Pub Date : 2025-11-17DOI: 10.1016/j.jtbi.2025.112307
Zita Borbála Fülöp, Raimondo Penta
Electroporation-based therapies such as electrochemotherapy (ECT) hold a great promise for improving cancer treatments. While highly effective for superficial tumours, its application for deep-seated malignancies is challenged by complex microstructural properties, and current models often lack a multiscale theoretical framework to capture those phenomena. Here we develop and solve a novel system of coupled partial differential equations of Darcy-Laplace type obtained by applying the asymptotic homogenisation technique. We study the tumour response stimulated by an electric field. We derive effective macroscale equations for the pressure, velocity, and electric potential, whilst incorporating both hydraulic and electric microscale tissue heterogeneities. Our coupled multiscale approach bridges the gap between the tumour microstructure and macroscale dynamics, offering a more comprehensive understanding of how tumour size, morphology, and hydraulic-electrical interactions influence interstitial flow. We present a parametric analysis of the hydraulic conductivity tensor and macroscale numerical simulation results for pressure and velocity fields, highlighting the role of the electric field in modulating fluid flow. Our findings provide meaningful insights towards advancing ECT protocols.
{"title":"Multiscale analysis of electrically stimulated vascularised tumours","authors":"Zita Borbála Fülöp, Raimondo Penta","doi":"10.1016/j.jtbi.2025.112307","DOIUrl":"10.1016/j.jtbi.2025.112307","url":null,"abstract":"<div><div>Electroporation-based therapies such as electrochemotherapy (ECT) hold a great promise for improving cancer treatments. While highly effective for superficial tumours, its application for deep-seated malignancies is challenged by complex microstructural properties, and current models often lack a multiscale theoretical framework to capture those phenomena. Here we develop and solve a novel system of coupled partial differential equations of Darcy-Laplace type obtained by applying the asymptotic homogenisation technique. We study the tumour response stimulated by an electric field. We derive effective macroscale equations for the pressure, velocity, and electric potential, whilst incorporating both hydraulic and electric microscale tissue heterogeneities. Our coupled multiscale approach bridges the gap between the tumour microstructure and macroscale dynamics, offering a more comprehensive understanding of how tumour size, morphology, and hydraulic-electrical interactions influence interstitial flow. We present a parametric analysis of the hydraulic conductivity tensor and macroscale numerical simulation results for pressure and velocity fields, highlighting the role of the electric field in modulating fluid flow. Our findings provide meaningful insights towards advancing ECT protocols.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"619 ","pages":"Article 112307"},"PeriodicalIF":2.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558390","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}
Pub Date : 2025-11-15DOI: 10.1016/j.jtbi.2025.112319
Arni S.R. Srinivasa Rao , Steven G. Krantz , John P. Barile
Despite the widespread use of deterministic models in understanding and controlling epidemics, they are often criticized for their inability to provide timely practical solutions during rapid spread. Similarly, conventional stochastic and statistical models also have limitations in providing time-sensitive solutions. These models are useful for implementing policy measures when there is enough time to make changes. In this article, we propose a novel approach to address these limitations by introducing a graphical network model with time-sensitive data blending to enhance deterministic epidemic models like the SIR model. This innovative approach could be valuable for rapidly spreading epidemics, providing timely model-based solutions to control their spread. For the first time, this article introduces higher-dimensional transmission rate functions in the literature and methods to obtain such functions.
