Bulletin of Mathematical Biology最新文献

{"title":"Publisher Correction: Investigation of a Two-Patch Within-Host Model of Hepatitis B Viral Infection.","authors":"Keoni Castellano, Omar Saucedo, Stanca M Ciupe","doi":"10.1007/s11538-026-01780-7","DOIUrl":"https://doi.org/10.1007/s11538-026-01780-7","url":null,"abstract":"","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149214985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Transcritical Bifurcation at Infinity Governs Plaque Stability in a Lipid-Structured Macrophage Model of Atherosclerosis.","authors":"Emine Atıcı Endes, Neslihan Nesliye Pelen","doi":"10.1007/s11538-026-01751-y","DOIUrl":"https://doi.org/10.1007/s11538-026-01751-y","url":null,"abstract":"<p><p>Atherosclerotic plaques are fatty deposits in arterial walls and a major cause of heart attacks and strokes. Macrophage proliferation triggers plaque growth and instability, but the specific conditions that convert stable plaques into unstable ones remain unclear. To provide insight into the conditions for this transition, we apply bifurcation analysis to the lipid-structured atherosclerosis model proposed by Chambers et al. (Bull Math Biol 86(8):104, 2024). We demonstrate that, in the asymptotic regime where macrophage levels become large ( <math><mrow><mi>M</mi> <mo>→</mo> <mi>∞</mi></mrow> </math> ), the model exhibits an asymptotic fast-slow structure that does not hold outside this limit. Within this asymptotic regime, we reduce the full system onto a slow invariant manifold, providing a simplified yet accurate description of the dynamics near the critical proliferation-emigration threshold. We prove, via centre manifold theory, that the positive steady state loses stability through a transcritical bifurcation at infinity at the critical proliferation-emigration threshold <math> <mrow><msub><mi>ρ</mi> <mi>c</mi></msub> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mi>γ</mi></mrow> </math> . The analysis reveals that the positive equilibrium branch approaches and exchanges stability with a boundary equilibrium at infinity, providing a rigorous dynamical explanation for the transition that was identified but left unexplored in the original study. Complementing this analytical contribution, we conduct a global sensitivity analysis using Partial Rank Correlation Coefficients (PRCC), identifying macrophage proliferation, emigration, and efferocytosis as the dominant regulators of plaque dynamics. Targeted parameter investigations near <math><msub><mi>ρ</mi> <mi>c</mi></msub> </math> reveal a regulatory decoupling between macrophage accumulation and necrotic core growth, providing new biological insight into the behaviour of the model near the critical proliferation-emigration threshold.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Modeling and Control of Brucellosis in a Metapopulation Network Considering Animal Transportation: A Case Study of Xinjiang, China.","authors":"Xin Pei, Erjia Cui, Mingtao Li, Chaojian Shen, Yurong Wei, Jie Wei, Xiaoyun Mi, Wenjun Jing, Juan Zhang, Zhen Jin","doi":"10.1007/s11538-026-01776-3","DOIUrl":"https://doi.org/10.1007/s11538-026-01776-3","url":null,"abstract":"<p><p>Cross-regional animal transportation is a crucial factor in the spatial transmission of brucellosis, and infections occurring during transportation cannot be overlooked. Clarifying the spatiotemporal transmission patterns of brucellosis under animal transportation mechanisms is of great significance for enabling targeted prevention and control of the disease. Based on data from the Xinjiang Animal Husbandry and Veterinary Big Data Platform, we constructed a directed and weighted sheep transportation network. We established a coupled human-sheep metapopulation model for brucellosis that incorporates infection during animal transportation, thereby providing a more accurate description of brucellosis spread across the metapopulation network. The epidemic threshold was derived theoretically. For the application, a brucellosis transmission model was simulated based on the constructed sheep transportation network in Xinjiang. It was observed that with an increase in transportation rate, epidemic threshold first decreases then increases, indicating that excessively high or low transportation rates should be avoided. Notably, epidemic threshold also increases as the infection rate during transportation rises, which exacerbates brucellosis epidemics in practice, so its potential risks cannot be ignored. For brucellosis control, we first identified key regions and key transportation routes using network centrality, then