搜索结果：Theory in biosciences = Theorie in den Biowissenschaften

DNA, or deoxyribonucleic acid, exists in every human cell, including hair, blood, and skin, carrying the genetic blueprint for all living organisms. Comprised of two strands with four nucleotides-adenine (A), thymine (T), cytosine (C), and guanine (G)-DNA forms a double-helix structure that encodes species-specific traits. Its ability to store data and perform logical operations makes it crucial for biological research, particularly in genome sequencing, which involves complex nonlinear mathematical models. To address these challenges, nonlinear partial differential equations (NPDEs) effectively model DNA's dynamic behavior. The Atangana's conformable derivative accommodates memory effects and nonlocal properties, which are crucial in describing the viscoelastic and hereditary nature of biological systems such as DNA. Unlike integer-order derivatives, this approach captures the complexity of the molecular interactions and relaxation phenomena observed in DNA dynamics. Recent literature has supported the use of fractional models for DNA due to their ability to reflect real-world phenomena more accurately (e.g., base pair opening and long-range interactions). In this study, we explore fractional-order derivatives using Atangana's conformable derivative, applying the ψ - ϕ -expansion method to investigate double-chain DNA dynamical patterns. This method provides precise soliton solutions, such as one-soliton kinks, multiple-soliton solutions, and periodic waves, crucial for understanding DNA's optical properties. Solitons represent localized, stable wave packets that maintain their shape while propagating. In the context of DNA, these structures can model energy transmission along the chain without dispersion. This directly corresponds to base pair openings during transcription, where localized energy must be delivered and preserved to break hydrogen bonds selectively. Hence, solitons offer a feasible mathematical abstraction of physical mechanisms observed in transcription and DNA breathing. The visualized soliton solutions from the space-time fractional-order double-chain DNA model underscore the system's biological importance. The findings have potential applications in evaluating systems and refining scientific insights into DNA dynamics.

Can mathematical proofs be employed for the solution of fundamental molecular-level problems in biology? Recently, I mathematically tackled complex mechanistic problems arising during the synthesis of the universal biological currency, adenosine triphosphate (ATP) by the FOF1-ATP synthase, nature's smallest rotary molecular motor, using graph-theoretical and combinatorial approaches for the membrane-bound FO and water-soluble F1 domains of this fascinating molecule (see Nath in Theory Biosci 141:249‒260, 2022 and Theory Biosci 143:217‒227, 2024). In the third part of this trilogy, I investigate another critical aspect of the molecular mechanism-that of coupling between the FO and F1 domains of the ATP synthase mediated by the central γ-subunit of ∼ 1 nanometer diameter. According to Nath's torsional mechanism of energy transduction and ATP synthesis the γ-subunit twists during ATP synthesis and the release of stored torsional energy in the central γ-stalk causes conformational changes in the catalytic sites that lead to ATP synthesis, with 1 ATP molecule synthesized per discrete 120° rotation. The twisted γ-subunit breaks the symmetry of the molecule, and its residual torsional strain is shown to readily accommodate any symmetry mismatch existing between FO and F1. A mathematical number theory proof is developed to quantify the extent of symmetry mismatch at any angular position during rotation and derive the conditions for the regaining of symmetry at the end of a 360° rotation. The many chemical and biological implications of the mechanism and the mathematical proof are discussed in detail. Finally, suggestions for further mathematical development of the subject based on ideas from symmetry and group theory have been made. In sum, the answer to the question posed at the beginning of the Abstract is a resounding YES. There exists new, relatively unexplored territory at the interface of mathematics and molecular biology, especially at the level of molecular mechanism. It is hoped that more mathematicians and scientists interested in interdisciplinary work are encouraged to include in their research program approaches of this type-a mathematical proofs-inspired molecular biology-that have the power to lead to new vistas. Such molecular-scale mechanistic problems in biology have proved extraordinarily difficult to solve definitively using conventional experimental, theoretical, and computational approaches.