{"title":"Integrating community level transmission geographical networks into a dynamical system for better epidemic control","authors":"Arni S.R. Srinivasa Rao , Steven G. Krantz , John P. Barile","doi":"10.1016/j.jtbi.2025.112319","DOIUrl":"10.1016/j.jtbi.2025.112319","url":null,"abstract":"<div><div>Despite the widespread use of deterministic models in understanding and controlling epidemics, they are often criticized for their inability to provide timely practical solutions during rapid spread. Similarly, conventional stochastic and statistical models also have limitations in providing time-sensitive solutions. These models are useful for implementing policy measures when there is enough time to make changes. In this article, we propose a novel approach to address these limitations by introducing a graphical network model with time-sensitive data blending to enhance deterministic epidemic models like the SIR model. This innovative approach could be valuable for rapidly spreading epidemics, providing timely model-based solutions to control their spread. For the first time, this article introduces higher-dimensional transmission rate functions in the literature and methods to obtain such functions.</div><div>AMS MSC 2020 classifications: 92D30; 62P10; 65T60.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112319"},"PeriodicalIF":2.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544131","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}
Pub Date : 2025-11-15DOI: 10.1016/j.jtbi.2025.112314
Neha Bansal, Katerina Kaouri, Thomas E. Woolley
Epidemiological models can inform policymaking on disease control strategies, and these models often rely on sampled contact networks. The Random Walk (RW) sampling algorithm, commonly used for network sampling, produces size-biased samples that over-represent highly connected individuals, leading to biased estimates of disease spread. The Metropolis-Hastings Random Walk (MHRW) addresses this by providing samples representative of the underlying network’s connectivity distribution. We compare MHRW and RW in reducing size bias across four network types: Erdös-Rényi (ER), Small-world (SW), Negative-binomial (NB), and Scale-free (SF). We simulate disease spread using a stochastic Susceptible-Infected-Recovered (SIR) framework. RW tends to overestimate infections (by 25 % in ER, SW, NB) and secondary infections (by 25 % in ER, SW and 80 % in NB), and underestimate time-to-infection in NB networks. MHRW reduces the size bias, except on SF networks, where both algorithms provide non-representative samples and highly variable estimates. We find that RW is appropriate for fast-spreading, high-mortality epidemics in homogeneous or moderately random networks (ER, SW). In contrast, MHRW is better suited for slower and low-severity epidemics and can be effective in both homogeneous and heterogeneous networks (ER, SW, NB). However, MHRW is computationally expensive and less accurate when duplicate nodes are removed. We also analyse real-world data from cattle movement and human contact networks; MHRW generates disease spread estimates closer to the underlying network than RW. Our findings guide the selection of sampling algorithms based on network structure and epidemic characteristics, enhancing the reliability of disease modelling for policymaking.
流行病学模型可以为疾病控制战略的决策提供信息,而这些模型往往依赖于抽样接触网络。随机漫步(RW)抽样算法通常用于网络抽样,它产生的样本有大小偏差,过度代表高度联系的个体,导致对疾病传播的估计有偏差。Metropolis-Hastings Random Walk (MHRW)通过提供代表底层网络连接分布的样本来解决这个问题。我们比较了MHRW和RW在四种网络类型(Erdös-Rényi (ER)、小世界(SW)、负二项(NB)和无标度(SF))中减少尺寸偏差的效果。我们使用随机易感-感染-恢复(SIR)框架模拟疾病传播。RW倾向于高估感染(在ER、SW、NB中为25%)和继发性感染(在ER、SW中为25%,在NB中为80%),并低估NB网络中的感染时间。除了SF网络,MHRW减少了大小偏差,其中两种算法都提供了非代表性样本和高度可变的估计。我们发现RW适用于同质或中等随机网络中快速传播、高死亡率的流行病(ER, SW)。相比之下,MHRW更适合于较慢和低严重程度的流行病,并且可以在同质和异质网络中有效(ER、SW、NB)。然而,MHRW的计算成本很高,并且在删除重复节点时准确性较低。我们还分析了来自牛的运动和人类接触网络的真实数据;MHRW产生的疾病传播估计值比RW更接近基础网络。我们的研究结果指导了基于网络结构和流行病特征的抽样算法的选择,提高了疾病建模为政策制定提供的可靠性。
{"title":"Reducing size bias in epidemic network modelling","authors":"Neha Bansal, Katerina Kaouri, Thomas E. Woolley","doi":"10.1016/j.jtbi.2025.112314","DOIUrl":"10.1016/j.jtbi.2025.112314","url":null,"abstract":"<div><div>Epidemiological models can inform policymaking on disease control strategies, and these models often rely on sampled contact networks. The Random Walk (RW) sampling algorithm, commonly used for network sampling, produces size-biased samples that over-represent highly connected individuals, leading to biased estimates of disease spread. The Metropolis-Hastings Random Walk (MHRW) addresses this by providing samples representative of the underlying network’s connectivity distribution. We compare MHRW and RW in reducing size bias across four network types: Erdös-Rényi (ER), Small-world (SW), Negative-binomial (NB), and Scale-free (SF). We simulate disease spread using a stochastic Susceptible-Infected-Recovered (SIR) framework. RW tends to overestimate infections (by 25 % in ER, SW, NB) and secondary infections (by 25 % in ER, SW and 80 % in NB), and underestimate time-to-infection in NB networks. MHRW reduces the size bias, except on SF networks, where both algorithms provide non-representative samples and highly variable estimates. We find that RW is appropriate for fast-spreading, high-mortality epidemics in homogeneous or moderately random networks (ER, SW). In contrast, MHRW is better suited for slower and low-severity epidemics and can be effective in both homogeneous and heterogeneous networks (ER, SW, NB). However, MHRW is computationally expensive and less accurate when duplicate nodes are removed. We also analyse real-world data from cattle movement and human contact networks; MHRW generates disease spread estimates closer to the underlying network than RW. Our findings guide the selection of sampling algorithms based on network structure and epidemic characteristics, enhancing the reliability of disease modelling for policymaking.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112314"},"PeriodicalIF":2.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544122","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}