simulated the control effect with practical control measures. The results demonstrated that strengthening quarantine at checkpoints and reducing sheep transportation between key areas and other regions are more effective than implementing the same measures on critical transportation routes. Specifically, node degree centrality can effectively identify key regions. In early control stage, prioritize reducing internal infection rates through regular quarantine, purification and standardized breeding management; in the middle and late stages, the most effective control is increase culling rate. Our study clarifies the impact of animal transportation and infections occurring during transportation, and analyzes the effectiveness of different control measures from network structure perspective, providing a theoretical basis for brucellosis control in Xinjiang and other regions.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics and Numerical Analysis of a Multi-Patch Influenza Transmission Model with Population Migration and Daily Commuting.","authors":"Lusha Shi, Shuai Shen, Lili Liu, Xiaohong Zhang, Zhen Jin","doi":"10.1007/s11538-026-01689-1","DOIUrl":"https://doi.org/10.1007/s11538-026-01689-1","url":null,"abstract":"<p><p>Human mobility plays an important role in the disease spatial transmission. In this paper, a multi-patch SEIR Influenza model incorporating long-term population migration and daily commuting was formulated to describe Influenza interregional spreading. Mathematically, the explicit expression for basic reproduction number was derived by the next-generation matrix method, and the globally asymptotically stability of disease free equilibrium and the unique positive equilibrium were rigorously proved. Numerically, taking the end of 2025 influenza epidemic in Shanxi province as an example, the model was fitted by using influenza surveillance data, census data, and Baidu migration data from the 11 cities. A Logistic decay form of transmission rate is introduced to characterize the effects of intervention time and intervention strength. The numerical results showed a good fitting and indicate that the provincial basic reproduction number is 2.5877, while the city-level values range from 1.13-2.85. Sensitivity analysis further showed that the initial transmission intensity, infectious period, and intervention time are key factors affecting Influenza spreading, and local transmission contributes more to epidemic spread than inter-city mobility, which implies that reducing local transmission is more effective when controlling disease spreading. Moreover, under some certain conditions, early targeted control in hotspot cities can achieve effects comparable to or even better than province-wide uniform control. These findings provide theoretical support and quantitative evidence for understanding interregional influenza transmission and designing region-specific prevention and control strategies.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Likelihood-Based Framework for Simultaneously Learning Both Noise and Growth Dynamics Using Biologically-Informed Neural Networks.","authors":"Rebecca M Crossley, Ruth E Baker","doi":"10.1007/s11538-026-01755-8","DOIUrl":"10.1007/s11538-026-01755-8","url":null,"abstract":"<p><p>In recent years, neural ordinary differential equation frameworks such as Biologically-Informed Neural Networks (BINNs) have shown promise for learning mechanistic laws from sparse data. However, most existing approaches implicitly assume homoscedastic Gaussian noise, and therefore do not account for potentially meaningful structure in biological variability. Here, we present an extension to the existing BINNs framework that includes a learnable noise model, allowing discovery of the noise model directly from data. Using population growth as an example, we demonstrate that the framework accurately recovers the underlying noise structure and improves predictions of the underlying growth laws compared to existing approaches. As such, this work establishes a general likelihood-based framework for jointly learning dynamics and heteroscedastic noise within mechanistic neural network approaches.