Acta Biotheoretica最新文献

Biological systems depend on communication over distances, ranging from molecular gradients to systemic neuroendocrine and neuroimmune circuits. While many distance effects in biology are explained by well-established mechanisms such as diffusion, paracrine signaling, neural conduction, and extracellular vesicle trafficking, there are also claims of long-distance influences that may be mediated by consciousness, electromagnetic fields, or hypothesized morphic fields. This review synthesizes controlled laboratory evidence, evaluates speculative mechanisms, including quantum field effects and morphic resonance, and compares them with well-replicated findings in immunology and bioelectromagnetics. The Constrained Disorder Principle (CDP) is a novel theoretical framework that posits variability within constraints as essential for biological function and may underlie some of these effects. The paper discusses the debate over methodological rigor and replicability in research on nonlocal biological effects. While evidence supports the importance of distance in biological communication through known carriers, claims regarding consciousness and morphic resonance remain unverified, despite challenges to their validity. Future research must strike a balance between openness and rigorous experimental standards.

To understand the dynamics of Alzheimer’s disease, we formulate a generalized mathematical model based on three events: aggregation of disease-related proteins, activation of immune cells and initiation of inflammation. We incorporate functional forms in the model to represent the complex biological interactions between components related to Alzheimer’s disease. We take explicit forms depending on the properties of functions in the model. We describe the system dynamics by locating biologically feasible steady states, determining stability properties and identifying the effective parameters. Parameters are estimated using two methods: biological literature and data fitting. We perform sensitivity and uncertainty analyses to identify the most influential parameters. Partial Rank Correlation Coefficient and scatter plots are used to visualize global sensitivity. Our results reveal that lower activation rate and higher proliferation rate of microglia may contribute to a reduction in toxic protein aggregate levels, thus slowing the disease’s early progression.

The construction of a circular code through a biological process, particularly a primitive one in the absence of the protein world, has remained an open problem since the discovery of a maximal (C^3) self-complementary trinucleotide circular code in genes in 1996 (Arquès and Michel, 1996). Circular codes are defined by their ability to recover the correct reading frame of genes at any position. While a class of 216 such trinucleotide codes has been identified, the KL method (Koch and Lehman, 1997), based on nucleotide probability products, generates only a restricted subclass of 88 (C^3)-codes (Lacan and Michel, 2001). Revisiting this probabilistic framework 25 years later, we demonstrate that various classes of dinucleotide circular codes can be generated using a nucleotide probability product model (called Construction 2). We introduce the concept of transitive dinucleotide codes and prove new theorems characterizing their circularity and comma-free properties. Using codon usage from bacteria, archaea, and eukaryotes, 2 “universal” maximal dinucleotide circular codes are observed: (D_{1,2}={AT, CA, CT, GA, GC, GT}) in the codon site (1-2) and (D_{2,3}) in the codon site (2-3) which can be deduced from (D_{1,2}) by 1-letter cyclical permutation (alpha _{1}) or identically by reversing permutation (D_{2,3} = alpha _{1}(D_{1,2}) = overset{longleftarrow }{D_{1,2}}). Unexpectedly, we then show that, under the independence assumption, the dinucleotide code (E_{1,2}) through Construction 2 from nucleotide frequencies in the codon sites 1 and 2, is a maximal dinucleotide circular code and is equal to the observed dinucleotide code: (E_{1,2} = D_{1,2}). These findings support a theoretical model in which dinucleotide circular codes may have originated from statistical properties of primitive nucleotide distributions, providing insights into the possible emergence of the genetic code.

Causal analysis of multiscale systems is crucial for understanding how biological networks are organized and function across scales, including the origin of life itself. This article introduces an axiomatic theory of causation based on the physical concept of cause. Its rigorous derivation from first principles allows us to formulate the law of conservation of causation, expressed in terms of the continuity equation in fluid dynamics. This law states that the flow of causation in dynamical, endogenously coarse-grained systems is conserved across scales. This means that these systems are causally renormalizable across scales; both upward and downward causation are forbidden. The theory also introduces a tool for calculating (i) the minimal linear chain necessary for a biological system to causally connect its two components at a distance, and (ii) the spatial span of the system, defined not as a metric volume of space occupied by the system but as the number of scales causally spanned by its dynamics. Finally, it raises age-old questions about reductionism and emergence, determinism and agency in living systems.

Reaction–diffusion epidemic models play a central role in understanding how infectious diseases propagate through space and time, offering valuable insight for public health analysis. A key element of such models is the incidence function, which governs the nonlinear interaction between susceptible and infected populations. Despite extensive studies on various incidence formulations, the systematic identification of a suitable one for a given setting remains an open question. This work introduces a theoretical framework that interprets the selection of an incidence function as quantifying the contribution of several plausible formulations to the overall transmission dynamics, inferred from observational data. The resulting problem takes the form of a PDE-constrained optimization, where the objective is to determine the optimal weights in a convex combination of incidence functions that best fit the observed epidemic patterns. The analysis establishes the Fréchet differentiability of the parameter-to-state operator and derives first-order optimality conditions via an adjoint system. A numerical illustration, based on the Landweber iteration method, highlights the framework’s potential as a mathematical tool to enhance modeling accuracy and support strategies aimed at disease prevention.