Pub Date : 2025-11-14DOI: 10.1016/j.jtbi.2025.112316
Oscar Delaney, Christopher~R.~P. Brown, Andrew~D. Letten, Jan Engelstäder
An increasingly important goal in the design of antimicrobial treatment regimens is to minimise the probability of resistance evolving, without harming individual patients’ outcomes. A key characteristic to consider when choosing an antibiotic for treatment is its mode of action: bacteriostatic (growth-inhibiting) or bactericidal (mortality-inducing). We present a theoretical model comparing the efficacy of bacteriostatic, bactericidal, and intermediate drugs at preventing the evolutionary rescue of an initially susceptible bacterial population. We find that, all else equal, in resource-abundant environments, bacteriostatic drugs are best, as they constrain cell divisions and thus allow fewer resistance mutations to occur. This contrasts with the prevailing assumption that bactericidal drugs are best as they actively kill cells. When multiple drugs are employed, using one bacteriostatic and one bactericidal drug is usually optimal, because the cell division rate cannot fall below zero, so there are diminishing returns to bacteriostatic activity from two drugs. Severe resource constraints mean that growth rates are already low, and thus there is less benefit to bacteriostatic drugs further limiting growth, so bactericidal drugs are favoured. If these findings are empirically verified in the laboratory and in vivo, they could significantly guide clinical practice.
{"title":"Drug mode of action and resource constraints modulate antimicrobial resistance evolution","authors":"Oscar Delaney, Christopher~R.~P. Brown, Andrew~D. Letten, Jan Engelstäder","doi":"10.1016/j.jtbi.2025.112316","DOIUrl":"10.1016/j.jtbi.2025.112316","url":null,"abstract":"<div><div>An increasingly important goal in the design of antimicrobial treatment regimens is to minimise the probability of resistance evolving, without harming individual patients’ outcomes. A key characteristic to consider when choosing an antibiotic for treatment is its mode of action: bacteriostatic (growth-inhibiting) or bactericidal (mortality-inducing). We present a theoretical model comparing the efficacy of bacteriostatic, bactericidal, and intermediate drugs at preventing the evolutionary rescue of an initially susceptible bacterial population. We find that, all else equal, in resource-abundant environments, bacteriostatic drugs are best, as they constrain cell divisions and thus allow fewer resistance mutations to occur. This contrasts with the prevailing assumption that bactericidal drugs are best as they actively kill cells. When multiple drugs are employed, using one bacteriostatic and one bactericidal drug is usually optimal, because the cell division rate cannot fall below zero, so there are diminishing returns to bacteriostatic activity from two drugs. Severe resource constraints mean that growth rates are already low, and thus there is less benefit to bacteriostatic drugs further limiting growth, so bactericidal drugs are favoured. If these findings are empirically verified in the laboratory and in vivo, they could significantly guide clinical practice.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112316"},"PeriodicalIF":2.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535110","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}
Pub Date : 2025-11-13DOI: 10.1016/j.jtbi.2025.112284
Jean-Philippe Berteau , Abdennasser Chekroun , Laurent Pujo-Menjouet , Kevin Yueh-Hsun Yang
The goal of our study was to establish how a specific part of the bone Gene Regulatory Network (GRN) controls mineralization in response to stiffness. We hypothesized that a system of differential equations model stiffness-sensitive gene regulation in human mesenchymal stem cells through the epistatic genetic interactions between stiffness (e.g. WNT-β catenin pathway) and five of the main transcription factors and bone proteins (e.g. RUNX2, BSP, OSX, OC, and OPN). To test this hypothesis, we (i) performed in-vitro experiments culturing bone cells on different stiffness, (i) adapted our previously published model from being continuously time-dependent to continuously stiffness-sensitive, and (iii) simulated protein production in function of stiffness and other protein production from the best estimate of parameters coming from the experimental work. Our experimental findings reveal a non-parametric relationship between stiffness and RUNX2 production, with no discernible linear trends for other proteins. Modeling results demonstrate that continuous variations in stiffness enable simulation of bone GRN gene expression, fitting our novel experimental dataset. Specifically, our computational results indicate that OPN production peaks at low stiffness (8 kPa), while RUNX2, OSX, and OC achieve maximum production at higher stiffness levels (64 kPa). This alignment underscores the model’s capacity to replicate experimental data accurately. Additionally, our approach predicts that WNT-β-catenin activation serves as an enhancer for OPN and BSP production. The model also highlights a negative feedback-like interaction between OC and BSP production. Stiffness variations were shown to have a significant impact on OC and BSP production and a moderate effect on OPN production. By employing a stiffness-sensitive gene regulation model, we provide insights into one of the mineralization patterns through the prediction of bone protein expression dynamics.
