Acta Biotheoretica最新文献

Plasticity of living systems has long attracted life scientists in different fields, but a detailed philosophical analysis of the very concept has yet to be undertaken. Antonine Nicoglou’s Plasticity in the Life Sciences addresses this problem. By combining a historical examination of the concept of plasticity from Aristotle to contemporary biology and philosophical analysis of its status and roles in biological research, the book provides a rich picture of plasticity as a “boundary concept.” It is also a great example of a highly integrated historical and philosophical inquiry into a scientific concept.

Bill Wimsatt and Mark Wilson are each the author of a body of work whose fruitfulness is rivaled only by its forbiddingness. Despite deep sympathies between their approaches and conclusions, their work has not yet been read together. This paper makes the case for doing so. We identify a shared question at the heart of their work: how is it that limited beings such as ourselves come to possess genuine knowledge of a complex world? We then show that Wimsatt and Wilson arrive at similar answers to this question. Over a range of topics (investigative strategies, the uses of models, and theoretical and conceptual structure), both scholars emphasize the functional messiness of science. This is complemented by a pragmatist-leaning philosophical methodology that recognizes that one of the core uses of knowledge is to scaffold the acquisition of more knowledge. The core of the paper traces the mutually supportive interplay between their philosophical doctrines and methods. We end with two brief discussions: one a defense of their winding, playful writing styles, the other a brief consideration of the relationship between their work and Arthur Fine’s natural ontological attitude.

Instabilities and Turing patterns in stochastic spatiotemporal systems in which a fraction of an evolving population, after undergoing a series of dynamic transitions, returns to its original state, remain largely unexplored. Adopting an epidemic model incorporating reinfections as an exemplar of such a system, we present stability and pattern-formation analyses of the stochastic reaction-diffusion equations that represent the model. Saturation effects in epidemic spread lead to nonlinear considerations, while random environmental effects motivate a stochastic term. Turing bifurcation and the emergence of equilibrium patterns are analysed with respect to three fundamental parameters - reinfection, saturation, and noise intensity. Using higher-order stability analysis and stochastic averaging, we find the Turing instability and also uncover self–organized, distinct equilibrium patterns of infection spread. Additionally, results elucidating the effects of stochastic excitation and its intensity, as well as the competing influence of saturation and reinfection on stability and pattern formation, are presented. The results are also expected to be broadly significant beyond epidemic modelling, for studies of noise-induced instabilities and morphogenesis in spatiotemporal nonlinear dynamical systems.

We present a neutral delay differential equation (NDDE) modification of the FitzHugh–Nagumo (FHN) model. One linear and three nonlinear variants are proposed to capture the interaction between delayed effects in the membrane potential and the stimulating current, including a delayed rate (neutral) term. We derive fixed points, compute eigenvalues, and obtain Hopf conditions to map regions of stability, instability, and oscillation. Parameter studies show how the neutral delay reorganizes dynamics and modulates the robustness of nerve firing. Numerical simulations quantify frequency, ISI, amplitude, and spike counts and reproduce canonical phenotypes: regular spiking adapts to large stimulus delays, intrinsic bursting yields rapid spikes, chattering shows high-frequency bursts with moderate delays, fast-spiking exhibits high rates, and low-threshold neurons adapt to high frequencies with small delays. Overall, the NDDE representation provides a compact, physically motivated framework in which time delay governs stability and synchronization, offering insight for diagnostic modeling and potential therapies for disorders with irregular firing.

In an in vivo situation, the tissue near a blood vessel is rich in oxygen supply compared to the one far from a blood vessel. In this article, our objective is to explore the effect of non-uniform oxygen supply on the development of the necrotic core of a tumor. We adopt a multiphase continuum-based approach to model the growth of a tumor. To simulate the model, a finite-difference-based numerical approach in line with the “Semi-Implicit Method for Pressure-Linked Equations" (SIMPLE) algorithm is adopted. Investigations reveal that the necrotic core develops near the boundary with lower oxygen concentration. The position of the necrotic core strongly depends on the oxygen supply through the tumor boundary. The results predict asymmetrical tumor growth under unequal oxygen supply at tumor boundaries. Also, it is hinted that a tumor with a larger necrotic core grows more slowly than a tumor containing a smaller necrotic core. The present model has the potential to anticipate in vivo and in vitro situations. The findings will be beneficial for clinicians and medical practitioners in predicting the stage of a tumor.

Population genetics relies heavily on homozygosity statistics. Over the past two decades, however, as the number and applications of homozygosity-based statistics has increased, there has been a crisis of confidence. Homozygosity statistics are mathematically constrained by other population genetics statistics, such as maximum allele frequency, creating problems for interpretation and portability. Mathematical investigations into these constraints have been criticized for being irrelevant to biology. Noah Rosenberg’s new book Mathematical Properties of Population-Genetic Statistics is his response to these concerns. While accepting many of the criticisms, Rosenberg shows that careful attention to purely mathematical constraints on homozygosity can be leveraged to design new statistics to achieve new goals.

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.