Perception of numerousness, i.e. number of items in a set, is an important cognitive ability, which is present in several animal taxa. In spite of obvious differences in neuroanatomy, insects, fishes, reptiles, birds, and mammals all possess a "number sense". Furthermore, information regarding numbers can belong to different sensory modalities: animals can estimate a number of visual items, a number of tones, or a number of their own movements. Given both the heterogeneity of stimuli and of the brains processing these stimuli, it is hard to imagine that number cognition can be traced back to the same evolutionary conserved neural pathway. However, neurons that selectively respond to the number of stimuli have been described in higher-order integration brain centres both in primates and in birds, two evolutionary distant groups. Although most probably not of the same evolutionary origin, these number neurons share remarkable similarities in their response properties. Instead of homology, this similarity might result from computational advantages of the underlying coding mechanism. This means that one might expect numerousness information to undergo similar steps of neural processing even in evolutionary distant neural pathways. Following this logic, in this review we summarize our current knowledge of how numerousness is processed in the brain from sensory input to coding of abstract information in the higher-order integration centres. We also propose a list of key open questions that might promote future research on number cognition.

In this article, we introduce the new mathematical concept of circular mixed sets of words over an arbitrary finite alphabet. These circular mixed sets may not be codes in the classical sense and hence allow a higher amount of information to be encoded. After describing their basic properties, we generalize a recent graph theoretical approach for circularity and apply it to distinguish codes from sets (i.e. non-codes). Moreover, several methods are given to construct circular mixed sets. Finally, this approach allows us to propose a new evolution model of the present genetic code that could have evolved from a dinucleotide world to a trinucleotide world via circular mixed sets of dinucleotides and trinucleotides.

The universal phenomenon of evolution consists of change after change in flow configuration in a time direction that is perceptible to the observer. This reality clashes with the doctrine of precise optima, minima, and maxima, now rigidly in place because of calculus and computational simulations of all kinds of flowing and changing configurations. With two dissimilar examples, access on an area (a human settlement) and along a line (animal locomotion), it is shown that even a 1-percent imperfection in performance is accompanied by a sizable bandwidth of freedom to hit the 'target', that is, an easily accessible design with close to perfect performance. The evolutionary designs reveal the physics behind the phenomenon of diminishing returns in the vicinity of the mathematical optimum. In evolution what works is kept.

Proteinoids, or thermal proteins, are inorganic entities formed by heating amino acids to their melting point and commencing polymerisation to form polymeric chains. Typically, their diameters range from 1μm to 10μm. Some amino acids incorporated into proteinoid chains are more hydrophobic than others, leading proteinoids to cluster together when they are present in aqueous solutions at specific concentrations, allowing them to grow into microspheres. The peculiar structure of proteinoids composed of linked amino acids endows them with unique properties, including action-potential like spiking of electrical potential. These unique properties make ensembles of proteinoid microspheres a promising substrate for designing future artificial brains and unconventional computing devices. To evaluate a potential of proteinoid microspheres for unconventional electronic devices we measure and analyse the data-transfer capacities of proteinoid microspheres. In experimental laboratory conditions we demonstrate that the transfer function of proteinoids microspheres is a nontrivial phenomenon, which might be due to the wide range of proteinoid shapes, sizes, and structures.

Inspired in a coenzyme-like behavior, an alternative mechanism to induce homochirality within a small vesicle is proposed. The system includes six different chemical species: an achiral substrate A, the enantiomeric forms L and D, a coenzyme E and two intermediate catalytic forms LE and DE. Whereas the coenzyme and the intermediate catalytic forms are trapped within the vesicle, the substrate and the two enantiomeric forms are able to diffuse selectively across the vesicle boundary. Instead of using autocatalysis, the production of new enantiomers includes two different steps, the production of intermediate catalytic species (LE, DE) and the catalytic production of new enantiomers from the substrate. Using a suitable parameterization, we found that the chiral evolution of the system is highly dependent on the total amount of coenzyme within the vesicle, regardless of whether the surrounding membrane is permeable or not. However, the existence of large flows from the outside can destabilize the homochiral state inside the vesicle. In general, homochiral states tend to arise when the amount of coenzyme is quite low, a value that can vary according to the parametrization. On the other hand, the system tends to decrease the enantiomeric excess when the coenzyme levels are high enough. In general, the appearance of homochirality is conditioned by stochastic fluctuations in coenzyme levels within the vesicle, an effect that is gradually amplified throughout the entire process of enantiomer synthesis.

An evolutionary computation framework to learn binary threshold networks is presented. Inspired by the recent trend of binary neural networks, where weights and activation thresholds are represented using 1 and -1 such that they can be stored in 1-bit instead of full precision, we explore this approach for gene regulatory network modeling. We test our method by inferring binary threshold networks of two regulatory network models: Quorum sensing systems in bacterium Paraburkholderia phytofirmans PsJN and the fission yeast cell-cycle. We considered differential evolution and particle swarm optimization for the simulations. Results for weights having only 1 and -1 values, and different activation thresholds are presented. Full binary threshold networks were found with minimum error (2 bits), whereas when the binary restriction is relaxed for the activation thresholds, networks with 0 bit error were found.

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.

Living fungal mycelium networks are proven to have properties of memristors, capacitors and various sensors. To further progress our designs in fungal electronics we need to evaluate how electrical signals can be propagated through mycelium networks. We investigate the ability of mycelium-bound composites to convey electrical signals, thereby enabling the transmission of frequency-modulated information. Mycelium networks were found to reliably transfer signals with a recoverable frequency comparable to the input, in the 100Hz to 10 000Hz frequency range. Mycelial adaptive responses, such as tissue repair, may result in fragile connections, however. While the mean amplitude of output signals was not reproducible among replicate experiments exposed to the same input frequency, the variance across groups was highly consistent. Our work is supported by NARX modelling through which an approximate transfer function was derived. These findings advance the state of the art of using mycelium-bound composites in analogue electronics and unconventional computing.

We have reviewed and interpreted the thermodynamic principles for flower pigmentation. The basic thoughts are as follows: 1) any biological trait is associated with one thermodynamic system; 2) a thermodynamic system of biology cannot be physically isolated from complex thermal systems of biology but can be separately studied using thermodynamic methods; 3) a thermodynamic system of biology has all types of information, including volume, shape, and structure, unlike the traditional thermal system of gases; 4) a thermodynamic system of biology is associated with one type of biological structure that is not fully fixed but can change its conformation under different conditions; and 5) a thermodynamic system of biology shows a hierarchical structure. On the basis of these principles, several conclusions regarding flower pigmentation are obtained as follows: 1) processes of pigmentation formation can be divided into reversible and irreversible processes; 2) the reversible process is related to quantitative changes in pigments; 3) the irreversible process is related to the formation of stable pigmentation patterns that are physiologically inherited; 4) the spot pattern of color pigmentation represents an independent island of the physiological system; 5) many types of activators and inhibitors are involved in flower pigmentation production; 6) the patterns of flower pigmentation can be modulated; and 7) the evolution mechanism of organogenesis can be separated into several steps of independent thermodynamic processes. Our conclusion is that the thermodynamic system, rather than the dynamic system, is the essential and fundamental attribute of biological behaviors.

The origin of eukaryotes is one of the most fundamental problems in the entire history of life. How did eukaryotes arise? Previous attempts to solve the problem are very far from an answer, at best they propose a solution to one of the various innovations that ended up culminating in eukaryotes. Based on a hypothetical-deductive methodology, as usual in evolutionary issues, I propose that eukaryotes emerged from the endosymbiotic association between a flagellate parasite and its host, of which the sperm is the main vestige. The hypothesis unifies the solution to the vast array of acquisitions shared by eukaryotes that differentiate them from other beings, remarkably cell nucleus, mitosis, meiosis and sexual reproduction. The solution has a deep impact on understanding the origin and functioning of all complex life forms.

We develop a theory of oxygen hemoglobin association and derive the oxygen hemoglobin association equation and determine the values of the four association constants by curve fitting four commonly accepted data points that relate oxygen saturation and oxygen partial pressure (PO2) in the blood using mathematical reasoning and chemical kinetics. The four association constants come from the progression of oxygen binding to each of the four subunits on the hemoglobin molecule in a cooperative manner. The binding of oxygen alters the affinity of subsequent binding of additional oxygen molecules, which is reflected in changing magnitudes of the association constants. We also demonstrate rather surprisingly that the value of the third association constant is significantly smaller than all other association constants and offer some conjecture about this puzzling finding. With our equation, we can compute the distributions of all five oxyhemoglobin species at various PO2 levels that are published for the first time in the history of hemoglobin research. After examining the distributions, we find the triply bound oxyhemoglobin exists in very low concentration, consistent with the small third association constant. In addition, we present the oxygen levels where maximal concentrations of various oxyhemoglobin species occur, another unexpected finding that was never published before. Finally, we determine the inflection point of the hemoglobin association curve, a defining property to a specific sigmoid curve, representing the steepest portion of the curve.

The necessity to record and store biological data is increasing in due course of time. However, it is quite difficult to understand biological mechanisms and keep a track of these events in some storage mediums. DNA (deoxyribonucleic acid) is the best candidate for the storage of cellular events in the biological system. It is energy efficient as well as stable at the same time. DNA-based writers and memory devices are continually evolving and finding new avenues in terms of their wide range of applications. Among all the DNA-based storage devices that employ enzymes like recombinases, nucleases, integrases, and polymerases, one of the most popular tools used for these devices is the emerging and versatile CRISPR Cas technology. CRISPR Cas is a prokaryotic immune system that keeps a memory of viral attacks and protects prokaryotes from potential future infections. The main aim of this short review is to study such molecular recorders and writers that employ CRISPR Cas technologies and obtain an in-depth overview of the mechanisms involved and the applications of these molecular devices.