{"title":"Stiffness-sensitive gene regulation in human mesenchymal stem cells: Modelling mechanotransduction to predict mineralization and bone protein expression","authors":"Jean-Philippe Berteau , Abdennasser Chekroun , Laurent Pujo-Menjouet , Kevin Yueh-Hsun Yang","doi":"10.1016/j.jtbi.2025.112284","DOIUrl":"10.1016/j.jtbi.2025.112284","url":null,"abstract":"<div><div>The goal of our study was to establish how a specific part of the bone Gene Regulatory Network (GRN) controls mineralization in response to stiffness. We hypothesized that a system of differential equations model stiffness-sensitive gene regulation in human mesenchymal stem cells through the epistatic genetic interactions between stiffness (<em>e.g.</em> WNT-<em>β</em> catenin pathway) and five of the main transcription factors and bone proteins (<em>e.g.</em> RUNX2, BSP, OSX, OC, and OPN). To test this hypothesis, we (i) performed <em>in-vitro</em> experiments culturing bone cells on different stiffness, (i) adapted our previously published model from being continuously time-dependent to continuously stiffness-sensitive, and (iii) simulated protein production in function of stiffness and other protein production from the best estimate of parameters coming from the experimental work. Our experimental findings reveal a non-parametric relationship between stiffness and RUNX2 production, with no discernible linear trends for other proteins. Modeling results demonstrate that continuous variations in stiffness enable simulation of bone GRN gene expression, fitting our novel experimental dataset. Specifically, our computational results indicate that OPN production peaks at low stiffness (8 kPa), while RUNX2, OSX, and OC achieve maximum production at higher stiffness levels (64 kPa). This alignment underscores the model’s capacity to replicate experimental data accurately. Additionally, our approach predicts that WNT-<em>β</em>-catenin activation serves as an enhancer for OPN and BSP production. The model also highlights a negative feedback-like interaction between OC and BSP production. Stiffness variations were shown to have a significant impact on OC and BSP production and a moderate effect on OPN production. By employing a stiffness-sensitive gene regulation model, we provide insights into one of the mineralization patterns through the prediction of bone protein expression dynamics.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112284"},"PeriodicalIF":2.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530970","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}
Pub Date : 2025-11-13DOI: 10.1016/j.jtbi.2025.112313
Yang Deng , Yi Zhao
Tuberculosis (TB) remains a significant global health threat, particularly in the regions with diverse age-specific transmission patterns and increasing drug resistance. To address these challenges, this study establishes a dual-strain model that incorporates both drug-resistant and drug-sensitive strains to investigate how these strains contribute to the dynamics of TB transmission. By integrating age heterogeneity, social interactions, and seasonal variations, the model offers a detailed depiction of TB transmission process, highlighting its inherent complexity across various population groups. We derive the basic reproduction number of the model as the maximum of the two reproduction numbers: one for the drug-resistant strain () and one for the drug-sensitive strain (). It is found that the disease-free periodic equilibrium of the system is globally asymptotically stable when , in the absence of reinfection. We further explore the competitive dynamics of drug-resistant and drug-sensitive strains under and . Using a Markov Chain Monte Carlo (MCMC) algorithm, the model is calibrated with monthly TB infection data from mainland China, enabling the reconstruction of TB transmission dynamics across eight age-specific groups. The study reveals that drug-sensitive tuberculosis strains exhibit more prominent transmission characteristics compared to drug-resistant strains. Moreover, increased vaccination coverage significantly reduces TB prevalence, particularly in younger populations, while reducing contact intensity effectively suppresses TB across all age groups. These findings highlight the role of combining age-structured modeling, strain dynamics, and behavioral interventions, offering implications for the targeted TB control strategies.