The origin of viruses is one of the central mysteries in evolutionary biology. Although mainstream hypotheses such as "reduction," "escape," and "virus-first" exist, they each face significant difficulties in explaining the motivation for establishing viral parasitism, the origin of the capsid, and the minimalism of RNA viruses. This paper aims to propose a new model named the "environmentally driven coordinated reduction" model, intended to provide a logical framework with a clear environmental driving mechanism for the "polyphyletic" origin of viruses, particularly the "reduction pathway." This model posits that an emergency dormancy program initiated by ancient prokaryotes to cope with extreme environmental conditions on the surface of the primitive ocean-including the active disassembly of organelles, streamlining of the genome, and the formation of multi-layered protective capsids-constituted the initial driving force for viral reduction. These "dormant bodies," upon returning to suitable environments and having lost the capacity for independent survival, saw the release of their genetic material passively evolve into parasitic infection. This model not only proposes an origin pathway for DNA viruses but also innovatively places the origin of RNA viruses as a secondary simplification product, resulting from a reversion to a more energy-efficient RNA system under energy depletion pressure, thereby naturally incorporating minimal molecules like viroids into the endpoint of its evolutionary path. In particular, this paper explicitly identifies the last universal common ancestor (LUCA) and its direct descendants as the primary source of viral evolution. The large gene pool possessed by LUCA not only provided a dispensable gene set as an energy reserve, but also significantly extended the maximum dormancy period of cells under extreme environments through the effect of genomic redundancy, thereby exponentially increasing the probability of the viralization transition. This mechanism is rigorously demonstrated through mathematical models in Chapter 7, revealing the quantitative relationship between LUCA's large genome and the probability of viral origin. Through a critical hypothetical review, this paper systematically analyzes the shortcomings of these classic hypotheses and, on this basis, provides a logical framework for a unified picture of viral origins, ultimately proposing specific predictions testable by computational biology and experimental evolutionary biology.

This paper re-examines the definition of life, critiquing and building upon Plante's recently proposed symbiotic, holistic, and gradualist framework. Plante's model integrates symbiosis across biological scales, holism to unify hierarchical complexity, and gradualism to address the continuum between non-living and living entities. While innovative, the model omits two critical factors underpinning life: information and water. These elements form the foundation for a novel approach based on informational dissipative dynamics and Prigogine-like structures. Water is posited as a dynamic, topological medium capable of encoding and transferring information via transient hydrogen-bond networks. This phenomenon creates "informational topologies" that guide the organization of molecules, bridging the gap between physical randomness and biological order. The proposed framework explores how water properties drive the emergence of autopoietic systems through the interplay of thermodynamic, informational, and quantum dynamics. The model introduces the concept of informational entropy gradients within water-molecule interactions, facilitating the iterative development of structured, dissipative systems. These gradients sustain the system far from equilibrium, enabling life complexity and persistence. As these systems evolve, the interplay of entropic gradients, dissipative energy, and information processing leads to increased order, self-replication, and, ultimately, the emergence of life. By re-framing life as an informational dissipative process, the paper bridges gaps in Plante's approach and proposes a broader, foundational understanding of biological systems. This perspective offers a unifying framework for exploring life origins, evolution, and complexity while highlighting water's indispensable role in shaping living systems.

The conflict between individual and collective interests makes fostering cooperation in human societies a challenging task, requiring drastic measures such as the establishment of sanctioning institutions. These institutions are costly because they have to be maintained regardless of the presence or absence of offenders. Here, we revisit some improvements to the standard N-person prisoner's dilemma formulation with institutional punishment in a well-mixed population, namely the elimination of overpunishment, the requirement of a minimum number of contributors to establish the sanctioning institution, and the sharing of its maintenance costs once this minimum number is reached. In addition, we focus on large groups or communities for which sanctioning institutions are ubiquitous. Using the replicator equation framework for an infinite population, we find that by sufficiently fining players who fail to contribute either to the public good or to the sanctioning institution, a population of contributors immune to invasion by these free riders can be established, provided that the contributors are sufficiently numerous. In a finite population, we use finite-size scaling to show that, for some parameter settings, demographic noise helps to fixate the strategy that contributes to the public good but not to the sanctioning institution even for infinitely large populations when, somewhat counterintuitively, its proportion in the initial population vanishes with a small power of the population size.