Maturana and Varela defined an autopoietic system as a self-regenerating network of processes. We reinterpret and elaborate this conception starting from a process ontology and its formalization in terms of reaction networks and chemical organization theory. An autopoietic organization can be modelled as a network of "molecules" (components) undergoing reactions, which is (operationally) closed and self-maintaining. Such organizations, being attractors of a dynamic system, tend to self-organize-thus providing a model for the origin of life. However, in order to survive in a variable environment, they must also be resilient, i.e. able to compensate perturbations. According to the "good regulator theorem" this requires some form of cognition, i.e. knowing which action to perform for which perturbation. Such cognition becomes more effective as it learns to anticipate perturbations by discovering invariant patterns in its interactions with the environment. Nevertheless, the resulting predictive model remains a subjective construction. Such implicit model cannot be interpreted as an objective representation of external reality, because the autopoietic system does not have direct access to that reality, and there is in general no isomorphism between internal and external processes.

Perception of color by humans and other primates is a complex problem, studied by neurophysiology, psychophysiology, psycholinguistics, and even philosophy. Being mostly trichromats, simian primates have three types of opsin proteins, expressed in cone neurons in the eye, which allow for the sensing of color as the physical wavelength of light. Further, in neural networks of the retina, the coding principle changes from three types of sensor proteins to two opponent channels: activity of one type of neuron encode the evolutionarily ancient blue-yellow axis of color stimuli, and another more recent evolutionary channel, encoding the axis of red-green color stimuli. Both color channels are distinctive in neural organization at all levels from the eye to the neocortex, where it is thought that the perception of color (as philosophical qualia) emerges from the activity of some neuron ensembles. Here, using data from neurophysiology as a starting point, we propose a hypothesis on how the perception of color can be encoded in the activity of certain neurons in the neocortex. These conceptual neurons, herein referred to as 'color neurons', code only the hue of the color of visual stimulus, similar to place cells and number neurons, already described in primate brains. A case study with preliminary, but direct, evidence for existing conceptual color neurons in the human brain was published in 2008. We predict that the upcoming studies in non-human primates will be more extensive and provide a more detailed description of conceptual color neurons.

In the seminal work on autopoiesis by Varela, Maturana, and Uribe, they start by addressing the confusion between processes that are history dependent and processes that are history independent in the biological world. The former is particularly linked to evolution and ontogenesis, while the latter pertains to the organizational features of biological individuals. Varela, Maturana, and Uribe reject this framework and propose their original theory of autopoietic organization, which emphasizes the strong complementarity of temporal and non-temporal phenomena. They argue that the dichotomy between structure and organization lies at the core of the unity of living systems. By opposing history-dependent and history-independent processes, methodological challenges arise in explaining phenomena related to living systems and cognition. Consequently, Maturana and Varela reject this approach in defining autopoietic organization. I argue, however, that this relationship presents an issue that can be found in recent developments of the science of artificial intelligence (AI) in different ways, giving rise to related concerns. While highly capable AI systems exist that can perform cognitive tasks, their internal workings and the specific contributions of their components to the overall system behavior, understood as a unified whole, remain largely uninterpretable. This article explores the connection between biological systems, cognition, and recent developments in AI systems that could potentially be linked to autopoiesis and related concepts such as autonomy and organization. The aim is to assess the advantages and disadvantages of employing autopoiesis in the synthetic (artificial) explanation for biological cognitive systems and to determine if and how the notion of autopoiesis can still be fruitful in this perspective.

In this work we present a systematic mathematical approximation scheme that exposes the way that information, about the evolutionary forces of selection and random genetic drift, is encoded within gene-frequency trajectories. We determine approximate, time-dependent, gene-frequency trajectory statistics, assuming additive selection. We use the probability of fixation to test and illustrate the approximation scheme introduced. For the case where the strength of selection and the effective population size have constant values, we show how a standard diffusion approximation result, for the probability of fixation, systematically emerges when increasing numbers of approximate trajectory statistics are taken into account. We then provide examples of how time-dependent parameters influence gene-frequency statistics.

Proteinoids, or thermal proteins, are produced by heating amino acids to their melting point and initiating polymerisation to produce polymeric chains. In aqueous solutions proteinoids swell into hollow microspheres. These microspheres produce endogenous burst of electrical potential spikes and change patterns of their electrical activity in response to illumination. We report results on a detailed investigation on the effects of white cold light on the spiking of proteinoids. We study how different types and intensities of light determine proteinoids' spiking amplitude, period, and pattern. The results of this study will be utilised to evaluate proteinoids for their potential as optical sensors and their application in unconventional computing.

In the Mathematical Biology community, Reinhart Heinrich (1946-2006) is well-known as one of the founders of Metabolic Control Analysis. Moreover, he made significant contributions to the modelling of erythrocyte metabolism and signal transduction cascades, optimality principles in metabolism, theoretical membrane biophysics and other topics. Here, the historical context of his scientific work is outlined and numerous personal memories of the scholarship of, and cooperation with, Reinhart Heinrich are narrated. Attention is drawn again to the pros and cons of normalized and non-normalized control coefficients. The role of the Golden Ratio in a dynamic optimization problem in genetic regulation of metabolism is discussed. Overall, this article is aimed at keeping alive the memory of a unique university teacher, researcher and friend.

Some neurological disorders such e.g. as Alzheimer disease are accompanied by the appearance of amyloid fibrils inside and outside cells. Herein, I present a generic coarse-grained kinetic mean-field model describing at the extracellular level the interplay of fibrils and cells. It includes the formation and degradation of fibrils, activation of healthy cells with respect to the fabrication of fibrils, and death of activated cells. The corresponding analysis indicates that the disease development can occur in two qualitatively different regimes. The first one is controlled primarily by the intrinsic factors resulting in slow increase of fibril production inside cells. The second one implies faster self-promoted growth of the fibril population by analogy with explosion. This prediction reported as a hypothesis is of interest for conceptual understanding of the neurological disorders.

This article provides an alternative perspective on viruses, exploring their origins, ecology, and evolution. Viruses are recognized as the most prevalent biological entities on Earth, permeating nearly all environments and forming the virosphere-a significant biological layer. They play a crucial role in regulating bacterial populations within ecosystems and holobionts, influencing microbial communities and nutrient recycling. Viruses are also key drivers of molecular evolution, actively participating in the maintenance and regulation of ecosystems and cellular organisms. Many eukaryotic genomes contain genomic elements with viral origins, which contribute to organismal equilibrium and fitness. Viruses are involved in the generation of species-specific orphan genes, facilitating adaptation and the development of unique traits in biological lineages. They have been implicated in the formation of vital structures like the eukaryotic nucleus and the mammalian placenta. The presence of virus-specific genes absent in cellular organisms suggests that viruses may pre-date cellular life. Like progenotes, viruses are ribonucleoprotein entities with simpler capsid architectures compared to proteolipidic membranes. This article presents a comprehensive scenario describing major transitions in prebiotic evolution and proposes that viruses emerged prior to the Last Universal Common Ancestor (LUCA) during the progenote era. However, it is important to note that viruses do not form a monophyletic clade, and many viral taxonomic groups originated more recently as reductions of cellular structures. Thus, viral architecture should be seen as an ancient and evolutionarily stable strategy adopted by biological systems. The goal of this article is to reshape perceptions of viruses, highlighting their multifaceted significance in the complex tapestry of life and fostering a deeper understanding of their origins, ecological impact, and evolutionary dynamics.

Is information the other face of causation? This issue cannot be clarified without discussing how these both are related to physical laws, logic, computation, networks, bio-signaling, and the mind-body problem. The relation between information and causation is also intrinsically linked to many other concepts in complex systems theory such as emergence, self-organization, synergy, criticality, and hierarchy, which in turn involve various notions such as observer-dependence, dimensionality reduction, and especially downward causation. A canonical example proposed for downward causation is the collective behavior of the whole system at a macroscale that may affect the behavior of each its member at a microscale. In neuroscience, downward causation is suggested as a strong candidate to account for mental causation (free will). However, this would be possible only on the condition that information might have causal power. After introducing the Causal Equivalence Principle expanding the relativity principle for coarse-grained and fine-grained linear causal chains, and a set-theoretical definition of multiscale nested hierarchy composed of modular ⊂-chains, it is shown that downward causation can be spurious. It emerges only in the eyes of an observer, though, due to information that could not be obtained by "looking" exclusively at the behavior of a system at a microscale. On the other hand, since biological systems are hierarchically organized, this information gain is indicative of how information can be a function of scale in these systems and a prerequisite for scale-dependent emergence of cognition and consciousness in neural networks.

Calcium is one of the most versatile messengers for intracellular signaling. In the case of cilia and flagella calcium has the central role in transfer of communications between extracellular stimuli and intracellular formation of frequency modulated signal and their deciphering by target proteins. In this paper, the diffusion of fluorescently or otherwise tagged and un-tagged Ca2＋ particles is analyzed by solving the system of pertaining reaction-diffusion equations. We used Fourier transform tools to get asymptotic eigenfunctions for tagged (un-tagged) free and buffered Ca2＋ ions. We made some numerical estimations for diffusion coefficients corroborating the fact that messages diffuse faster than Ca2＋ messengers. From the best of our knowledge, this is the first time that Ca2＋ signaling in living cells is biophysically elaborated within the framework of model presented here. We suggest the experimental assay on the basis of radioactive Ca2＋ as tagged probe.

The concept of Learning by Stimulus Avoidance (LSA) has been proposed in recent literature, and the methods of avoiding stimuli: action, prediction, and separation appear to align well with the formation of Bertschinger's informational closure. In this study, we provide experimental evidence demonstrating that spiking neural networks, which avoid stimuli, can indeed facilitate the emergence of informational closure. The established link between LSA and informational closure lays the foundation for further exploration of autopoietic relationships and the self-organization of closure within neural networks.