{"title":"Mathematical modeling of tuberculosis with two strains, seasonality, and age heterogeneity","authors":"Yang Deng , Yi Zhao","doi":"10.1016/j.jtbi.2025.112313","DOIUrl":"10.1016/j.jtbi.2025.112313","url":null,"abstract":"<div><div>Tuberculosis (TB) remains a significant global health threat, particularly in the regions with diverse age-specific transmission patterns and increasing drug resistance. To address these challenges, this study establishes a dual-strain model that incorporates both drug-resistant and drug-sensitive strains to investigate how these strains contribute to the dynamics of TB transmission. By integrating age heterogeneity, social interactions, and seasonal variations, the model offers a detailed depiction of TB transmission process, highlighting its inherent complexity across various population groups. We derive the basic reproduction number of the model as the maximum of the two reproduction numbers: one for the drug-resistant strain (<span><math><msubsup><mi>R</mi><mn>0</mn><mi>r</mi></msubsup></math></span>) and one for the drug-sensitive strain (<span><math><msubsup><mi>R</mi><mn>0</mn><mi>s</mi></msubsup></math></span>). It is found that the disease-free periodic equilibrium of the system is globally asymptotically stable when <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo>=</mo><mi>max</mi><mrow><mo>(</mo><msubsup><mi>R</mi><mrow><mn>0</mn></mrow><mi>r</mi></msubsup><mo>,</mo><msubsup><mi>R</mi><mrow><mn>0</mn></mrow><mi>s</mi></msubsup><mo>)</mo></mrow><mo><</mo><mn>1</mn></mrow></math></span>, in the absence of reinfection. We further explore the competitive dynamics of drug-resistant and drug-sensitive strains under <span><math><mrow><msubsup><mi>R</mi><mn>0</mn><mi>r</mi></msubsup><mo>></mo><mn>1</mn><mo>></mo><msubsup><mi>R</mi><mn>0</mn><mi>s</mi></msubsup></mrow></math></span> and <span><math><mrow><msubsup><mi>R</mi><mn>0</mn><mi>s</mi></msubsup><mo>></mo><mn>1</mn><mo>></mo><msubsup><mi>R</mi><mn>0</mn><mi>r</mi></msubsup></mrow></math></span>. Using a Markov Chain Monte Carlo (MCMC) algorithm, the model is calibrated with monthly TB infection data from mainland China, enabling the reconstruction of TB transmission dynamics across eight age-specific groups. The study reveals that drug-sensitive tuberculosis strains exhibit more prominent transmission characteristics compared to drug-resistant strains. Moreover, increased vaccination coverage significantly reduces TB prevalence, particularly in younger populations, while reducing contact intensity effectively suppresses TB across all age groups. These findings highlight the role of combining age-structured modeling, strain dynamics, and behavioral interventions, offering implications for the targeted TB control strategies.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"619 ","pages":"Article 112313"},"PeriodicalIF":2.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531003","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}
Pub Date : 2025-11-12DOI: 10.1016/j.jtbi.2025.112318
Decheng Kong, Kai Xue, Ping Wang, Zeyu Xu, Zhiqin Huang
Coordinated longitudinal queue behavior in biological groups, such as migratory bird flocks, remains underexplored in classical collective motion models that focus on metric-based interactions and synchronous dynamics. This study utilizes a modified self-propelled particle model incorporating topological interactions, gliding asynchrony, and limited view angle to investigate the mechanisms driving longitudinal queue formation. Simulations reveal that interacting with only two topological neighbors is critical for stable queue emergence, with an optimal view angle range of [200°, 270°] balancing frontward tracking and lateral collision avoidance. Gliding asynchrony enhances queue formation efficiency by reducing neighbor interaction frequency, leading to higher success rates and lower interaction complexity compared to synchronous or random update mechanisms. Topological interaction networks exhibit high connectivity and stability, fundamentally supporting queue maintenance, while metric-based or Voronoi interactions fail to produce linear order. The study highlights the interplay of limited sensory perception, low neighbor connectivity, and asynchronous dynamics in self-organized migration queues, providing a theoretical guidance for understanding animal collective behavior and guiding robotic swarm design.