Plants exhibit rapid, coordinated responses to environmental stimuli despite lacking a central nervous system, prompting interest in non-classical signaling mechanisms. Recent findings in quantum biology indicate that quantum coherence and entanglement, previously considered too ephemeral for the hot, humid biological medium, could be the basis for certain types of plant signal transduction. This review integrates present knowledge on plant signaling networks and describes theoretical frameworks in which quantum behavior could be involved. Theoretical models, including site-based Hamiltonians for exciton transport in photosynthetic complexes, spin-Hamiltonian models of radical-pair processes in cryptochromes, and quantum percolation theories of plasmodesmatal transport, are reviewed. These models propose that plants might utilize quantum correlations to increase signal fidelity, energy efficiency, and adaptive response between tissues. Experimental evidence for coherence in photosynthesis and cryptochrome-mediated magnetoreception supports these models. Quantum entanglement is proposed to improve long-distance communication and energy transfer in plants. Implications for practical applications range from quantum-informed crop breeding, precision farming, and efficient resource management. Future research directions, including experimental verification of quantum signatures in vivo, are outlined, with implications for bio-inspired quantum engineering in agriculture. Combining quantum mechanics and plant biology provides a paradigm-changing view of plant communication and opens new interdisciplinary horizons in fundamental science and agricultural innovations.

Biological systems under chronic resource overload often exhibit asymmetric transitions into high-load states that are difficult to reverse. Within a bow-tie (hourglass) framework, such dynamics arise when diverse inputs are funnelled through a constrained regulatory core governing system-level responses. Here, lake eutrophication and human obesity are analysed as structurally distinct yet dynamically analogous manifestations of resource overload. In lakes, external nutrient inputs and internal biogeochemical feedbacks drive shifts from clear-water, macrophyte-dominated regimes to hypertrophic, phytoplankton-dominated states. In humans, sustained caloric surplus interacts with metabolic-hormonal regulation and behavioural-social drivers to promote the development and stabilisation of obesity. In both systems, reinforcing feedbacks organise around a central regulatory core, reducing flexibility, generating hysteresis, and constraining recovery trajectories. Despite these similarities, key asymmetries emerge. In lakes, dynamics under overload are dominated by a limited set of reinforcing feedbacks, whereas in obesity regulation remains distributed across interacting physiological, behavioural, and environmental domains. In both cases, responses involve cascade-like propagation of effects, taking the form of trophic cascades in lakes and cross-domain feedback cascades in humans. These results show that similar system-level dynamics, including alternative stable states, tipping points, and hysteresis with constrained reversibility, can arise from differently structured regulatory architectures. The comparison demonstrates that reversibility is system-specific and shaped by the organisation of the regulatory core and associated feedbacks. Interpreting eutrophication and obesity through a bow-tie framework provides a comparative, architecture-based perspective on resource overload and helps explain why effective interventions require coordinated actions targeting multiple components of the feedback structure.

The tumor microenvironment constitutes a complex system shaped by the intricate interactions among tumor cells, immune cells, and cytokines. Within this environment, the interplay between immune cells and cytokines is crucial in influencing tumor growth and progression. Despite advancements in clinical tumor immunotherapy, there remains a gap in comprehensive simulations of tumor immune responses, particularly regarding cytokine-driven processes. This study aims to address this gap by investigating the regulatory interactions among tumor cells, immune cells, and cytokines to simulate the complexities of tumor immunotherapy. We develop a comprehensive modeling and computational framework incorporating PD-1 inhibitors and interleukin-10 (IL-10) antibodies. Through detailed mathematical analysis, we elucidate the impact of changes in the immune microenvironment on tumor cells number. Our findings highlight the significant therapeutic effect of anti-PD-1 and IL-10 inhibitors, with increased drug dosage correlating with a reduction in tumor burden. Furthermore, combination therapy demonstrates a marked extension of survival with reduced dosages compared to monotherapy. Based on model simulations, we proposed prognostic predictions by assessing the microenvironmental status before treatment. The findings indicate a promising method for enhancing treatment effectiveness and offering potential advantages to patients receiving tumor immunotherapy.

