changes in the system are random, but irreversible changes occur when the system more efficiently absorbs and dissipates free energy. England called this principle dissipative adaptation13. These three principles provided a unified explanation for the nonequilibrium phenomena expressed in the particle model.

However, as long as we use models of particles without interactions, the physics-based constraints cannot be fully incorporated into the description of life phenomena. This is because biological behavior is signal-based. Biological systems consist of numerous hierarchies, from cells to ecosystems, and a high degree of coordination is observed3,14. Such coordination is possible because of the diverse messages exchanged at all levels of the biological hierarchy14. Messages generated by biological systems include chemical, visual, and auditory information. Messages consisting of chemical substances include intracellular signaling molecules, intercellular signaling molecules, and molecules exchanged between individual organisms. The colors and shapes of living organisms become visual information through light. The physical vibrations emitted by living organisms become auditory information through sound. Until now, messages exchanged between biological systems have been explained by mechanistic constraints on how they are transmitted and how that information is processed.

When spontaneous motion occurs in nature, it manifests itself as periodic motion15. Oscillators have the property of synchronizing when they are connected by weak interactions. Messages exchanged in biological systems induce synchronization between systems by changing the state of the system receiving them. The steady state generated by synchronization between oscillators is broken by the disappearance of signaling. We show in this paper that the temporal evolution of biological systems can be described by physics-based constraints by positing oscillators as the basic dynamic unit and modeling the different patterns of self-organization as caused by synchronization of systems by messages or desynchronization by fluctuations.

Biological systems can be represented by three different models. In biology and medicine, living organisms are represented by types and functions1. In the surrogate model, the state of the system under consideration is represented by a vector at a single point in space as a set of variables16. The temporal evolution of the state is represented by the trajectory of a point in space that moves with the evolution of time17. The space that is a collection of states is called a phase space or state space18. In dynamics, trajectories on phase space are considered to be attracted to a certain collection of states. Such a region is called an attractor. The behavior of the attractor states governs the steady-state characteristics of the system. We show in this paper that category theory allows us to synthesize three models of biological systems described from different perspectives.

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