On a generalized Levinthal's paradox: The role of long- and short range interactions in complex bio-molecular reactions, including protein and DNA folding

Unsolved morphogenesis problems and the hidden order

In this work, the morphogenesis mechanisms are considered from the complexity perspective. It is shown that both morphogenesis and the functioning of organs should be unstable in the case of short-range interaction potentials. The repeatability of forms during evolution is a strong argument for its directionality. The formation of organs during evolution can occur only in the presence of a priori information about the structure of such an organ. The focus of the discussion is not merely on constraining potential possibilities but on the concept of directed evolution itself. A morphogenesis model was constructed based on nontrivial quantum effects. These interaction effects between biologically important molecules ensure the accurate synthesis of cells, tissues, and organs.

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Thinking, holograms, and the quantum brain

This article continues the development of the idea that all human behavior and thinking are innate. A model of thinking and functioning of the brain has been constructed, which is capable of explaining both the accuracy of molecular processes and the innateness of behaviors. The focus of the model is the phase of the wave function of the particle, which is an additional (free) parameter. It should also be emphasized that the phase of the wave function of a particle is inextricably linked with the quantum action S in the Feynman's formulation of quantum mechanics (path integrals). A hypothesis is proposed: the set of particles that make up neurons and the brain is controlled by changing the phases from the outside (by a higher order system). Such a control system must be outside our world because our measurement methods do not allow us to determine the phase of an elementary particle. In a sense, it can be viewed as an extension of Bohm's ideas about the holographic brain and the holographic universe. Experiments are proposed that could confirm or disprove this model.

Mechanisms and models of movement of protocells and bacteria in the early stages of evolution

A review of the physicochemical models of the movement of protocells and bacteria was performed. The mechanisms of gliding and movement based on flagella are considered. Based on the models, the average speed of movement of protocells and bacteria was calculated. A physicochemical model of bacterial gliding was constructed. The efficiency of the process of converting the energy of ATP into the energy of motion is estimated. A review of models of movement with the help of flagella was performed. A model has been constructed for converting ATP energy into proton and sodium motive forces, which, in turn, are converted into energy of rotor rotation. The problem of the accuracy of operation of nanomachines, on the basis of which the directed movement of bacteria occurs, is discussed. The considered models can be applied to create nanomotors for medical purposes.

Viruses, immunity and evolution

In this article, the evolution of viruses is analyzed in terms of their complexity. It is shown that the evolution of viruses is a partially directed process. The participation of viruses and mobile genetic elements in the evolution of other organisms by integration into the genome is also an a priori directed process. The high variability of genomes (including the genes of antibodies), which differs by orders of magnitude for various viruses and their hosts, is not a random process but is the result of the action of a molecular genetic control system. Herein, a model of partially directed evolution of viruses is proposed. Throughout the life cycle of viruses, there is an interaction of complex biologically important molecules that cannot be explained on the basis of classic laws. The interaction of a virus with a cell is essentially a quantum event, including selective long-range action. Such an interaction can be interpreted as the "remote key-lock" principle. In this article, a model of the interaction of biologically important viral molecules with cellular molecules based on nontrivial quantum interactions is proposed. Experiments to test the model are also proposed.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

From leaves to roots: Biophysical models of transport of substances in plants

The transport processes of substances in various plant tissues are extremely diverse. However, models aimed at elucidating the mechanisms of such processes are almost absent in the literature. A unified view of all these transport processes is necessary, considering the laws of statistical physics and thermodynamics. A model of active ion transport was constructed based on the laws of statistical physics. Using this model, we traced the entire pathway of substances and energy in a plant. The pathway included aspects of the production of energy in the process of photosynthesis, consumption of energy to obtain nutrients from the soil, transport of such substances to the main organelles of all types of plant cells, the rise of water with dissolved substances along the trunk to the leaves, and the evaporation of water, accompanied by a change in the percentage of isotopes caused by different rates of evaporation. Models of ion transport in the chloroplasts and mitochondria of plant cells have been constructed. A generalized model comprising plant cells and their vacuoles was analyzed. A model of the transport of substances in the roots of plants was also developed. Based on this model, the problem of transport of substances in tall trees has been considered. The calculated concentrations of ions in the vacuoles of cells and resting potentials agreed well with the experimental data.