This study presents a comprehensive analytical and artificial intelligence neural network (AINN)-based investigation of wave propagation in dry long bones, modeled as an orthotropic hollow cylindrical structure. The proposed model incorporates the combined effects of initial stress, magnetic field, and rotational motion to capture the complex behavior of bone-like media under coupled physical influences. The governing equations are formulated within a continuum mechanics framework and solved analytically using displacement potential methods, yielding solutions in terms of Bessel functions that satisfy the cylindrical geometry and boundary conditions. Two distinct cases, corresponding to the absence and presence of rotation, are examined to assess the influence of rotational effects on wave dynamics. A detailed parametric analysis is carried out to evaluate the variation of phase velocity and frequency with respect to wave number, initial stress, magnetic parameter, density, and geometric ratios. The analytical results indicate that initial stress enhances wave propagation, while magnetic effects introduce damping and rotation significantly modifies dispersion characteristics. To improve computational efficiency and predictive capability, an AINN model is developed and trained using analytically generated data. The AINN predictions show excellent agreement with the analytical results, as confirmed through parity plots, error analysis, residual distribution, and loss convergence behavior. The novelty of the study lies in the unified analytical-AINN framework that integrates mechanical, electromagnetic, and rotational effects within a single model. The findings provide important insights for wave-based characterization of bone structures and have potential applications in non-destructive evaluation, ultrasonic diagnostics, and advanced biomedical sensing systems.

The complexity and diversity inherent in living organisms have long been regarded as significant challenges to the formulation of a universally accepted definition of life. Life is manifested across diverse forms, characterized by properties such as growth, reproduction, responsiveness, adaptation, and homeostasis. However, these traits are not exclusively confined to living systems; they have also been observed, to varying extents, in certain non-living entities. As a result, the conceptual boundary between the living and the non-living has been rendered increasingly ambiguous. In this study, the transformation of non-living matter into living systems is investigated, and it is demonstrated that this process lacks a precise temporal threshold that clearly marks the emergence of life. Through mathematical analysis, it is shown that no comprehensive definition of life captures a distinct separation within the chemical continuum that leads from inanimate to animate matter. The absence of uniquely defining features that unequivocally distinguish living organisms from non-living entities is thereby revealed. This analysis challenges traditional assumptions regarding the definability of life, emphasizing the need for a revised conceptual framework that accounts for the continuum between non-living and living systems.

A mathematical analysis of influenza virus transmission is undertaken, combining rigorous theoretical development with numerical simulations informed by real-world data. The terms in the equations introduce parameters which are determined by fitting the model for matching clinical data sets using nonlinear least-square method. Wave patterns, critical illness factors, and forecasts of influenza transmission at national levels in Mexico, Italy, and South Africa are examined, alongside evaluations of the effectiveness of existing control measures and proposals for alternative policy interventions. Data for 120 weeks from October 2021 to March 2023 are used to fit the model. Numerical simulations and sensitivity analysis reveal the effectiveness of various prevention strategies. We performed data fitting using Latin hypercube sampling, sensitivity indices, Partial Rank Correlation Coefficient (PRCC), and p values to estimate the basic reproduction number R 0 and validate the model with data from these countries. Leveraging this validation, we identify optimal control strategies involving antiviral treatment protocols to suppress viral spread, reduce new infections, and minimize systemic costs. The existence and uniqueness of the optimal control pair are rigorously established, with the derived optimality system solved numerically. Additionally, we investigated the qualitative behavior of the threshold quantity, which determines whether the disease dies out or persists in the population. Finally, numerical experiments illustrate the impact of key parameters on transmission dynamics, corroborating theoretical predictions.