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149165135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Curse of Black Sigatoka: A Backward Bifurcation Perspective.","authors":"Bernard Asamoah Afful, Luis F Gordillo","doi":"10.1007/s11538-026-01769-2","DOIUrl":"10.1007/s11538-026-01769-2","url":null,"abstract":"<p><p>Black Sigatoka disease (BSD), also known as black leaf streak disease, is an airborne fungal infection caused by Pseudocercospora fijiensis that severely impacts global banana and plantain production. Its persistence and resistance to eradication make it one of the most challenging plant diseases to manage. In this paper, we propose a deterministic pathogen-host model to describe BSD dynamics. Due to dual transmission pathways (ascospores and conidia) and mate limitation in sexual reproduction, the model exhibits a backward bifurcation: a stable endemic equilibrium coexists with the disease-free equilibrium for certain parameter values in which the basic reproduction number, <math><msub><mi>R</mi> <mn>0</mn></msub> </math> , is less than 1. This phenomenon explains why control strategies that solely reduce <math><msub><mi>R</mi> <mn>0</mn></msub> </math> below one may fail. For the backward bifurcation regime, we perform sensitivity analysis of the endemic equilibrium using normalized forward sensitivity indices, Latin Hypercube Sampling, and Partial Rank Correlation Coefficients. Results indicate that effective control must extend beyond <math><msub><mi>R</mi> <mn>0</mn></msub> </math> reduction and prioritize (1) limiting production of new susceptible leaves during high-risk periods and (2) developing and deploying disease-resistant plant varieties. To incorporate transmission variability, we also formulate a stochastic version of the model using the Stochastic Simulation Algorithm (SSA). Extensive numerical simulations compare stochastic realizations with deterministic predictions and quantify variability in disease dynamics. To identify the principal drivers of persistence and variability, we analyze the endemic equilibrium using Sobol's variance-based sensitivity method, which highlights the role of nonlinear parameter interactions in shaping variability.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tree Rearrangement Graphs Admit Paths of Decreasing Robinson-Foulds Distance.","authors":"Lena Collienne, Frederick A Matsen Iv","doi":"10.1007/s11538-026-01748-7","DOIUrl":"10.1007/s11538-026-01748-7","url":null,"abstract":"<p><p>Tree rearrangements such as Nearest Neighbor Interchange (NNI) and Subtree Prune and Regraft (SPR) are commonly used to explore phylogenetic treespace. Computing distances based on them, however, is often intractable, so the efficiently computable Robinson-Foulds (RF) distance is used in practice. We investigate how the RF distance behaves along paths in the NNI and SPR graphs, where trees are nodes and edges represent single rearrangements. We show that any two trees are connected by a path along which the RF distance to the target decreases monotonically in the NNI graph and strictly in the SPR graph; we also show that a strictly decreasing NNI path does not always exist.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity Analysis on a Computational Model of Olfaction Using Gaussian Process, Generalized Polynomial Chaos and Neural Networks.","authors":"Vidya Chandrasekaran, Yanyan He, Dominick Ohara, Lindsay Waldrop","doi":"10.1007/s11538-026-01768-3","DOIUrl":"10.1007/s11538-026-01768-3","url":null,"abstract":"<p><p>The process of odor capture by arrays of chemosensory hairs is a complex phenomenon influenced by multiple factors, such as hair morphology and surrounding fluid characteristics. Understanding how these parameters affect the fluid flow and odor transport is critical for advancing biological understanding and informing the potential design of bio-inspired sensing systems. However, performing comprehensive analyses with high-fidelity computational fluid dynamics models can be computationally expensive. To address this challenge, we investigate three surrogate modeling approaches - Gaussian process regression, generalized polynomial chaos and feedforward neural networks - as efficient numerical approximations to full simulations. These surrogates are trained on a limited number of simulations and then used to predict system responses efficiently. Their predictive accuracy is compared using synthetic examples across a range of scenarios. For the biological application considered here, cross-validation is employed to identify the surrogate model with the best performance. Using these surrogate models, we implement a variance-based global sensitivity analysis method to analyze the impact of uncertainty of inputs (such as morphological variables) on flow characteristics.