In this work, we investigate isomorphisms of graphs associated with the 216 maximal self-complementary (C^3)-codes over the genetic alphabet ({A,C,G,T}). Such codes play an important role in maintaining the correct reading frame during the translational process in the ribosome and have been classified into 27 equivalence classes under the action of the dihedral group (D_4). Naturally, this group action induces graph isomorphisms between the graphs associated with maximal self-complementary (C^3)-codes, as shown in Fimmel et al. (2016). However, we demonstrate here that these induced isomorphisms of the associated graphs are not the only graph isomorphisms between such codes. Specifically, we calculate the largely non-trivial automorphism groups of all the 216 graphs associated to maximal self-complementary (C^3)-codes and we show that no isomorphism exists between maximal self-complementary (C^3)-codes belonging to different equivalence classes. Finally, we provide examples illustrating that the assumptions of maximality, self-complementarity, or the (C^3)-property can not be omitted.

Bone remodeling is a fundamental physiological process that maintains skeletal integrity through a tightly regulated balance between bone resorption and formation. Mathematical modeling provides a powerful framework for understanding the complex regulatory mechanisms underlying this process, particularly the interplay between biomechanical stimuli and cellular dynamics. This study introduces a new mathematical model of bone remodeling designed to capture the temporal evolution of key bone cell populations and their response to mechanical stimuli within a simplified yet biologically informed architecture. We formulate a system of coupled nonlinear ordinary differential equations to describe the dynamics of osteoclasts, osteoblasts, and osteocytes within a single basic multicellular unit (BMU). The model incorporates feedback regulation via strain energy density, allowing mechanical input to influence cellular activity and bone surface transitions. Initial conditions are assigned to reflect the sequential activation of bone remodeling phases. Physiological parameters are adopted from well-established literature, while non-sourced parameters are tuned to ensure model stability and biological plausibility. The simulations reproduce the canonical sequence of bone remodeling: initiation, resorption, reversal, and formation. The model captures the coupling between mechanical loading and cellular activity, demonstrating how osteocyte signaling can modulate the recruitment of osteoclasts and osteoblasts. The results also highlight the system’s capacity to stabilize around a dynamic equilibrium, sensitive to both internal parameters and external mechanical inputs. This model offers a minimal yet comprehensive representation of bone remodeling dynamics within a single BMU, integrating mechanical and biological controls into a unified mathematical structure. It provides a foundation for future extensions toward spatially distributed models and applications in mechanobiological simulation and computational bone health assessment.

Measles remains a major global public health challenge despite available vaccines. To evaluate the combined impact of routine immunization and supplementary vaccination campaigns, we developed a compartmental mathematical model that incorporates declining immunity and breakthrough infections. We derived the control reproduction number using the next-generation matrix method, analyzed stability and bifurcation properties of the equilibria, and conducted sensitivity analysis to determine key drivers of transmission. The model was parameterized from the literature and calibrated to 2024 Nigerian measles case data using Markov Chain Monte Carlo sampling. The model revealed that the disease-free equilibrium is globally stable when the control reproduction number is less than one, but a backward bifurcation indicates that reducing the reproduction number below unity may not suffice for elimination. Sensitivity analysis identified the transmission rate among vaccinated individuals, breakthrough infections, and waning immunity as dominant drivers of transmission. Simulations demonstrated that while routine vaccination delays and reduces outbreak peaks, it does not interrupt transmission alone; annual campaigns outperform biennial strategies, preventing 40% more cases; and combined vaccination reduces the reproduction number below unity while preventing 65-80% of infections versus no vaccination. Critically, temporary disruptions in routine coverage significantly increase outbreak risk, and maintaining vaccine efficacy above 90% alongside hospitalization of at least 50% of infectious individuals is essential for containment. These results underscore that high-coverage routine vaccination must be integrated with periodic high-intensity campaigns and robust clinical care to close immunity gaps, mitigate waning protection, and accelerate measles elimination.

Ernst Mayr famously distinguished between proximate and ultimate causal explanations. His view was decisive in the fact that development was not included in the theory of evolution. Nevertheless, the explanatory role of developmental processes in evolution is a central theme of current theoretical biology, which has led to several revisions of Mayr’s distinction. One of the reasons for this is that the interactions between organisms and the environment influence evolution. This is a cornerstone of the Extended Evolutionary Synthesis. How are these reciprocal interactions integrated into evolutionary theory? I will argue that this question can sometimes be answered by adopting an interactivist view based on a reinterpretation of reciprocal causation: developmental processes (such as niche construction or developmental plasticity) interact reciprocally with natural selection to produce evolved traits. In the following, I will critically analyze Mayr’s critics to determine whether the integration of development and evolution based on the reinterpretation of reciprocal causation is an appropriate alternative. I will argue that this is not the case, since this interactivist framework raises several explanatory problems. Instead, I should reconsider Mayr’s distinction by adopting an alternative view of evolutionary causation, known as the statisticalist view of natural selection, which posits that the only level of causation is the individual level and that ultimate explanations are statistical in nature. I argue that this framework avoids the explanatory problems identified earlier. To explore the relationship between organisms–environment reciprocal causation and ultimate explanations, I introduce the concept of ‘statistical reciprocity’ to measure the statistical effects of reciprocal causation in population changes. I situate this proposal within a general framework I call ‘population ontogenetics’, which is seen as an attempt to unify development and evolution beyond interactivist positions.