Diversity is supposed to create better groups and societies but sometimes fails. It is explained why the power of diversity may not create better groups in the current diversity prediction theory. Diversity may hurt civic life and introduce distrust. This is because the current diversity prediction theory is based on real numbers that ignore individual abilities. Its diversity prediction theory maximizes performance with infinite population size. Contrary to this, collective intelligence or swarm intelligence is not maximized by infinite population size, but by population size. The extended diversity prediction theory using the complex number allows us to express individual abilities or qualities. The diversity of complex numbers always produces better groups and societies. The wisdom of crowds, collective intelligence, swarm intelligence or nature-inspired intelligence is implemented in the current machine learning or artificial intelligence, called Random Forest. The problem of the current diversity prediction theory is detailed in this paper.

Information, the measure of order in a complex system, is the opposite of entropy, the measure of chaos and disorder. We can distinguish several levels at which information is processed in the brain. The first one is the level of serial molecular genetic processes, similar in some aspects to digital computations (DC). At the same time, higher cognitive activity is probably based on parallel neural network computations (NNC). The advantage of neural networks is their intrinsic ability to learn, adapting their parameters to specific tasks and to external data. However, there seems to be a third level of information processing as well, which involves subjective consciousness and its units, so called qualia. They are difficult to study experimentally, and the very fact of their existence is hard to explain within the framework of modern physical theory. Here I propose a way to consider consciousness as the extension of basic physical laws - namely, total entropy dissipation leading to a system simplification. At the level of subjective consciousness, the brain seems to convert information embodied by neural activity to a more simple and compact form, internally observed as qualia. Whereas physical implementations of both DC and NNC are essentially approximate and probabilistic, qualia-associated computations (QAC) make the brain capable of recognizing general laws and relationships. While elaborating a behavioral program, the conscious brain does not act blindly or gropingly but according to the very meaning of such general laws, which gives it an advantage compared to any artificial intelligence system.

The evolution of DNA and DNA polymerases was a crucial step in the evolution of life on Earth. In the present work, we reconstruct the ancestral sequence and structure for the B family polymerases. Using comparative analyses, we infer the transient state between the ancestor retrotranscriptase and the contemporary B family DNA polymerases. Exonuclease motif was detected in the primary ancestral sequence, as well as an elongation-functioning motif. It is remarkable that the ancestral molecule is more comparable to the retrotranscriptases in terms of structural domains, even though we discovered similarities in the primary sequence with proteins from the B family of DNA polymerases. The present B family proteins differ structurally from retrotranscriptases the most, although the reconstruction of the ancestor protein was able to capture the transitional steps between these two families of polymerases.

The theory of autopoiesis has been influential in many areas of theoretical biology, especially in the fields of artificial life and origins of life. However, it has not managed to productively connect with mainstream biology, partly for theoretical reasons, but arguably mainly because deriving specific working hypotheses has been challenging. The theory has recently undergone significant conceptual development in the enactive approach to life and mind. Hidden complexity in the original conception of autopoiesis has been explicated in the service of other operationalizable concepts related to self-individuation: precariousness, adaptivity, and agency. Here we advance these developments by highlighting the interplay of these concepts with considerations from thermodynamics: reversibility, irreversibility, and path-dependence. We interpret this interplay in terms of the self-optimization model, and present modeling results that illustrate how these minimal conditions enable a system to re-organize itself such that it tends toward coordinated constraint satisfaction at the system level. Although the model is still very abstract, these results point in a direction where the enactive approach could productively connect with cell biology.

Understanding how biological organisms are autonomous-maintain themselves far from equilibrium through their own activities-requires understanding how they regulate those activities. In multicellular animals, such control can be exercised either via endocrine signaling through the vasculature or via neurons. In C. elegans this control is exercised by a well-delineated relatively small but distributed nervous system that relies on both chemical and electric transmission of signals. This system provides resources to integrate information from multiple sources as needed to maintain the organism. Especially important for the exercise of neural control are neuromodulators, which we present as setting agendas for control through more traditional electrical signaling. To illustrate how the C. elegans nervous system integrates multiple sources of information in controlling activities important for autonomy, we focus on feeding behavior and responses to adverse conditions. We conclude by considering how a distributed nervous system without a centralized controller is nonetheless adequate for autonomy.

This article highlights the potential contribution of biological codes to the course and dynamics of evolution. The concept of organic codes, developed by Marcello Barbieri, has fundamentally changed our view of how living systems function. The notion that molecular interactions built on adaptors that arbitrarily link molecules from different "worlds" in a conventional, i.e., rule-based way, departs significantly from the law-based constraints imposed on livening things by physical and chemical mechanisms. In other words, living entities and non-living things behave like rules and laws, respectively, but this important distinction is rarely considered in current evolutionary theory. The many known codes allow quantification of codes that relate to a cell, or comparisons between different biological systems and may pave the way to a quantitative and empirical research agenda in code biology. A starting point for such an endeavour is the introduction of a simple dichotomous classification of structural and regulatory codes. This classification can be used as a tool to analyse and quantify key organising principles of the living world, such as modularity, hierarchy, and robustness, based on organic codes. The implications for evolutionary research are related to the unique dynamics of codes, or 'Eigendynamics' (self-momentum) and how they determine the behaviour of biological systems from inside, whereas physical constraints are imposed mainly from outside. A speculation on the drivers of macroevolution in light of codes is followed by the conclusion that a meaningful and comprehensive understanding of evolution depends on including codes into the equation of life.

Negentropy explored by Schrödinger for saving biology within thermodynamics stands firm on the perpetual temporal cohesion of material origin. The temporal cohesion refers to the cohesion acting between what has been produced and what is going to produce, thus keeping the negentropy as a measure of organization constantly positive in time. Such cohesion is ubiquitous in measurement internal to the material world. Internal measurement in the quantum realm really lets the current "detecting" constantly feed upon some quantum resources available from the preceding "detected" a moment ago. The quantum resources transferred during the cohesive process serve as a physical factor for bridging between the two different temporalities; the present perfect and progressive tenses. What has been detected comes to participate in an attribute of what is going to detect next as constantly following suit. Temporal cohesion is an agential mediator connecting the adjacent temporalities in a manner different from the spatial cohesion observing only the single temporality restricted to the present tense. The activity of enhancing the magnitude of negentropy may have preceded the emergence of something called life phenomenon. Biology feeds upon temporal cohesion.

Plants connect both pollinators and herbivores, and motivate the exploration of community structure in ecological networks merging antagonistic and mutualistic interactions. Evidence has shown that the two opposite plant-animal interactions are not independent from each other, in particular, herbivores can affect plant-pollinator pairwise interactions. Here, we explored effects of herbivore-induced pollinator limitation on community stability (including temporal stability and composition stability) of the mutualism-antagonism continuum. Our model demonstrated that pollinator limitation can boost up both temporal stability (i.e., the proportion of stable communities) and composition stability (i.e., species persistence), while the positive effects also depend on the strength of antagonistic and mutualistic interactions. Specifically, a community with higher temporal stability has a higher composition stability. Meanwhile, the correlations between network architecture and composition stability are also affected by pollinator limitation. Therefore, our results highlight that pollinator limitation can enhance community stability and may alter network architecture-composition stability relationship, and further advance the interplay between multiple types of species interactions within ecological networks.

Photosynthetic organisms use photosynthesis to harvest sunlight and convert the solar energy into chemical energy, which is then used to reduce atmospheric carbon dioxide into organic molecules. This process forms the basis of all life on Earth, and stands at the beginning of the food chain which feeds the world population. Not surprisingly, many research efforts are currently ongoing aiming at improving growth and product yield of photosynthetic organisms, and several of these activities directly target the photosynthetic pathways. Metabolic Control Analysis (MCA) shows that, in general, the control over a metabolic flux, such as carbon fixation, is distributed among several steps and highly dependent on the external conditions. Therefore, the concept of a single 'rate-limiting' step is hardly ever applicable, and as a consequence, any strategy relying on improving a single molecular process in a complex metabolic system is bound to fail to yield the expected results. In photosynthesis, reports on which processes exert the highest control over carbon fixation are contradictory. This refers to both the photosynthetic 'light' reactions harvesting photons and the 'dark' reactions of the Calvin-Benson-Bassham Cycle (CBB cycle). Here, we employ a recently developed mathematical model, which describes photosynthesis as an interacting supply-demand system, to systematically study how external conditions affect the control over carbon fixation fluxes.

Copper is essential for life, but is toxic in excess. Copper homeostasis is achieved in the cytoplasm and the periplasm as a unique feature of Gram-negative bacteria. Especially, it has become clear the role of the periplasm and periplasmic proteins regarding whole-cell copper homeostasis. Here, we addressed the role of the periplasm and periplasmic proteins in copper homeostasis using a Systems Biology approach integrating experiments with models. Our analysis shows that most of the copper-bound molecules localize in the periplasm but not cytoplasm, suggesting that Escherichia coli utilizes the periplasm to sense the copper concentration in the medium and sequester copper ions. In particular, a periplasmic multi-copper oxidase CueO and copper-responsive transcriptional factor CusS contribute both to protection against Cu(I) toxicity and to incorporating copper into the periplasmic components/proteins. We propose that Gram-negative bacteria have evolved mechanisms to sense and store copper in the periplasm to expand their living niches.

Parkinson's disease (PD) is a neurodegenerative disease represented by the progressive loss of dopamine producing neurons, with motor and non-motor symptoms that may be hard to distinguish from other disorders. Affecting millions of people across the world, its symptoms include bradykinesia, tremors, depression, rigidity, postural instability, cognitive decline, and falls. Furthermore, changes in gait can be used as a primary diagnosis factor. A dataset is described that records data on healthy individuals and on PD patients, including those who experience freezing of gait, in both the ON and OFF-medication states. The dataset is comprised of data for four separate tasks: voluntary stop, timed up and go, simple motor task, and dual motor and cognitive task. Seven different classifiers are applied to two problems relating to this data. The first problem is to distinguish PD patients from healthy individuals, both overall and per task. The second problem is to determine the effectiveness of medication. A thorough analysis on the classifiers and their results is performed. Overall, multilayer perceptron and decision tree provide the most consistent results.