The Impact of Redox, Hydrolysis and Dehydration Chemistry on the Structural and Magnetic Properties of Magnetoferritin Prepared in Variable Thermal Conditions

Ferritin, a spherically shaped protein complex, is responsible for iron storage in bacteria, plants, animals, and humans. Various ferritin iron core compositions in organisms are associated with specific living requirements, health state, and different biochemical roles of ferritin isomers. Magnetoferritin, a synthetic ferritin derivative, serves as an artificial model system of unusual iron phase structures found in humans. We present the results of a complex structural study of magnetoferritins prepared by controlled in vitro synthesis. Using various complementary methods, it was observed that manipulation of the synthesis technology can improve the physicochemical parameters of the system, which is useful in applications. Thus, a higher synthesis temperature leads to an increase in magnetization due to the formation of the magnetite phase. An increase in the iron loading factor has a more pronounced impact on the protein shell structure in comparison with the pH of the aqueous medium. On the other hand, a higher loading factor at physiological temperature enhances the formation of an amorphous phase instead of magnetite crystallization. It was confirmed that the iron-overloading effect alone (observed during pathological events) cannot contribute to the formation of magnetite.

The Brain and the New Foundations of Mathematics

Many concepts in mathematics are not fully defined, and their properties are implicit, which leads to paradoxes. New foundations of mathematics were formulated based on the concept of innate programs of behavior and thinking. The basic axiom of mathematics is proposed, according to which any mathematical object has a physical carrier. This carrier can store and process only a finite amount of information. As a result of the D-procedure (encoding of any mathematical objects and operations on them in the form of qubits), a mathematical object is digitized. As a consequence, the basis of mathematics is the interaction of brain qubits, which can only implement arithmetic operations on numbers. A proof in mathematics is an algorithm for finding the correct statement from a list of already-existing statements. Some mathematical paradoxes (e.g., Banach–Tarski and Russell) and Smale’s 18th problem are solved by means of the D-procedure. The axiom of choice is a consequence of the equivalence of physical states, the choice among which can be made randomly. The proposed mathematics is constructive in the sense that any mathematical object exists if it is physically realized. The consistency of mathematics is due to directed evolution, which results in effective structures. Computing with qubits is based on the nontrivial quantum effects of biologically important molecules in neurons and the brain.

An investigation into the energy transfer efficiency of a two-pigment photosynthetic system using a macroscopic quantum model

Despite several different measures of efficiency that are applicable to the photosynthetic systems, a precise degree of efficiency of these systems is not completely determined. Introducing an efficient model for the dynamics of light-harvesting complexes in biological environments is a major purpose in investigating such systems. Here, we investigate the effect of macroscopic quantum behavior of a system of two pigments on the transport phenomena in this system model which interacts with an oscillating environment. We use the second-order perturbation theory to calculate the time-dependent population of excitonic states of a two-dimensional Hamiltonian using a non-master equation approach. Our results demonstrate that the quantum efficiency is robust with respect to the macroscopicity parameter h˜ solely, but the ratio of macroscopicity over the pigment-pigment interaction energy can be considered as a parameter that may control the energy transfer efficiency at a given time. So, the dynamical behavior and the quantum efficiency of the supposed photosynthetic system may be influenced by a change in the macroscopic behavior of the system.

Intra- and intercellular transport of substances: Models and mechanisms

Non-equilibrium-statistical models of intracellular transport are built. The most significant features of these models are microscopic reversibility and the explicit considerations of the driving forces of the process - the ATP-ADP chemical potential difference. In this paper, water transport using contractile vacuoles, the transport and assembly of microtubules and microfilaments, the protein distribution within a cell, the transport of neurotransmitters from the synaptic cleft and the transport of substances between cells using plasmodesmata are discussed. Endocytosis and phagocytosis models are considered, and transport tasks and information transfer mechanisms inside the cell are explored. Based on an analysis of chloroplast movement, it was concluded that they have a complicated method of influencing each other in the course of their movements. The role of quantum effects in sorting and control transport mechanisms is also discussed. It is likely that quantum effects play a large role in these processes, otherwise reliable molecular recognition would be impossible, which would lead to very low intracellular transport efficiency.