In biology, the concept of "living organism" has traditionally been based on the smallest level of organization comprising all the necessary and essential characteristics of life: the cell. Today, this concept is being challenged by the analysis of ambiguous biological entities, located both below and above the level of the living cell, which exhibit some of the characteristics of living organisms. This situation has given rise to an epistemological pluralism of the concepts of "organism", "individual" and "living", for which no clear and unanimous definition has yet been accepted. The aim of this manuscript is to explore new ideas and perspectives for defining the concept of "living organism", in order to eliminate a certain level of pluralism that could generate confusion, particularly in the pragmatic context of biological research. First, I expose the dualism of the concepts of "organism" and "individual" and suggest a fusion of these concepts in order to eliminate a certain level of pluralism. In doing so, I develop a symbiotic and holistic definition of the concept of "living organism", which includes different structural levels of the organism: molecular, cellular and ecosystems. Second, I present the epistemological problem of the concept of "living", which is closely related to the concepts of "organism" and "individual", by analyzing the list and gradational types of definition. In doing so, I propose a new symbiotic, holistic and gradualist model of the concept of "living organism", using a gradation of several properties of the living applied to the different structural levels of the organism developed previously (molecular, cellular, ecosystems).

This article characterizes Oyama's concept of ontogenesis of information formally. I apply the mathematical notion of synergistic information to the framework proposed by Griffiths et al. (2015) for developmental information and specificity. This allows us to examine the specificity revealed by the interaction of variables as a result of interventions on interactions. I define Developmental Synergistic Information as the specificity of interacting variables obtained by measuring how much mutual information interventions on interactions carry about the effect variable. To formalise this concept, I use partial information decomposition, one of the most robust frameworks for analysing synergistic information. Some examples of developmental synergistic information are presented. Finally, I consider the philosophical implications of developmental synergistic information, arguing that it supports important tenets of organism-centered biology: (i) synergistic information has ontogeny-order is generated in epigenesis; (ii) such information is non-transmissible through channels of inheritance-the specificity of outcomes must be reconstructed anew in each generation; (iii) the developmental organism (or any synergistic system under consideration) is itself a cause of development-causation resides in the coaction of developmental variables; and (iv) the developmental context of information must be taken into account-that developmental causation is always embedded in and constrained by a developmental matrix of other specifiers.

Due to predicted global climate change, there have been significant alterations in agricultural production patterns, which had a negative impact on ecosystems as well as the commercial and export prospects for the production of grapevines. The natural biochemistry of grapevines, including their chlorophyll content, net photosynthetic rate, Fv/Fm ratio, photorespiration, reduced yield, and quality is also anticipated to be negatively impacted by the various effects of light, temperature, and carbon dioxide at elevated scales. Grapevine phenology, physiology, and quality are impacted by the inactivation of photosystems (I and II), the Rubisco enzyme system, pigments, chloroplast integrity, and light intensity by temperature and increasing CO2 levels. Grape phenological events are considerably altered by climatic conditions; in particular, berries mature earlier, increasing the sugar-to-acid ratio. In enology, the sugar-to-acid ratio is crucial since it determines the wine's final alcohol concentration and flavour. As light intensity and CO2 levels rise, the biosynthesis of anthocyanins and tannins declines. As the temperature rises, the production of antioxidants diminishes, affecting the quality of raisins. Table grapes are more sensitive to temperature because of physiological problems like pink berries and a higher sugar-to-acidity ratio. Therefore, the systemic impact of light intensity, temperature, and increasing CO2 levels on grapevine physiology, phenology, photosystems, photosynthesis enzyme system, and adaptive strategies for grape producers and researchers are highlighted in this article.

The newly described CIRDD and IROD clusters of Type 2 Diabetes represent clinically meaningful phenotypes, yet no subtype-specific molecular or omics datasets currently exist to define their mechanisms. This study introduces a predictive extrapolation framework to infer how phytocompounds from Andrographis paniculata may interact with these subtypes by mapping established pathways of insulin resistance, β-cell dysfunction, and obesity-driven inflammation onto CIRDD- and IROD-relevant axes. From the broader T2DM network, ten hub proteins (INS, AKT1, TNF, IL6, MMP9, and others) were prioritized based on functional importance and subsequently assigned to each subtype according to their documented physiological roles. Docking analysis, supported by redocking validation against high-resolution crystallographic complexes, enabled assessment of theoretical ligand-protein interactions. The model predicts that CIRDD may be primarily modulated through β-cell regulatory hubs (INS, AKT1), whereas IROD appears more strongly influenced through inflammatory-metabolic hubs (TNF, TLR4, MMP9). Apigenin and andrographolide displayed the strongest predicted affinities (binding energies ≤ -8.5 kcal/mol), and redocking yielded RMSD values below 2.0 Å, supporting the reliability of the docking protocol. Overall, this work proposes a theoretical, biologically anchored framework for predicting subtype-specific phytochemical mechanisms in the absence of direct molecular datasets. While experimental validation is required, the approach offers a rational basis for prioritizing plant-derived candidates for CIRDD and IROD.