Promoters are DNA regulatory elements located near the transcription start site and are responsible for regulating the transcription of genes. DNA fragments arranged in a certain order form specific functional regions with different information contents. Information theory is the science that studies the extraction, measurement and transmission of information. The genetic information contained in DNA follows the general laws of information storage. Therefore, method in information theory can be used for the analysis of promoters carrying genetic information. In this study, we introduced the concept of information theory to the study of promoter prediction. We used 107 features extracted based on information theory methods and a backpropagation neural network to build a classifier. Then, the trained classifier was applied to predict the promoters of 6 organisms. The average AUCs of the 6 organisms obtained by using hold-out validation and ten-fold cross-validation were 0.885 and 0.886, respectively. The results verified the effectiveness of information-theoretic features in promoter prediction. Considering the possible redundancy in the feature set, we performed feature selection and obtained key feature subsets related to promoter characteristics. The results indicate the potential utility of information-theoretic features in promoter prediction.

The impact of different lockdown strategies upon the total number of infections in an epidemic are evaluated for two models of infection: one in which the disease confers permanent immunity, and one in which it does not. The strategies are based upon the proportion of the population infected at a time in order to trigger lockdown, combined with the proportion of interactions removed during lockdown. The population, its interactions, and the relative strengths of those interactions are stored in a weighted contact network, from which edges are removed during lockdown. These edges are selected using an evolutionary algorithm (EA) designed to minimize total infections. Using the EA to select edges significantly reduces total infections in comparison to random selection. In fact, the EA results for the least strict conditions were similar or better to the random results for the most strict conditions, showing that a judicious choice of restrictions during lockdown has the greatest effect on reducing infections. Further, when using the most strict rules a smaller proportion of interactions can be removed to obtain similar or better results in comparison to removing a higher proportion of interactions for less strict rules.

Using the formal framework of the Free Energy Principle, we show how generic thermodynamic requirements on bidirectional information exchange between a system and its environment can generate complexity. This leads to the emergence of hierarchical computational architectures in systems that operate sufficiently far from thermal equilibrium. In this setting, the environment of any system increases its ability to predict system behavior by "engineering" the system towards increased morphological complexity and hence larger-scale, more macroscopic behaviors. When seen in this light, regulative development becomes an environmentally-driven process in which "parts" are assembled to produce a system with predictable behavior. We suggest on this basis that life is thermodynamically favorable and that, when designing artificial living systems, human engineers are acting like a generic "environment".

The relationship between humans and technology has attracted increasing attention with the advent of ever stronger models of artificial intelligence. Humans and technology are intertwined within multiple autopoietic loops of stress, care, and intelligence. This paper suggests that technology should not be seen as a mere tool serving humans' needs, but rather as a partner in a rich relationship with humans. Our model for understanding autopoietic systems applies equally to biological, technological, and hybrid systems. Regardless of their substrates, all intelligent agents can be understood as needing to respond to a perceived mismatch between what is and what should be. We take this observation, which is evidence of intrinsic links between ontology and ethics, as the basis for proposing a stress-care-intelligence feedback loop (SCI loop for short). We note that the SCI loop provides a perspective on agency that does not require recourse to explanatorily burdensome notions of permanent and singular essences. SCI loops can be seen as individuals only by virtue of their dynamics, and are thus intrinsically integrative and transformational. We begin by considering the transition from poiesis to autopoiesis in Heidegger and the subsequent enactivist tradition, and on this basis formulate and explain the SCI loop. In an acknowledgment of Maturana's and Varela's project, our findings are considered against the backdrop of a classic Buddhist model for the cultivation of intelligence, known as the bodhisattva. We conclude by noting that SCI loops of human and technological agency can be seen as mutually integrative by noticing the stress-transfers between them. The loop framework thus acknowledges encounters and interactions between humans and technology in a way that does not relegate one to the subservience of the other (neither in ontological nor in ethical terms), suggesting instead integration and mutual respect as the default for their engagements. Moreover, an acknowledgment of diverse, multiscale embodiments of intelligence suggests an expansive model of ethics not bound by artificial, limited criteria based on privileged composition or history of an agent. The implications for our journey into the future appear numerous.

The sequencing of eukaryotic genomes has shown that tandem repeats are abundant in their sequences. In addition to affecting some cellular processes, tandem repeats in the genome may be associated with specific diseases and have been the key to resolving criminal cases. Any tool developed for detecting tandem repeats must be accurate, fast, and useable in thousands of laboratories worldwide, including those with not very advanced computing capabilities. The proposed method, the Rapid Perfect Tandem Repeat Finder (RPTRF), minimizes the need for excess character comparison processing by indexing the input file and significantly helps to accelerate and prepare the output without artifacts by using an interval tree in the filtering section. The experiments demonstrated that the RPTRF is very fast in discovering all perfect tandem repeats of all categories of any genomic sequences. Although the detection of imperfect TRs is not the focus of the RPTRF, comparisons show that it even outperforms some other tools (in five selected gold standards) designed explicitly for this purpose. The implemented tool and how to use it are available on GitHub.

The increasing global demand for vegetable oils will only be met if there are significant improvements in the productivity of the major oil crops, such as oilseed rape. Metabolic engineering offers the prospect of further gains in yield beyond that already achieved by breeding and selection but requires guidance as to the changes that need to be made. Metabolic Control Analysis, through measurement and estimation of flux control coefficients, can indicate which enzymes have the most influence on a desired flux. Some experiments have previously reported flux control coefficients for oil accumulation in the seeds of oilseed rape, and others have measured control coefficient distributions for multi-enzyme segments of oil synthesis in seed embryo metabolism measured in vitro. In addition, other reported manipulations of oil accumulation contain results that are exploited further here to calculate previously unknown flux control coefficients. These results are then assembled within a framework that allows an integrated interpretation of the controls on oil accumulation from the assimilation of CO2 to deposition of oil in the seed. The analysis shows that the control is distributed to an extent that the gains from amplifying any single target are necessarily limited, but there are candidates for joint amplification that are likely to act synergistically to produce much more significant gains.

In this paper, we investigate the Casimir effect within a virus RNA, particularizing the study to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Then, we discuss the possibility of occurring damage or mutation in its genome due to the presence of quantum vacuum fluctuations inside and around the RNA ribbon. For this, we consider the geometry and the nontrivial topology of the viral RNA as having a simple helical structure. We initially compute the non-thermal Casimir energy associated to that geometry, considering boundary conditions that constrain the zero point oscillations of a massless scalar field to the cylindrical cavity containing a helix pitch of RNA ribbon. Then we extend the obtained result to the electromagnetic field and, following, we calculate the probability of occurring damage or mutation in RNA by using the normalized inverse exponential distribution, which suppresses very low energies, and consider cutoff (threshold) energies corresponding to UV-A and UV-C rays, surely responsible by mutations. Then, by taking into account UV-A, we arrive at a mutation rate per base per infection cycle, which in the case of the SARS-CoV-2 is non-negligible. We find a maximum value of this mutation rate for an RNA ribbon radius, applying it for SARS-CoV-2, in particular. We also calculate a characteristic longitudinal oscillation frequency for the helix pitch value corresponding to the local minimum of the Casimir energy. Finally, we consider thermal fluctuations of classical and quantum nature and show that the corresponding probability of mutation is completely negligible for that virus. Therefore, we conclude that only the nontrivial topology and the geometric attributes of the RNA molecule contribute to the possible mutations caused by quantum vacuum fluctuations in the viral genome.

Although the papers of Kacser and Burns (1973) and Heinrich and Rapoport (1974a,b) are commonly taken as the birth of metabolic control analysis, many of the ideas in them are foreshadowed in earlier papers, from 1956 onwards, when Kacser first argued for taking a systemic view of genetics and biochemistry.

Coffee rust is one of the main diseases that affect coffee plantations worldwide, causing large-scale ecological and economic damage. While multiple methods have been proposed to tackle this challenge, using snails as biological agents have shown to be the most consistent and promising approach. However, snails are an invasive species, and overusing them can cause devastating outcomes. In this paper, we develop and explore an ecological-epidemiological mathematical model for the coffee tree rust pandemic control using snails as biological agents. We analyze the equilibria of the proposed system with their stability properties. In addition, we perform numerical analysis to obtain the sensitivity of the system to different changes and manipulation of the snails pandemic control, under specific conditions. Finally, we propose an in silico mechanism to obtain an analytical connection between the system's initial condition and the number of snails needed to optimally control the rust pandemic spread while preventing the snail population to grow unmanageably. Our model can be used to optimize the usage of snails as biological agents to control the rust pandemic in spatially-small areas, by predicting the number of snails one needs to introduce to the ecosystem in order to obtain a desired outcome.

Cardiovascular diseases can be attributed to irregular blood pressure, which may be caused by malfunctioning kidneys that regulate blood pressure. Research has identified complex oscillations in the mechanisms used by the kidney to regulate blood pressure. This study uses established physiological knowledge and earlier autoregulation models to derive a fractional order nephron autoregulation model. The dynamical behaviour of the model is analyzed using bifurcation plots, revealing periodic oscillations, chaotic regions, and multistability. A lattice array of the model is used to study collective behaviour and demonstrates the presence of chimeras in the network. A ring network of the fractional order model is also considered, and a diffusion coupling strength is adopted. A basin of synchronization is derived, considering coupling strength, fractional order or number of neighbours as parameters, and measuring the strength of incoherence. Overall, the study provides valuable insights into the complex dynamics of the nephron autoregulation model and its potential implications for cardiovascular diseases.

This paper presents an alternative to Autopoietic Enactivism in the form of a Code Biology-informed account on human sense-making. It demonstrates the possibility of avoiding a dualism between, on the one hand, the autonomy of individual sense-makers and, on the other, the heteronomy of social facts. This is possible because code biological principles are pertinent to different levels of biological and non-biological organization and cut across the organismic self-non-self border. Analytically, one can maintain the overall integrity of an agent as a separable unit of (inter)action while also avoiding an autonomy-heteronomy divide. We therefore emphasise the constitutive role of codified relations that, while irreducible to operational closure, connect the sense-making agent's social interactions to those of other agents. The move grants a central, constitutive role to external norms (or, heteronomy) as altering the internal, embodied integrity of an autonomous agent. Drawing on the case of prosthetics use in amputees, we show that successful integration of a prothesis cannot be reduced to the substitution of a missing limb. Rather, it demands experienced bodily wholeness on the part of the agent which can only be achieved by attuning and adapting to use of a prosthesis while also internalizing social norms and values. It is concluded that many aspects of the living actualize codified relations which incorporate both heteronomous and autonomous traits.

