While projects in philosophy of science often focus on similar sorts of topics, there are often massive differences in how philosophers approach them. One approach that has enjoyed significant success—and that is modeled in the work of Wimsatt and others—involves taking a conceptual engineering perspective in analyzing these topics. In this paper, we outline main features of this conceptual engineering approach and use it to advance three emerging claims regarding causal explanation in biology and neuroscience. We suggest that causal explanation in these domains is non-reductive in contrast to always requiring (or improving with) lower-level details, that it is guided by precise explanatory targets in contrast to complete or multidimensional explanatory targets, and that it is pluralist in involving importantly distinct types of causal systems.
This article examines neoteny and regressive evolution in two emblematic amphibian species: Ambystoma mexicanum (axolotl) and Proteus anguinus (olm). Rather than framing larval persistence and trait loss as evolutionary shortcomings, the paper proposes an alternative interpretation in which developmental arrest functions as a viable population-level outcome of selection under specific ecological constraints, rather than as a developmental shortcoming. Drawing on evo–devo literature, endocrine regulation of metamorphosis, and comparative life-history analysis, the study argues that both species exemplify non-teleological evolutionary pathways. By foregrounding strategy over deficiency, the article contributes to broader debates on normativity, progress, and directionality in evolutionary biology.
The metaphor of “scaffolding” originates from developmental psychology, and has become a central theoretical concept that plays explanatory roles in psychology, developmental biology, and evolutionary biology. In developmental psychology, the scaffolding process is typically characterized as a child’s temporary reliance on external structure in training activities, which leads to long-term transformation of the child’s capacities. In recent attempts to clarify this concept for wider usage (e.g., Caporael, Griesemer, and Wimsatt 2014a, b; Neto et al. 2023), the temporariness of dependence on scaffolds is still seen as a typical, if not defining, feature of scaffolding explanations. In this paper, we follow Bill Wimsatt’s footsteps (Wimsatt 2014a, 2019; Wimsatt and Griesemer 2007) in examining the development of skills that require complex developmental trajectories. Our cases are jazz improvisation and scientific expertise, both of which are distinctively social and improvisational. We argue that the social scaffolds required to reach the maturity of such expertise are continually required thereafter for maintenance and further development. This is because improvisational training is somewhat indistinguishable from improvisational performance, and performance in social expertise aims at interactions, communications, and rapport between interlocutors.
Science is the best way to produce facts about reality. The best, at least, that limited human beings have devised so far. Yet, not even scientists quite seem to understand how scientific knowledge is generated. This is not only a philosophical but also a practical problem, as our misunderstandings affect the quality of our research and limit the directions it can take. In light of this, it may be good if we reflected a bit more on how we do science — to become better researchers through philosophy. Here, I provide an accessible introduction to a philosophical approach that achieves precisely this: William Wimsatt’s multi-perspectival realism. It disabuses us of widespread but misleading myths and idealizations about science, such as the idea that everything in the world can be reduced to a fundamental level, or that we can approach a “view from nowhere” — complete and objectively detached knowledge of the world. Wismatt proposes an alternative view based on his thorough studies of actual research practice. It cuts deeply into the layered yet messy structure of reality, and the improvised but potent tools we have available, as limited and evolved beings, to explore it. Wimsatt reframes science as an irregular yet adaptive process rather than a cumulative repository of unalterable facts. His philosophy provides a workable and grounded middle way between radical skepticism and naïve belief in the objective truth of science. It explains how knowledge is conceptually constructed by humans, but still connects us to reality in a trustworthy way. We need such a new view of science, not only to improve our research practices and outcomes but, more generally, to gain a more realistic understanding of ourselves, the world, and our place and role within it.
Epidemiological surveillance systems often provide data on specific characteristics of an infected population. For instance, sex, geographical location, socioeconomic level and age of the registered individuals. This allows us to study the population divided into groups. However, information on the dynamics of infected people classified by group is not usually exploited when analyzing the evolution of an epidemic. In this work, we propose a tool to analyze how the spread of an epidemic is heterogeneous among different population groups. Based on records of infected individuals we identify synchronicity and causality interactions among population groups. Describing this dynamics and which population groups are the first focus of infection is essential for decision makers. We represent time series by population group and their degree of similarity using a weighted graph, and we apply a community detection algorithm to partition this graph. Each community is composed of synchronized age groups. The direction of interaction among different communities is identified using sample cross-correlation in a domain that can indicate causality. This is illustrated by considering age groups and using datasets of COVID-19 in Jalisco, Mexico and influenza A(H1N1) in the USA. In both cases, the proposed methodology detected which age groups show synchronized behavior across time, and which age groups influence the subsequent appearance of epidemic outbreaks in other groups.
This paper considers the behavior of Wolbachia infection in a dioecious population as a discrete dynamical system. A recurrence relation is obtained as a function of the initial infected male/female frequencies and the cytoplasmic incompatibility of the population. Experimental data from Wolbachia-infected terrestrial isopod populations and a model proposed in Wolbachia-infected mosquitoes from the literature are compared with the proposed system.
In this paper we study genetic variation at a highly polymorphic locus of a monoecious or dioecious population whose census and effective sizes differ and possibly vary independently over time. More specifically, we develop a general framework for the allele frequency spectrum (AFS) at this locus, and functions of the AFS. Examples of such functions are number of alleles, number of common and rare alleles, allelic diversity, gene diversity, higher order gene diversity and Hill numbers. We develop exact recursions for the expected AFS, and its functionals, with particular interest in populations that experience a rapid (a few generations) bottleneck followed by approaching a new equilibrium between mutation and drift. A grid-based numerical algorithm is developed, which is exact for small populations and approximate for large populations. This algorithm is exemplified with exact calculations for small populations that undergo a bottleneck, and approximate calculations for a moderately large population that rapidly decreases in size.