{"title":"Emergence of longitudinal queue behavior based on topological interaction and asynchronous dynamics","authors":"Decheng Kong, Kai Xue, Ping Wang, Zeyu Xu, Zhiqin Huang","doi":"10.1016/j.jtbi.2025.112318","DOIUrl":"10.1016/j.jtbi.2025.112318","url":null,"abstract":"<div><div>Coordinated longitudinal queue behavior in biological groups, such as migratory bird flocks, remains underexplored in classical collective motion models that focus on metric-based interactions and synchronous dynamics. This study utilizes a modified self-propelled particle model incorporating topological interactions, gliding asynchrony, and limited view angle to investigate the mechanisms driving longitudinal queue formation. Simulations reveal that interacting with only two topological neighbors is critical for stable queue emergence, with an optimal view angle range of [200°, 270°] balancing frontward tracking and lateral collision avoidance. Gliding asynchrony enhances queue formation efficiency by reducing neighbor interaction frequency, leading to higher success rates and lower interaction complexity compared to synchronous or random update mechanisms. Topological interaction networks exhibit high connectivity and stability, fundamentally supporting queue maintenance, while metric-based or Voronoi interactions fail to produce linear order. The study highlights the interplay of limited sensory perception, low neighbor connectivity, and asynchronous dynamics in self-organized migration queues, providing a theoretical guidance for understanding animal collective behavior and guiding robotic swarm design.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112318"},"PeriodicalIF":2.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524880","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}
Pub Date : 2025-11-11DOI: 10.1016/j.jtbi.2025.112290
Adam Howes , Alex Stringer , Seth R. Flaxman , Jeffrey W. Imai–Eaton
Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment coverage are jointly modelled using both household survey data and routinely reported health system data. The model is provided as a tool for countries to input their data to and generate estimates with during a yearly process supported by UNAIDS. Previously, inference has been conducted using empirical Bayes and a Gaussian approximation, implemented via the TMBR package. We propose a new inference method based on an extension of adaptive Gauss-Hermite quadrature to deal with more than 20 hyperparameters. Using data from Malawi, our method improves the accuracy of inferences for model parameters, while being substantially faster to run than Hamiltonian Monte Carlo with the No-U-Turn sampler. Our implementation leverages the existing TMBC++ template for the model’s log-posterior, and is compatible with any model with such a template.
{"title":"Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature","authors":"Adam Howes , Alex Stringer , Seth R. Flaxman , Jeffrey W. Imai–Eaton","doi":"10.1016/j.jtbi.2025.112290","DOIUrl":"10.1016/j.jtbi.2025.112290","url":null,"abstract":"<div><div>Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment coverage are jointly modelled using both household survey data and routinely reported health system data. The model is provided as a tool for countries to input their data to and generate estimates with during a yearly process supported by UNAIDS. Previously, inference has been conducted using empirical Bayes and a Gaussian approximation, implemented via the <span>TMB</span> <span>R</span> package. We propose a new inference method based on an extension of adaptive Gauss-Hermite quadrature to deal with more than 20 hyperparameters. Using data from Malawi, our method improves the accuracy of inferences for model parameters, while being substantially faster to run than Hamiltonian Monte Carlo with the No-U-Turn sampler. Our implementation leverages the existing <span>TMB</span> <span>C++</span> template for the model’s log-posterior, and is compatible with any model with such a template.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112290"},"PeriodicalIF":2.0,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145514510","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}
Pub Date : 2025-11-10DOI: 10.1016/j.jtbi.2025.112304
Sanju Sardar , Priti Kumar Roy , Sk Mosaraf Ahammed , Tushar Ghosh , David Greenhalgh
Methanol poisoning is an infrequent but immensely dangerous intoxication, causing severe metabolic disturbances, neurological dysfunction, and even death, if not treated timely and properly. In this article, we formulate a mathematical model based on the chemical kinetics reaction, to analyse the effect of co-administration of the antidote ethanol and folinic acid for the treatment of methanol toxicity. The maximum concentration level of formic acid has been identified, and through a one-dimensional impulsive system, we determined the maximum dosing interval of folinic acid. Under appropriate assumptions we have demonstrated the existence and stability of the equilibrium-like periodic orbit of our system with impulsive administration of folinic acid and ethanol. The dynamical changes of toxic metabolites are illustrated numerically for different doses and dosing intervals. We performed a sensitivity analysis to identify the key parameters affecting formic acid concentration during treatment. Model results were validated by comparing them with clinical and experimental data on methanol half-life during ethanol therapy and formic acid clearance under folinic acid treatment. Based on our detailed analytical and numerical analysis, we recommend an effective dosing regimen of folinic acid and ethanol to detoxify the human body and clearly identify the conditions beyond which hemodialysis becomes essential. We verified all of our analytical outcomes through numerical simulation.