Photosynthesis is the predominant biochemical process of carbon dioxide assimilation in the biosphere. To reduce carbon dioxide into organic compounds, photosynthetic organisms have one or two distinct photochemical reaction centre complexes with which they capture solar energy and generate ATP and reducing power. The core polypeptides of the photosynthetic reaction centres show low homologies but share overlapping structural folds, overall architecture, similar functional properties and highly conserved positions in protein sequences suggesting a common ancestry. However, the other biochemical components of photosynthetic apparatus appear to be a mosaic resulting from different evolutionary trajectories. The current proposal focusses on the nature and biosynthetic pathways of some organic redox cofactors that participate in the photosynthetic systems: quinones, chlorophyll and heme rings and their attached isoprenoid side chains, as well as on the coupled proton motive forces and associated carbon fixation pathways. This perspective highlights clues about the involvement of the phosphorus and sulphur chemistries that would have shaped the different types of photosynthetic systems.

In a previous paper, the authors proposed a mathematical definition of a public language and a new model of consciousness based on that public language. Consciousness spans a wide range of disciplines, including medicine, philosophy, mathematics, physics, and psychology, and it is desirable that the model of consciousness should also be able to answer questions of consciousness in each discipline. This paper applies the HLbC model proposed by the authors to the question of psychological consciousness and examines whether it can explain the phenomenon. Regarding the problem of so-called optical illusions, such as Rubin's vase, it can be explained by the fact that consciousness is caused by a stochastic fluctuation of consciousness with respect to figures that can be interpreted in multiple ways. Empathy and mutual understanding are also proposed to be mathematically represented and understood in terms of the Kullback-Leibler divergence, which is the basis of the HLbC model. Furthermore, the HLbC model was applied to prospect theory, the basis of behavioural economics, and confirmed that its properties can be successfully explained. From the above, we confirmed that the proposed HLbC model can explain the phenomenon of psychological consciousness.

In the light of possible advance in the elucidation of emergence in natural systems we address the likelihood of an overriding relationship between the local and the global in Nature. We consider the possible views of an abstract conglomeration of entities, and whether a global representation can be established. This leads to a consideration of the problem in natural contexts to establish a relationship between local and global properties. We describe the application of a model-hierarchical representation to a biological cell, and its implications in terms of subjective and objective viewpoints. We conclude by stating the concluded relationship between local and global properties in Nature, in that control is exercised not only from local-to-global, but also from global-to-local.

The code paradigm in biological and social sciences arises to Aristotle. For conscious activity, Aristotle introduced the notion of reflexive self-awareness in sense perception. This reflexive process generates the codes that signify sensual perceptive events and constrain human behavior. Coding systems grow via the generation of hypertextual statements reflecting new meanings in the process defined by Marcello Barbieri as a codepoiesis. It results in the establishment of higher-level codes (metacodes) forming the semiotic screen that has a nature of the set of perceived objects internalized by the conscious subject in encoding the symbolic actions. The characteristic feature of the semiotic screen consists in its property of being shared between the communicating agents. A sufficient complexity of nervous system, through the appearance of mirror neurons that are fired both when a subject executes certain action and when he observes another subject performing a similar action, represents a prerequisite for the emergence of reflexive codes in evolution. The codes appearing as a result of reflexive awareness and establishing different sociotypes, span from the symbolic systems of art and music through the common language to the formal language of logic and mathematics. Social dynamics is based on the implementation of reflexive coding activity and results in the growth and decay of social systems and civilizations.

This work presents a comprehensive model that aims to unify our understanding of embryogenesis, ageing, and cancer. While there have been previous attempts to construct models separately for two of these phenomena (such as embryogenesis and cancer, ageing and cancer), models encompassing all three are relatively scarce, if not entirely absent. The model's most notable feature is the presence of driver cells throughout the body, which may correspond to Spemann's organisers. These driver cells play a vital role in propelling development as they dynamically emerge from non-driver cells and inhabit specialised niches. Remarkably, this continuous process persists throughout an organism's entire lifespan, signifying that development unfolds from conception to the end of life. Driver cells orchestrate change events through the induction of distinctive epigenetic patterns of gene activation. Events occurring at young age drive development, are subject to high evolutionary pressure and hence carefully optimised. Events occurring after reproduction age are subject to decreasing evolutionary pressure: for this reason, such events are "pseudorandom" -deterministic but erratic. Some of these events lead to age-related benign conditions, such as gray hair. Some lead to serious age-related diseases, such as diabetes and Alzheimer's disease. Furthermore, some of these events might perturb epigenetically key pathways involved in driver activation and formation, leading to cancer. In our model, this driver cell-based mechanism represents the backbone of multicellular biology: understanding and correcting its functioning may give the chance to solve a wide range of conditions at once.

Ecological codes have been defined as every biological code integrated by factors originated by the environmental context that participates in the codepoiesis process. Ecological codes create a strict relationship between the inner world of organsims and the external relational world, and represent the mechanism with which the vivo-scape is realized. Acoustic codes are used in nature to decode acoustic signals between individuals of the same or different species and belong to the category of biological codes. Ecoacoustic codes are the outcome of the evolution of acoustic codes, and results as the interplay between acoustic codes and environmental factors. Soundtope codes represent the results of emerging properties of the acoustic communities.

The paper simulates the process of the migration of a single energy excitation along a chain of tryptophans in cell microtubules connected by dipole-dipole interaction. The paper shows that the excited states propagation rate falls within the range of nerve impulse velocity. It was shown that such a process also causes a transfer of quantum entanglement between tryptophans, so that microtubules can be considered as signaling system, the basis for transmitting information via the quantum channel. The conditions under which the migration of entangled states in the microtubule is possible are obtained. In a certain sense, it allows us to argue that the signal function of tryptophans works as an analogue of a quantum repeater that transmits entangled states over microtubule by relaying through intermediate tryptophans. Thus, the paper shows that the tryptophan system can be considered as an environment that supports the existence of entangled states during the time comparable with the time of the processes in biosystems.

Adaptations in equine limbs, including fusion of its bones, such as the metapodials II and IV (splint bones) with metapodial III (cannon bone), have been debated. It is argued that cursorial adaptations in equine limbs with a progressive reduction in the number of digits lighten up the limbs and facilitate fast running. Similar fusions in other bones, such as the radius and ulna, tend to exhibit similar traits. For example, the fusion often occurs in a disto-proximal direction while leaving sharp ridges. This article proposes a model to understand the biomechanical advantages that a fused symmetric system of cannon and splint bones offers and tries to investigate osseous fusion from an evolutionary perspective. It demonstrated the uniqueness of this system from the context of symmetry and the presence of an array of parallel axes of rotation in equine limbs that amplifies the kinetic energy by maximizing the moment of inertia. The model highlighted the robustness of such an evolutionary mechanism in place and elucidated why it is a preferable construct. Furthermore, the discourse proposes a zipping action to describe the osseous fusion before making predictions on where the equine limb adaptations are perhaps heading.

Formalization of autopoiesis is an ongoing effort among theoretical biologists. In this field, Letelier and co-authors proposed that Robert Rosen's (M,R)-systems theory be used as a formalism for autopoiesis. In (M,R)-systems theory, Rosen proposes that one solve a set of functional closure equations (FCEs) which account for all of the components of the system as coming from within the system itself. A key part of the functional closure equations is the repair of the metabolism component of the system. Rosen's theory gives the organizational closure of the components as well as their products, as found in autopoiesis. However, according to Razeto-Barry (M,R)-systems leaves out some of the messiness and approximation that we find in autopoiesis as he reformulates it. A related problem is that though FCEs have a long history, they are difficult in practice to solve due to their mathematical formulation. In this paper we give a novel exact solution for the FCEs for continuous real vector-valued functions which is nevertheless difficult to compute. In addition we propose an extended form of FCEs which both captures more of the messiness of autopoiesis and also helps to make the FCEs more solvable. Finally, we use our solution for the extended FCEs to give an extended repair function for a metabolism taken from a representative class of biological dynamics for gene expression (the repressilator). More generally we show that one can use our solution for the extended FCEs to get an extended repair function for continuous real vector-valued functions.

We propose a theoretical model to investigate the thermodynamics of single and coupled two-state ion channels, associated with mechanoelectrical transduction (MET) and hair cell biophysics. The modeling was based on the Tsallis nonextensive statistical mechanics. The choice for a nonextensive framework in modeling ion channels is encouraged on the fact that we take into account the presence of interactions or long-range correlations in the dynamics of single and coupled ion channels. However, the basic assumptions that support Boltzmann-Gibbs statistics, traditionally used to model ion channel dynamics, state that the system is formed by independent or weakly interacting elements. Despite being well studied in many biological systems, the literature has not yet addressed the study of both entropy and mutual information related to isolated or physically interacting pairs of MET channels. Inspired by hair cell biophysics, we show how the presence of nonextensivity, or subadditivity and superadditivity modulates the nonextensive entropy and mutual information as functions of stereocilia displacements. We also observe that the magnitude of the interaction between the two channels, given by a nonextensive parameter, influences the amplitude of the nonextensive joint entropy and mutual information as functions of the hair cell displacements. Finally, we show how nonextensivity regulates the current versus displacement curve for a single and a pair of interacting two-state channels. The present findings shed light on the thermodynamic process involved in the molecular mechanisms of the auditory system.