Fundamental asymmetry of insertions and deletions in genomes size evolution

The origin of large genomes that underlies the long standing "C-value enigma" is only partially explained by selfish DNA. We investigated insertions and deletions (indels) of nucleotides and discussed their relevance in size evolution of random biological sequences (RBS) and genomes. By developing a probabilistic model of RBS based on size evolution of expandable sites in a thought perfect genome, it was found that insertion bias engenders exponential increase of average RBS sizes. When combined with existing large segments of genome that are not subject to selection pressure (e.g. selfish DNA), such insertion bias results in explosive expansion of genomes, and therefore helps explain the "C value enigma" besides selfish DNA. Such increase of RBS size is caused by the fundamental asymmetry of indels, with insertions result in more available sites and deletions result in less deletable nucleotides. In qualitative agreement with the size distribution of known genomes, tails of RBS size distributions exhibit exponential decay with probabilities of larger RBS segments being smaller. Unsurprisingly, a slight deletion bias (higher deletions probabilities) results in a slow decrease of average RBS size and may lead to their eventual vanishing. Contrary to intuition, strictly balanced insertion and deletion results in linearly increasing instead of completely fixed RBS size. Nonetheless, such slow linear increase of average RBS sizes with time are small in magnitude and are consequently not influential on genome size evolution, and certainly not a major contributor for the "C-value enigma". Our model suggested that insertion bias of nucleotides may provide complementary explanation for large genomes besides selfish DNA. The fundamental indel asymmetry is applicable for all forms of genomic insertions and deletions. Long-lasting exponential increase of genome size present energy and material requirement that is impossible to sustain. We therefore concluded that if there were explosively accelerating expansion caused by significant effective insertion bias for any survival species, it must have occurred sporadically. Our model also provided an explanation for the observed proportional evolution of genome size.

The N-space Episenome unifies cellular information space-time within cognition-based evolution

Self-referential cellular homeostasis is maintained by the measured assessment of both internal status and external conditions based within an integrated cellular information field. This cellular field attachment to biologic information space-time coordinates environmental inputs by connecting the cellular senome, as the sum of the sensory experiences of the cell, with its genome and epigenome. In multicellular organisms, individual cellular information fields aggregate into a collective information architectural matrix, termed a N-space Episenome, that enables mutualized organism-wide information management. It is hypothesized that biological organization represents a dual heritable system constituted by both its biological materiality and a conjoining N-space Episenome. It is further proposed that morphogenesis derives from reciprocations between these inter-related facets to yield coordinated multicellular growth and development. The N-space Episenome is conceived as a whole cell informational projection that is heritable, transferable via cell division and essential for the synchronous integration of the diverse self-referential cells that constitute holobionts.

New insights into the physical processes that underpin cell division and the emergence of different cellular and multicellular structures

Despite decades of focused research, a detailed understanding of the fundamental physical processes that underpin biological systems (structures and processes) remains an open challenge. Within the present paper we report on biomimetic studies, which offer new insights into the process of cell division and the emergence of different cellular and multicellular structures. Experimental studies specifically investigated the impact of including different concentrations of charged bio-molecules (cytokinin and gibberellic acid) on the growth of BaCO₃-SiO₂ based structures. Results highlighted the role of charge density on the emergence of long-range order, underpinned by a negentropic process. This included the growth of synthetic cell-like structures, with the intrinsic capacity to divide and change morphology at cellular and multicellular scales. Detailed study of dividing structures supports a hypothesis that cell division is dependent on the establishment of a charge-induced macroscopic quantum potential and cell-scale quantum coherence, which allows a description in terms of a macroscopic Schrödinger-like equation, based on a constant different from the Planck constant. Whilst the system does not reflect full correspondence with standard quantum mechanics, many of the phenomena that we typically associate with such a system are recovered. In addition to phenomena normally associated with the Schrödinger equation, we also unexpectedly report on the emergence of intrinsic spin as a macroscopic quantum phenomena, whose origins we account for within a four-dimensional fractal space-time and a macroscopic Pauli equation, which represents the non-relativistic limit of the Dirac equation.

Protocells and LUCA: Transport of substances from first physicochemical principles

Models of the transport of substances in protocells are considered from first physicochemical principles. Functional similarities and differences in the transport systems of archaea, cyanobacteria, E. coli, and diatoms have been analyzed. Based on the selection of the most important transport systems, a model of transport of substances through the membrane of the last universal common ancestor, LUCA, was constructed. Models of isotope separation in protocells were considered. Based on the proposed models, the difference in isotope concentrations in rocks can be predicted, which can serve as an indicator of the presence of life in the early stages of evolution. Mechanisms of energy conversion for the simplest forms of directed motion in protocells are considered. A special stage in the evolution of protocells is proposed - the minimal mobile cell.