The discipline of immunology has historically been foundationally framed by metaphors of war, portraying the body as a sovereign state defending its territory against foreign invaders. This paradigm, however, is not strictly a biological necessity but also a historical artifact of colonial logic that arguably limits our understanding of symbiosis, tolerance, and the nature of relational pathologies. This paper argues for a decolonial paradigm shift, proposing a comprehensive reframing of the immune system not as a military force but as a sophisticated system of communication, governance, and diplomacy within a multi-species community, the holobiont. We trace the colonial genealogy of war metaphors and expose its conceptual inadequacies in the face of modern biology, distinguishing between historical rhetorical resonances and causal scientific developments. Drawing inspiration from relational and ecological philosophies, we then propose a new conceptual lexicon, using the metaphor of the body as a quilombo, a diverse and resilient community. By separating canonical biological mechanisms from metaphorical interpretation, this framework reframes core immunological processes: inflammation becomes an urgent community assembly, the adaptive response a journey of information, and pathologies like autoimmunity, cancer, and immunodeficiency become crises of communication and social cohesion. Offered as a conceptual heuristic rather than a wholesale structural equivalent, this relational approach offers not only new avenues for research and therapy but also serves as a powerful pedagogical tool to foster a more holistic, integrated, and ecologically conscious view of life itself.
The COVID-19 pandemic has presented an unprecedented global challenge, significantly impacting public health, economies, and daily life worldwide. In response to this crisis, scientists and researchers around the world have worked tirelessly to understand the virus, its transmission dynamics, and most importantly, to develop effective vaccines. However, questions remain about the comparative effectiveness of these vaccines, particularly in real-world scenarios. This paper aimed to address this critical issue by employing advanced statistical techniques, including time-varying copulas. In this study, we employed four types of copula, namely Gaussian, Student-t, Clayton, and Gumbel, to model the temporal association between vaccination and the emergence of COVID-19 cases. The results of this study suggested that the number of subsequent vaccinations, especially the first to fourth vaccines, has a significant impact on the occurrence and spread of COVID-19 under particular conditions. Furthermore, we proposed several modeling scenarios for COVID-19 cases based on their temporal interactions with the number of subsequent vaccinations. The findings of this research provided a comprehensive understanding of the temporal relationship between vaccination and its impact on reducing COVID-19 cases.
This study presents a computational model that integrates bone remodeling dynamics with damage accumulation, focusing on both physiological and pathological conditions. Building upon Komarova’s classical model of osteoclast and osteoblast interactions, this work introduces fatigue-induced damage using a stress-life (S-N) approach. By simulating bone responses under sinusoidal and random mechanical loads, the model captures the cyclical nature of bone turnover. The results show that under normal physiological conditions, bone is able to repair microdamage and maintain structural integrity. However, in pathological scenarios such as osteoporosis and tumors, the remodeling cycle is disrupted, leading to an increase in damage accumulation and eventual structural failure. Through numerical simulations, the study also demonstrates the significant impact of fatigue on bone health, showing that repetitive mechanical loads, even below critical stress levels, can result in bone degradation over time. By capturing the accumulation of microdamage and its repair, the model offers potential applications in personalized medicine to assess fracture risks in varying stress and health scenarios. This approach provides a framework for understanding how different stress patterns contribute to bone damage and offers insights into the progression of bone diseases. The model could be extended using metabolic and age-related characteristics and serves as a potential tool for personalized medicine, helping to predict bone failure risks in individuals when they are submitted to repetitive mechanical loads.
The compartmental epidemiological model is commonly used to study dengue dynamics; some of these models precisely consider the mosquito population, and others indirectly capture its role in disease transmission term. In this article, we have performed a comparative analysis between a simple SIR model and a vector-host interaction model (VH model) by fitting the dengue fever data of the ongoing outbreak in America. Parameter estimations for both models have been performed using the Nelder-Mead simplex algorithm, considering the normalized root mean square error (NRMSE) as an optimization function. The significance and reliability of the estimated parameters towards the models’ predictions have been analysed through uncertainty and sensitivity analyses. Uncertainty analysis utilizing the Latin hypercube sampling (LHS) method has been performed to evaluate how various parameters within the models influence the basic reproduction number ( ℛ_0 ). Sensitivity analysis for the basic reproduction number ( ℛ_0 ), has been carried out by the partial rank correlation coefficients (PRCC) method. Additionally, we have computed the parameter regions ensuring the persistence of equilibrium points of both models. This study offers profound insights into model selection, parameter estimation, and forecasting future data trends for the ongoing dengue outbreak in America. However, this article focuses on exploring two key scientific questions: (1) Which type of compartmental model (SIR or VH) is more suitable to capture the trend of data on dengue fever for the ongoing outbreak in America? (2) Is America likely to face a prolonged dengue outbreak in the near future?
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.
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.
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.
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.
We propose a discrete model to determine the metabolic scaling exponent based on Fibonacci growth patterns and discrete biological development phases. In contrast to continuous fractal models such as the West-Brown-Enquist (WBE) theory, the present approach describes metabolic scaling as the cumulative result of successive discrete stages, each incrementally contributing to metabolic activity. The scaling exponent b(n) emerges naturally from the logarithmic relationship between consecutive Fibonacci numbers, varying systematically with the organism's developmental stage. A refined logarithmic formulation significantly enhances quantitative agreement with empirical metabolic data across various mammalian species. This discrete framework effectively captures deviations from classical scaling laws, directly connecting recursive hierarchical structures with metabolic processes. Our model provides an alternative to traditional fractal transport approaches and can be naturally extended to hierarchical physical systems, opening new avenues to explore stage-dependent scaling phenomena in complex adaptive systems.