We address semiotic features of genetic coding, primarily the mechanisms for distinguishing between triplets. It implies that the minimal elements that allow codon recognition and amino acid coding should be identified. Half a century ago, linguist Roman Jakobson and microbiologist François Jacob revealed functional similarities between nucleotides in the genetic code and phonemes in natural language. Developing this analogy, we introduce the concept of a semiotic nucleotide. Unlike "material" nucleotides, its characteristics are limited to the function of differentiation within the processing of genetic information. We demonstrate that, similarly to phonemes, nucleotides are also non-elementary entities and can be represented as a set of two differential features: a) the number of rings and b) the number of hydrogen bonds. This makes it possible to convert semiotic nucleotides into double-byte units of digital information. Proceeding from this assumption, we suggest a new vision of such phenomena as the heterogeneity of the genetic code in terms of coding types, to reveal the code-distinguishing potential of positions within triplets, and represent wobbling as a specific reading regime. All these phenomena relate to the peculiarity of the triplet's third position, where complete or partial neutralization of distinguishing features is possible.

Primary myelofibrosis is an untreatable age-related disorder of haematopoiesis in which a break in the crosstalk between progenitor Haematopoietic Stem Cells (HSCs) and neighbouring mesenchymal stem cells causes HSCs to rapidly proliferate and migrate out of the bone marrow. Around 90% of patients harbour mutations in driver genes that all converge to overactivate haematopoietic JAK-STAT signalling, which is thought to be critical for disease progression, as well as microenvironment modification induced by chronic inflammation. The trigger to the initial event is unknown but dysregulated thrombopoietin (TPO) and Toll-Like Receptor (TLR) signalling are hypothesised to initiate chronic inflammation which then disrupts stem cell crosstalk. Using a systems biology approach, we have constructed an intercellular logical model that captures JAK-STAT signalling and key crosstalk channels between haematopoietic and mesenchymal stem cells. The aim of the model is to decipher how TPO and TLR stimulation can perturb the bone marrow microenvironment and dysregulate stem cell crosstalk. The model predicted conditions in which the disease was averted and established for both wildtype and ectopically JAK mutated simulations. The presence of TPO and TLR are both required to disturb stem cell crosstalk and result in the disease for wildtype. TLR signalling alone was sufficient to perturb the crosstalk and drive disease progression for JAK mutated simulations. Furthermore, the model predicts probabilities of disease onset for wildtype simulations that match clinical data. These predictions might explain why patients who test negative for the JAK mutation can still be diagnosed with PMF, in which continual exposure to TPO and TLR receptor activation may trigger the initial inflammatory event that perturbs the bone marrow microenvironment and induce disease onset.

Metabolic control analysis has long been used as a systemic model of the genotype-phenotype (GP) relationship. By considering kinetic parameters and enzyme concentrations as reflecting the genotype level and metabolic fluxes or pools as phenotypes related to fitness, MCA has given a biological basis to the relationship between these two levels. The non-linear and concave relationship between enzymes and fluxes can account for common genetic effects that reductionist approaches have been powerless to explain, such as the dominance of active alleles over less active alleles, the various types of epistasis and heterosis, and reveals the structural links between these genetic effects. The summation property of the flux control coefficients accounts for the L-shaped distribution of Quantitative Trait Locus (QTL) effects, irrespective of other possible causes. Metabolic models of response to selection results in evolutionary scenarios that are markedly different from those derived from the classical infinitesimal model of quantitative genetics. In particular, evolution towards selective neutrality appears to be a consequence of the diminishing return of the flux-enzyme relationship. In this paper, we survey the historical and recent achievements of MCA in genetics, quantitative genetics and evolution, focusing on epistasis and the evolution of flux in relation to enzyme concentrations.

The gene encoding the spike glycoprotein of the SARS-CoV-2 virus that causes COVID-19 disease, was analyzed through two types of periodic tables (standard and cube) of the genetic code to discover the internal fine structure of the spike (S) protein. The analysis was performed on the Wuhan-Hu-1 SARS-CoV-2 sequence (GenBank accession number NC_045512.2). A partition was detected between codon numbers (three-letter code numbers) 47 and 48 that code amino acids in the S-protein. The population distribution of organized codes and amino acid replacements in the S-protein showed large differences between two regions of the cube-type periodic table. The genetic codes of codon numbers 48-63 (4th plane of the cube table) had a higher frequency than the genetic codes of each of the other three planes (1st-3rd planes). Planes-linkage structures involved in the partition were also analyzed and a simplified model for the S-protein gene was obtained where a planes-linkage of the 4th plane and another planes-linkage of the 1st-3rd planes were linked together in alternate shifts. Most of the code population in the 4th plane and their planes-linkage multiformity gave additional support to the partition between codon numbers 47 and 48 in the S-protein gene. Analysis of real lineages of the SARS-CoV-2 virus through the cube-type periodic table identified distinguishing features of the Omicron lineage that included not only a large code population within the receptor-binding domain of the S-protein, but also large percentage rises in the population of amino acid replacements in the 1st and 2nd planes.

What is an organism? In the absence of a fundamental biological definition, what constitutes a living organism, whether it is a unicellular microbe, a multicellular being or a multi-organismal society, remains an open question. New models of living systems are needed to address the scale of this question, with implications for the relationship between humanity and planetary ecology. Here we develop a generic model of an organism that can be applied across multiple scales and through major evolutionary transitions to form a toolkit, or bio-organon, for theoretical studies of planetary-wide physiology. The tool identifies the following core organismic principles that cut across spatial scale: (1) evolvability through self-knowledge, (2) entanglement between energy and information, and (3) extrasomatic "technology" to scaffold increases in spatial scale. Living systems are generally defined by their ability to self-sustain against entropic forces of degradation. Life "knows" how to survive from the inside, not from its genetic code alone, but by utilizing this code through dynamically embodied and functionally specialized flows of information and energy. That is, entangled metabolic and communication networks bring encoded knowledge to life in order to sustain life. However, knowledge is itself evolved and is evolving. The functional coupling between knowledge, energy and information has ancient origins, enabling the original, cellular "biotechnology," and cumulative evolutionary creativity in biochemical products and forms. Cellular biotechnology also enabled the nesting of specialized cells into multicellular organisms. This nested organismal hierarchy can be extended further, suggesting that an organism of organisms, or a human "superorganism," is not only possible, but in keeping with evolutionary trends.

The ETS domain transcription factor family is one of the major transcription factor superfamilies that play regulatory roles in development, cell growth, and cancer progression. Although different functions of ETS member proteins in the nervous system have been demonstrated in various studies, their role in neuronal cell differentiation and the evolutionary conservation of its target genes have not yet been extensively studied. In this study, we focused on the regulatory role of ETS transcription factors in neuronal differentiation and their functional evolution by comparative transcriptomics. In order to investigate the regulatory role of ETS transcription factors in neuronal differentiation across species, transcriptional profiles of ETS members and their target genes were investigated by comparing differentially expressed genes and gene regulatory networks, which were analyzed using human, gorilla, mouse, fruit fly and worm transcriptomics datasets. Bioinformatics approaches to examine the evolutionary conservation of ETS transcription factors during neuronal differentiation have shown that ETS member proteins regulate genes associated with neuronal differentiation, nervous system development, axon, and synaptic regulation in different organisms. This study is a comparative transcriptomic study of ETS transcription factors in terms of neuronal differentiation using a gene regulatory network inference algorithm. Overall, a comparison of gene regulation networks revealed that ETS members are indeed evolutionarily conserved in the regulation of neuronal differentiation. Nonetheless, ETS, PEA3, and ELF subfamilies were found to be relatively more active transcription factors in the transcriptional regulation of neuronal differentiation.

This work explores whether holding the last aspiration for a period of time can promote cooperation. Specifically, an evolutionary spatial prisoner's dilemma game mode is proposed, in which the players adjust strategies and aspirations by considering the payoff and environment. Therefore, the core is to allow players to hold the current aspiration for a period of time. Through numerical calculation, this study finds that the existence of an appropriate duration of aspiration can promote cooperation when b is less than a certain value. Moreover, the cooperation is gradually enhanced with the increase of T-max (maximum aspiration duration) when b is greater than it, but the enhancing effect is limited. It is also found that an appropriate value α (sensitivity to environmental change) can promote cooperation at different b intervals. Besides, this system indicates good robustness. Overall, this study provides a new perspective on exploring cooperative evolution based on aspiration.

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.

The glycolytic flux, and in particular lactate production, is strongly increased in cancer cells compared to normal cells, a characteristic often referred to as aerobic glycolysis or the Warburg effect. This makes the glycolytic pathway a potential drug target, in particular if the flux control distribution in the pathway has shifted due to the metabolic reprogramming in cancer cells. The flux response of a drug is dependent on both the sensitivity of the target to the drug and the flux control of the target, and both these characteristics can be exploited to obtain selectivity for cancer cells. Traditionally drug development programs have focused on selective sensitivity of the drug, not necessarily focussing on the flux control of the target. We determined the flux control of two steps that have been suggested to have high control in cancer cells, using two inhibitors, iodoacetic acid and 3-bromopyruvate, and measured a flux control of the glyceraldehyde 3-phosphate dehydrogenase close to zero, while the hexokinase holds 50% of all flux control in glycolysis in an invasive cancer cell line MDA-mb-231.

Collagen synthesis is severely diminished in osteoarthritis; thus, enhancing it may help the regeneration of cartilage. Collagen synthesis is submitted to a large procollagen cycle where the greater part of the newly synthesized protein is degraded inside the cell producing a huge waste of material and energy. We have applied the Metabolic Control Analysis approach to study the control of collagen synthesis flux by means of the response coefficients of the flux with respect to glycine, proline and lysine. Our results show that the main cause of the procollagen cycle is a protein misfolding mainly due to glycine scarcity, as well as a moderate deficiency of proline and lysine for collagen synthesis. Thus, increasing these amino acids in the diet (especially glycine) may well be a strategy for helping cartilage regeneration by enhancing collagen synthesis and reducing its huge waste in the procollagen cycle; this possibly contributes to the treatment and prevention of osteoarthritis.