This study employs a three-tier food chain framework to examine the influence of the Allee effect on prey foraging efficiency and physiological stability. The model incorporates a Holling type II functional response between the prey and mesopredator, while the top predator is a sexually reproducing species that interacts with the mesopredator through a Crowley-Martin formulation. The system's sensitivity to variations in the half-saturation constant is analyzed to investigate bifurcation phenomena and the onset of chaos. Chaotic behavior is characterized quantitatively using the largest Lyapunov exponent, revealing that changes in the half-saturation constant strongly affect the system's dynamical complexity. To account for spatial processes, the model is extended into a diffusive framework, and the resulting reaction-diffusion system is analyzed for Turing instabilities. Analytical conditions for diffusion-driven instability are derived, and the emergence of spatial patterns is confirmed through numerical simulations. The findings indicate that mutual interference among predators can both destabilize and stabilize the system, depending on parameter values. Increased top predator interference tends to promote stability, whereas enhanced residual decline in predator normalization leads to instability.

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines.

This study investigates the thermodynamic and mathematical foundations of trophic dynamics in ecological systems, focusing on the Generalized Lotka-Volterra (GLV) model to analyze prey/predator interactions under resource constraints. We introduce a framework where species' contributions are governed by mean biomass and relative trophic strength, subject to physiological and ecological bounds. The total biomass function and abiotic reservoir are defined to quantify the balance between biotic and abiotic resources, with the functional capture of net energetic flux. Key results demonstrate that dynamic equilibrium is achievable only in trivial cases (extinction) when measures of historical interactions are absolutely continuous. In contrast, memory-free models, where species interactions are instantaneous, permit nontrivial equilibria under harmonic conditions, for the effective trophic potential harmonicity implies local equilibrium, ensuring smooth integration of species into the trophic network without abrupt disruptions. A two-species system (in a bidimensional case) illustrates phase space dynamics: Stability analysis highlights the sensitivity of ecosystems to competitive dynamics and resource influx, with structured ecological inputs that promote equilibrium. The study bridges theoretical ecology and dynamical systems, offering insight into the stability of trophic networks under varying constraints and setting an effective machinery that establishes a clear connection between classical GLV models and mass balance treatments in ecosystems.

Smart's model (SM) describing the geometry of avian eggs is, uniquely, based on physiological characteristics of eggs formation in oviduct walls transforming a sphere to an ellipsoid, to an ovoid. The purpose of this study was to revisit and perform a more in-depth examination of SM, providing a possible improvement in terms of reducing the number of initial parameters and compliance with geometric principles fundamental for bodies of revolution. SM requires measuring five egg parameters: length (L), maximum breadth (B), displacement of the central axis to the level of maximum breadth (w), and two radii of the egg at a point shifted by ¼L from the pointed (r) and blunt (R) ends, respectively. A practical test for the reproduction degree of three egg shape varieties using five-parameter model confirmed its maximum accuracy compared to all others. Modifications using four parameters (L, B, w and r or B0, which is egg diameter at ½L) were also relatively accurate, and only slightly inferior. Using three parameters (L, B and w) was clearly insufficient; however, one of our three-parameter models met the requirements of the "Main Axiom of the mathematical formula of the bird's egg". In our opinion, two of Smart's postulates, the point of applying an oviduct force to provide the appropriate egg shape and the equality of L and the length of original ellipsoid, were used as fixed initial premises, which allowed to exclude many other possible options and to derive a mathematical model. Such an assumption arose according to the theoretical studies presented herein. Nevertheless, Smart's formula derivation based on physiology of egg formation is a pioneering approach to the development of egg-shape models.