{"title":"Treatment of methanol toxicity through ethanol and folinic acid: A mathematical study","authors":"Sanju Sardar , Priti Kumar Roy , Sk Mosaraf Ahammed , Tushar Ghosh , David Greenhalgh","doi":"10.1016/j.jtbi.2025.112304","DOIUrl":"10.1016/j.jtbi.2025.112304","url":null,"abstract":"<div><div>Methanol poisoning is an infrequent but immensely dangerous intoxication, causing severe metabolic disturbances, neurological dysfunction, and even death, if not treated timely and properly. In this article, we formulate a mathematical model based on the chemical kinetics reaction, to analyse the effect of co-administration of the antidote ethanol and folinic acid for the treatment of methanol toxicity. The maximum concentration level of formic acid has been identified, and through a one-dimensional impulsive system, we determined the maximum dosing interval of folinic acid. Under appropriate assumptions we have demonstrated the existence and stability of the equilibrium-like periodic orbit of our system with impulsive administration of folinic acid and ethanol. The dynamical changes of toxic metabolites are illustrated numerically for different doses and dosing intervals. We performed a sensitivity analysis to identify the key parameters affecting formic acid concentration during treatment. Model results were validated by comparing them with clinical and experimental data on methanol half-life during ethanol therapy and formic acid clearance under folinic acid treatment. Based on our detailed analytical and numerical analysis, we recommend an effective dosing regimen of folinic acid and ethanol to detoxify the human body and clearly identify the conditions beyond which hemodialysis becomes essential. We verified all of our analytical outcomes through numerical simulation.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112304"},"PeriodicalIF":2.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145508116","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}
White adipose tissue, composed of adipocyte cells, primarily stores energy as lipid droplets. The size of adipocytes varies significantly within the tissue according to the amount of stored lipids. A striking observation is that the adipocyte size distribution is bimodal, and thus, this tissue is lacking a characteristic size.
We propose a novel dynamical model, based on a partial differential equation, to represent the adipocyte size distribution. The model assumes continuous adipocyte growth, with a velocity dependent on cell radius and extracellular lipid availability, together with constant rates of cell recruitment and death.
We prove the existence and local stability of a unique stationary solution for a broad range of growth velocity functions. Choosing a parcimonious formulation, we show that only three parameters are enough to describe adipocyte size distributions measurements in rats. These parameters are robustly estimated through approximate Bayesian computation, and the model demonstrates excellent agreement with experimental data. This mechanistic, three-parameter framework offers a new and interpretable approach to characterizing adipocyte size distributions.
{"title":"Rapid cell turnover to model adipocyte size distribution","authors":"Louis Fostier , Aloïs Dauger , Romain Yvinec , Magali Ribot , Chloe Audebert , Hedi Soula","doi":"10.1016/j.jtbi.2025.112311","DOIUrl":"10.1016/j.jtbi.2025.112311","url":null,"abstract":"<div><div>White adipose tissue, composed of adipocyte cells, primarily stores energy as lipid droplets. The size of adipocytes varies significantly within the tissue according to the amount of stored lipids. A striking observation is that the adipocyte size distribution is bimodal, and thus, this tissue is lacking a characteristic size.</div><div>We propose a novel dynamical model, based on a partial differential equation, to represent the adipocyte size distribution. The model assumes continuous adipocyte growth, with a velocity dependent on cell radius and extracellular lipid availability, together with constant rates of cell recruitment and death.</div><div>We prove the existence and local stability of a unique stationary solution for a broad range of growth velocity functions. Choosing a parcimonious formulation, we show that only three parameters are enough to describe adipocyte size distributions measurements in rats. These parameters are robustly estimated through approximate Bayesian computation, and the model demonstrates excellent agreement with experimental data. This mechanistic, three-parameter framework offers a new and interpretable approach to characterizing adipocyte size distributions.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"618 ","pages":"Article 112311"},"PeriodicalIF":2.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145508140","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}