By analysing a large set of models obtained from the JWS Online and Biomodels databases, we tested to what extent the disequilibrium ratio can be used as an estimator for the flux control of a reaction, a discussion point that was already raised by Kacser and Burns, and Heinrich and Rapoport in their seminal MCA manuscripts. Whereas no functional relation was observed, the disequilibrium ratio can be used as an estimator for the maximal flux control of a reaction step. We extended the original analysis of the relationship by incorporating the overall pathway disequilibrium ratio in the expression, which made it possible to make explicit expressions for flux control coefficients.

A rich literature has grown up over the years that bears with autopoiesis, which tends to assume that it is a model, a theory, a principle, a definition of life, a property, refers to self-organization or even to hastily conclude that it is hylomorphic, hylozoist, in need of reformulation or to be overcome, making its status even more unclear. Maturana insists that autopoiesis is none of these and rather it is the causal organization of living systems as natural systems (NS) such that when it stops, they die. He calls this molecular autopoiesis (MA), which comprises two domains of existence: that of the self-producing organization (self-fabrication) and that of the structural coupling/enaction (cognition). Like all-NS in the universe, MA is amenable to be defined in theoretical terms, i.e. encoded in mathematical models and/or formal systems (FS). Framing the multiple formal systems of autopoiesis (FSA) into the Rosen's modeling relation (a process of bringing into equivalence the causality of NS and the inferential rules of FS), allows a classification of FSA into analytical categories, most importantly Turing machine (algorithmic) vs non-Turing machine (non-algorithmic) based, and FSA with a purely reactive mathematical image as cybernetic systems, i.e. feedbacks based, or conversely, as anticipatory systems making active inferences. It is thus the intent of the present work to advance the precision with which different FS may be observed to comply (preserve correspondence) with MA in its worldly state as a NS. The modeling relation between MA and the range of FS proposed as potentially illuminating their processes forecloses the applicability of Turing-based algorithmic computational models. This outcome indicates that MA, as modelled through Varela's calculus of self-reference or more especially through Rosen's (M,R)-system, is essentially anticipatory without violating structural determinism nor causality whatsoever, hence enaction may involve it. This quality may capture a fundamentally different mode of being in living systems as opposed to mechanical-computational systems. Implications in different fields of biology from the origin of life to planetary biology as well as in cognitive science and artificial intelligence are of interest.

Qualia have traditionally been considered difficult to measure objectively, but with the recent spread of fMRI (functional Magnetic Resonance Imaging) and other techniques, various experimental efforts have been made. In this paper, focusing on the qualia for color, we created 6 colors with different RGB values for reference colors of RED, light GREEN, BLUE, YELLOW, and PURPLE, and showed them to 306 subjects. For example, for RED and 5 generated colors, we asked them, "Choose a color that you feel is RED," and asked them to choose. A probability density function was defined for each of the five generated colors and the reference color, which is the primary color of RED, light GREEN, BLUE, YELLOW, and PURPLE, and the Kullback-Leibler divergence between the probability density function of the reference color and the generated color was calculated, the relationship between the number of samples of the selected color and the Kullback-Leibler divergence was obtained, and the difference in color sensation-qualia was calculated accordingly. As a result, it was confirmed that the larger the distance of the Kullback-Leibler divergence, the smaller the number of samples, but that the distribution shape in which the number of samples decreased for each color differed greatly. This suggests that if we see a color such as RED to PURPLE, we are randomly choosing a color that "feels."

Drug design and optimization are challenging tasks that call for strategic and efficient exploration of the extremely vast search space. Multiple fragmentation strategies have been proposed in the literature to mitigate the complexity of the molecular search space. From an optimization standpoint, drug design can be considered as a multi-objective optimization problem. Deep reinforcement learning (DRL) frameworks have demonstrated encouraging results in the field of drug design. However, the scalability of these frameworks is impeded by substantial training intervals and inefficient use of sample data. In this paper, we (1) examine the core principles of deep or multi-objective RL methods and their applications in molecular design, (2) analyze the performance of a recent multi-objective DRL-based and fragment-based drug design framework, named DeepFMPO, in a real-world application by incorporating optimization of protein-ligand docking affinity with varying numbers of other objectives, and (3) compare this method with a single-objective variant. Through trials, our results indicate that the DeepFMPO framework (with docking score) can achieve success, however, it suffers from training instability. Our findings encourage additional exploration and improvement of the framework. Potential sources of the framework's instability and suggestions of further modifications to stabilize the framework are discussed.

Following Ervin Bauer we accept that a living system can be characterized by its stable nonequilibrium. We represent such a system by a model hierarchy and relate system stability to computational delays across the hierarchy. For natural computation across the system assembly we advocate chaotic computation, and evaluate computational delay at the different organizational levels of the hierarchy. We compute inter-elemental access speed for the atomic and cell levels, and conclude that cell-related speed is 1000-10,000 times that of the atom level, confirming that on moving from the system-as-itself level towards the system-as-atoms level overall level access speed reduces. We conclude that Bauer's living system description as a stable nonequilibrium is justified.

Although there have been many studies revealing that biomarker genes for early cancer detection can be found in biomolecular networks, no proper tool exists to discover the cancer biomarker genes from various biomolecular networks. Accordingly, we developed a novel Cytoscape app called C-Biomarker.net, which can identify cancer biomarker genes from cores of various biomolecular networks. Derived from recent research, we designed and implemented the software based on parallel algorithms proposed in this study for working on high-performance computing devices. We tested our software on various network sizes and found the suitable size for each running mode on CPU or GPU. Interestingly, using the software for 17 cancer signaling pathways, we found that on average 70.59% of the top three nodes residing at the innermost core of each pathway are biomarker genes of the cancer respectively to the pathway. Similarly, by the software, we also found 100% of the top ten nodes at both cores of Human Gene Regulatory (HGR) network and Human Protein-Protein Interaction (HPPI) network are multi-cancer biomarkers. These case studies are reliable evidence for performance of cancer biomarker prediction function in the software. Through the case studies, we also suggest that true cores of directed complex networks should be identified by the algorithm of R-core rather than K-core as usual. Finally, we compared the prediction result of our software with those of other researchers and confirmed that our prediction method outperforms the other methods. Taken together, C-Biomarker.net is a reliable tool that efficiently detects biomarker nodes from cores of various large biomolecular networks. The software is available at https://github.com/trantd/C-Biomarker.net.

A palaeontological analysis of the evolutionary steps of Metazoa is tracing back the appearance and first steps of information behaviour as far as the Proterozoic Eon. Either the neural cell or the nervous system or the eyesight did not trigger the appearance of the information behaviour, but it did a novel way of diet. Carnivorous diet appeared on Earth slightly before the information behaviour as a completely new way of feeding - and what is more important: the application of light into the behavioural complexes as a radically innovative survival supporting tool. A genetic toolkit was ready for Metazoa, and the combination of the neural system, eyesight and carnivorous diet initiated the information behaviour. We provide an answer for this simple question: why did the carnivorous diet result in the first disruptive innovation in information behaviour? The junction of Big History, palaeontology and information history provides many challenging new aspects for further research.

Biological processes are characterized by a decrease in entropy in apparent violation of the second law of thermodynamics. Information stored in genomes help to solve this paradox when interpreted under the relationship between information and energy stated by Brillouin in the 1950's. However, the origins of living forms from inanimate matter which have no information storage device remains an open question. In this paper, a theoretical approach is developed on this issue. The replication of a simple entity with a binary genome is assumed to require an information-equivalent energy in addition to the standard activation energy. It is found that, in some conditions, a decrease in entropy can be accomplished together with a decrease in Gibbs free energy. An equation of the total energy for the replication of this entity is derived. Three factors are predicted to lower this energy: a small number of states of the coding sequence, a lower temperature, and a high ratio of the reaction on diffusion coefficients. These factors may have favoured the emergence of evolutionary demons-information storage devices that are able to decrease entropy. It is evaluated that some short, single-stranded RNA sequences made only of G and of C may conform to this model. The consequences of this model and its predictions on the origins of life on Earth and on other planets are discussed.

While allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potential curative therapy against hematological malignancies, modulation of donor T cell alloreactivity is required to enhance the graft-versus-leukemia (GVL) effect and control graft-versus-host-disease (GVHD) after allo-HSCT. Donor-derived regulatory CD4+CD25+Foxp3+ T cells (Tregs) play a central role in establishing of immune tolerance after allo-HSCT. They could be a key target to be modulated for increasing the GVL effect and control of GVHD. We constructed an ordinary differential equation model incorporating bidirectional interactions between Tregs and effector CD4+ T cells (Teffs) as a mechanism for control of Treg cell concentration. The goal is to elucidate how the interaction between Tregs and Teffs is modulated in order to get insights into fine tuning of alloreactivity after allo-HSCT. The model was calibrated with respect to published Treg and Teff recovery data after allo-HSCT. The calibrated model exhibits perfect or near-perfect adaptation to stepwise perturbations between Treg and Teff interactions, as seen in Treg cell populations when patients with relapsed malignancy were treated with anti-CTLA-4 (cytotoxic T lymphocyte-associated antigen 4). In addition, the model predicts observed shifts of Tregs and Teffs concentrations after co-stimulatory receptor IL-2R or TNFR2 blockade with allo-HSCT. The present results suggest simultaneous blockades of co-stimulatory and co-inhibitory receptors as a potential treatment for enhancing the GVL effect after allo-HSCT without developing GVHD.