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149134726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A High Throughput and User-Friendly Computational Tool for Quantifying and Visualizing Symmetry Variation in Image Datasets.","authors":"Liam D O'Brien, Punit Gandhi, Maria-Veronica Ciocanel, Adriana T Dawes","doi":"10.1007/s11538-026-01754-9","DOIUrl":"10.1007/s11538-026-01754-9","url":null,"abstract":"<p><p>Symmetry is a fundamental property of biological systems, yet it is commonly reduced to discrete, subjective classifications that fail to capture the continuous and graded nature of symmetry in real organisms. We present a high-throughput, user-friendly computational framework for quantifying and visualizing biological symmetry as a continuous variable using transformation information ( <math><mrow><mi>T</mi> <mi>I</mi></mrow> </math> ), an information theoretic measure that quantifies the divergence between an object and its transformed self. We establish key mathematical properties of <math><mrow><mi>T</mi> <mi>I</mi></mrow> </math> , including invariance, equivariance, and Lipschitz continuity, that guarantee robustness to transformations such as translation, rotation, reflection, and rescaling, enabling reliable symmetry quantification across heterogeneous image datasets without manual landmarking or image alignment. Building on this theoretical foundation, we introduce a computational pipeline that automates symmetry center detection, <math><mrow><mi>T</mi> <mi>I</mi></mrow> </math> curve computation, and visualization of the symmetry data using functional principal component analysis (fPCA). We demonstrate the utility of this framework on two biological datasets: a large collection of angiosperm flower images, where we investigate rotational and reflectional symmetries, and a dataset of simulated fern images, where we investigate fractal self-similarity under scaling and translation. Our analysis of the floral dataset provides novel evidence of convergent evolution. In both datasets, fPCA of the resulting <math><mrow><mi>T</mi> <mi>I</mi></mrow> </math> profiles reveals that symmetry is best characterized as a continuum rather than a set of discrete classes, and there is subtle morphological variation that is not captured by traditional categorical approaches. Together, these results suggest that <math><mrow><mi>T</mi> <mi>I</mi></mrow> </math> provides a mathematically principled and biologically informative foundation for integrating symmetry into quantitative evolutionary, ecological, and developmental models.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Mechanisms Driving Lesion Outcomes During Lung Tumor and Tuberculosis Granuloma Formation.","authors":"Christian T Michael, Maral Budak, Denise Kirschner","doi":"10.1007/s11538-026-01763-8","DOIUrl":"10.1007/s11538-026-01763-8","url":null,"abstract":"<p><p>Small cell lung cancer (SCLC) and tuberculosis (TB) are both deadly diseases that present with spatially complex lung lesions. These lesions share similarities, including spatial interactions between T cells and macrophages. Both SCLC and TB exhibit heterogeneous disease progression and responses to treatment; current experimental methods have few tools to investigate the spatiotemporal evolution of these lesions within human lungs. We have applied our computational agent-based model (ABM), GranSim, to extensively study heterogeneity of TB granuloma formation and treatment efficacy. We introduce TumorSim, an analogous ABM designed to understand heterogeneity of SCLC lung tumors. TumorSim mechanistically captures immune-tumor interactions, many of which are well-studied in isolation, including cytokine-based recruitment of immune cells and PD-1/PD-L1-based inhibition of cytotoxic T cells. Drawing from lung immunology literature, we define and explore wide parameter ranges to characterize TumorSim behavior using global sensitivity analysis. We compare factors that drive both tumor and granuloma outcomes. As model validation, sensitivity analysis captures several well-known correlates of improved SCLC outcomes including macrophage-mediated cytotoxic T-cell recruitment. Surprisingly, both models predict a two-phase formation process occurring with an abrupt change in tumor/granuloma dynamics upon arrival of adaptive immune cells into the lung. Simulations suggest that while C-C motif chemokine ligand 5 (CCL5) is associated with improved tumor control later during tumor growth, CCL5 plays a pro-tumor role early during tumor growth by recruiting regulatory T cells. We find that, like virtual granulomas, TumorSim tumors become enlarged when immunosuppressive mechanisms outweigh pro-inflammatory responses. This novel tumor model enables future studies of both immunotherapeutics and anti-cancer drugs.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}