Full-length nucleotide sequences of avian influenza A virus neuraminidase coding region (20,631 sequences) were analyzed and compared with those isolated from viruses infecting human and swine (63,750 sequences). If in fourfold degenerate sites there is asymmetric A-bias that may be more or less asymmetric depending on the type of neuraminidase and the host, than in twofold degenerate sites from third codon positions there is a strong asymmetric U-bias in coding regions of N4, N5, and N8 isolated from viruses infecting birds, as well as in those of N1 and N2 isolated from viruses infecting human, swine, and birds, while in coding regions of N9 isolated from birds, there is surprisingly strong C-bias, and in sequences of N3, N6, and N7 the usage of C is quite close to the level of U. Revealed stabilization of both U and C in twofold degenerate sites is the evidence of frequent changes in mutational pressure direction. Asymmetric mutational pressure was one of the sources of amino acid replacements that resulted in an equal percentage of sites with appeared and disappeared linear B-cell epitopes in N1, N2, N4, and N5 (33.62-35.33% vs. 32.41-36.45%, respectively), and controlled by the immune pressure it resulted in a stronger tendency to disappear for B-cell epitopes of N3, N6, N7, N8, and N9 of avian viruses (8.74-28.77% vs. 28.96-38.89%). The lack of correlation between nucleotide usages in fourfold and twofold degenerate sites for three nucleotides, except U, is a strong evidence of mutational pressure theory.

The Fisher's fundamental theorem of natural selection (FTNS) is a matter of longstanding debate in the community of mathematical biologists. Many researchers proposed different clarifications and mathematical reconstructions of the Fisher's original statement. The present study is motivated by our opinion that the controversy can be resolved by examining the Fisher's statement within the framework of two mathematical theories that are inspired by the Darwinian formalism: evolutionary game theory (EGT) and evolutionary optimization (EO). We present four rigorous formulations (some of them previously reported) of FTNS in four different setups that come from EGT and EO. Our study demonstrates that FTNS in its original form is correct only in certain setups. In order to be recognized as a universal law, the Fisher's statement should be: (a) clarified and completed and (b) relaxed by replacing the words "is equal to" with "does not exceed". Moreover, the real meaning of FTNS can be best understood from the information-geometric point of view. Such an approach shows that FTNS imposes an upper geometric bound on information flows in evolutionary systems. In this light, FTNS appears to be a statement about the intrinsic time scale of an evolutionary system. This leads to a novel insight: FTNS is an analogue of the time-energy uncertainty relation in physics. This further emphasizes a close relation with results on speed limits in stochastic thermodynamics.

In macro-public finance, the Ramsey rule (RR) concerns variable taxation to maximize social welfare and economic efficiency in a purely competitive monopolistic system. To extract tax revenue with the least loss of utility to the representative individual, RR dictates that optimal, proportionate taxes should be such as to diminish in the same proportion the production of each commodity taxed. The sources of supply that are inelastic, i.e., necessities/utilities, must be taxed more. We hypothesize that the Ramsey's economical approach might provide a general mechanism to investigate far-flung biological issues, such as preys/predators dynamics, food restriction in ecological niches, local changes in blood flow in rival or complementary organs of multicellular organisms. In particular, RR suggests a quantifiable relationship between the physiological decrease in cortical spike frequency occurring during sleep and energy consumption. Since small decreases in spike frequency during sleep are correlated with large decreases in the amount of consumed ATP, the brain could be considered an inelastic commodity which can be "taxed" more than other organs, allowing the whole organism to spare energy. Shedding light on the energy budget of the central nervous system, RR improves our knowledge of cerebral perfusion during sensory-evoked responses and tissue hypoxia caused by decreased blood flow, suggesting that energy from outside can be provided to counteract brain ischemia. In sum, the economical approach provided by Ramsey stands for a useful methodological tool that could be used in biological contexts to investigate the dynamical correlations among different organs in multicellular organisms.

We run stochastic simulations of the spatial version of the rock-paper-scissors game, considering that individuals use sensory abilities to scan the environment to detect the presence of enemies. If the local dangerousness level is above a tolerable threshold, individuals aggregate instead of moving randomly on the lattice. We study the impact of the locally adaptive aggregation on the organisms' spatial organisation by measuring the characteristic length scale of the spatial domains occupied by organisms of a single species. Our results reveal that aggregation is beneficial if triggered when the local density of opponents does not exceed 30%; otherwise, the behavioural strategy may harm individuals by increasing the average death risk. We show that if organisms can perceive further distances, they can accurately scan and interpret the signals from the neighbourhood, maximising the effects of the locally adaptive aggregation on the death risk. Finally, we show that the locally adaptive aggregation behaviour promotes biodiversity independently of the organism's mobility. The coexistence probability rises if organisms join conspecifics, even in the presence of a small number of enemies. We verify that our conclusions hold for more complex systems by simulating the generalised rock-paper-scissors models with five and seven species. Our discoveries may be helpful to ecologists in understanding systems where organisms' self-defence behaviour adapts to local environmental cues.

Autonomy, meaning freedom from exogenous control, requires independence of both constitution and cybernetic regulation. Here, the necessity of biological codes to achieve both is explained, assuming that Aristotelian efficient cause is 'formal cause empowered by physical force'. Constitutive independence requires closure to efficient causation (in the Rosen sense); cybernetic independence requires transformation of cause-effect into signal-response relations at the organism boundary; the combination of both kinds of independence enables adaptation and evolution. Codes and cyphers translate information from one form of physical embodiment (domain) to another. Because information can only contribute as formal cause to efficient cause within the domain of its embodiment, translation can extend or restrict the range over which information is effective. Closure to efficient causation requires internalised information to be isolated from the cycle of efficient causes that it informs: e.g. Von Neumann self-replicator requires a (template) source of information that is causally isolated from the physical replication system. Life operationalises this isolation with the genetic code translating from the (isolated) domain of codons to that of protein interactions. Separately, cybernetic freedom is achieved at the cell boundary because transducers, which embody molecular coding, translate exogenous information into a domain where it no longer has the power of efficient cause. Information, not efficient cause, passes through the boundary to serve as stimulus for an internally generated response. Coding further extends freedom by enabling historically accumulated information to be selectively transformed into efficient cause under internal control, leaving it otherwise stored inactive. Code-based translation thus enables selective causal isolation, controlling the flow from cause to effect. Genetic code, cell-signalling codes and, in eukaryotes, the histone code, signal sequence based protein sorting and other code-dependent processes all regulate and separate causal chains. The existence of life can be seen as an expression of the power of molecular codes to selectively isolate and thereby organise causal relations among molecular interactions to form an organism.

Ctenophores are invertebrate, gelatinous predators that perform complex movements due to their numerous ciliary comb plates. We investigated the behavioral responses of the ctenophore Mnemiopsis leidyi A. Agassiz, 1865 to red, green, and blue lights of different powers and fluxes emitted by LEDs or lasers. White LEDs were used to mimic natural sunlight. When laser light was directed to the aboral organ, the animals tended to leave the illumination zone. The blue-light reaction was six times faster than the red-light reaction. The behavioral strategy of the animals changed significantly when their freedom of maneuvering was restricted. Typical locomotions were ranked according to the laser beam avoidance time from the beginning of exposure to going into darkness. The minimum reaction time was required for turning and moving the ctenophore, while moving along the laser beam and turning around required more time. Typical patterns of behavior of M. leidyi in the light flux were established using cluster analysis. Three preferential behavioral strategies were identified for avoiding laser irradiation: 1) body rotation; 2) shifting sideways; and 3) movement with deviation from the beam. The elementary ability of ctenophores to make decisions in situative conditions has been demonstrated.

The flux distribution in metabolic network can be decomposed as non-negative linear combinations of elementary flux modes (EFMs). Identifying biologically relevant EFM combination by decomposing flux distribution in metabolic network is a useful method to study metabolisms in systems biology. However, the occurrence of biologically irrelevant EFMs hinders the application of such methods. In this paper, we introduce a novel method for identifying EFM combination by minimizing enzyme mass. Our proposed method, called EMMD (Enzyme Mass Minimization Decomposition), takes into consideration both thermodynamic and enzymatic constraints in stoichiometry metabolic models. By implementing EMMD, we can decompose the flux distributions in metabolic network to detect biologically relevant EFM combinations. We demonstrate the effectiveness of our method by applying it to the core Escherichia coli metabolic network and show that the optimal EFM combinations identified by EMMD are unique. Moreover, the optimal EFM combination identified by EMMD not only aligns more closely with experimental values in terms of estimated growth rate, but it also demonstrates more favorable thermodynamics. Finally, we investigated the growth of the core Escherichia coli metabolic network in Luria-Bertani medium containing different carbon sources, revealing the impact of various carbon sources on the growth rate of flux distribution. EMMD thus could be a promising complement to the existing flux decomposition tools.

A Locally active memristors can mimic neural synapses, resulting in rich neuro-morphological dynamics in biological neurons. To illustrate the impact of a local active memristive synapse, we consider coupled Hindmarsh-Rose (HR) neurons. Firstly, the dynamical transitions of the proposed system are investigated using bifurcation analysis and Lyapunov exponents, and we find that the transition between periodic and chaotic states depends on the input currents and memristive coupling strength. By performing the two-parameter analysis, the existence of periodic and chaotic regions is revealed. The collective behavior is then examined by expanding the network to include memristive coupled HR neurons under different network connectivities. We show that the system achieves synchronization behavior for all network connectivities, including regular, random, and small-world, when the strength of the memristive coupling is increased.

This study explores the food transport efficiency of termite using an individual-based model. Termites are believed to have evolved tunneling patterns that optimize food search and transport efficiency. However, few studies have investigated transport efficiency due to the difficulty of field observations. The model is characterized by four control variables: the number of simulated termites participating in transport (k1), the distribution of high curvature sections of the termite tunnel (k2), a quantity related to the density of the tunnel sections (k3), and the duration of traffic jams (k4). As k3 increases, the total length of the high curvature section decreases. Our simulation results show that the E(k1, k2) maps for k3 and k4 contain two modes: Mode A shows that E decreases with increasing k1 due to an increase in traffic jams, while Mode B shows E increasing with increasing k1 due to a decrease in the density of curved sections and an increase in jamming resolution time. The partial rank correlation coefficient analysis reveals that k1 and k2 have a negative effect on E, while k3 and k4 have a positive effect, with k1 having the greatest influence on E, followed by k3, k4, and k2. The ecological implications of the simulation results are briefly described, and the limitations of the model are discussed.