The link between the virus and antibody dynamics of an infected host to the transmission of the virus to a susceptible population remains a central problem in science as it involves several complex and dynamic processes at different scales. In this study, we integrate deterministic and stochastic within-host models to explore multiscale transmission dynamics. Our methodology accounts for encounter frequency, within-host variability, and reinfection dynamics to assess their impact on epidemic progression. Our results show that within-host stochasticity disrupts synchronized viral peaks, leading to a more uniform transmission pattern and reducing the effectiveness of interventions targeting peak viral load. Considering the half-life of antibodies is 25 days, cycles of reinfections cannot be maintained in small populations, but reinfections become self-sustaining when a circular network exceeds 21 nodes, allowing indefinite circulation. These findings emphasize the need for integrating within-host dynamics in epidemic research.
{"title":"Linking within-host immune dynamics to between-host transmission and reinfection risk.","authors":"Rodolfo Blanco-Rodriguez, Alejandro Anderson, Esteban Hernandez-Vargas","doi":"10.1016/j.jtbi.2025.112210","DOIUrl":"10.1016/j.jtbi.2025.112210","url":null,"abstract":"<p><p>The link between the virus and antibody dynamics of an infected host to the transmission of the virus to a susceptible population remains a central problem in science as it involves several complex and dynamic processes at different scales. In this study, we integrate deterministic and stochastic within-host models to explore multiscale transmission dynamics. Our methodology accounts for encounter frequency, within-host variability, and reinfection dynamics to assess their impact on epidemic progression. Our results show that within-host stochasticity disrupts synchronized viral peaks, leading to a more uniform transmission pattern and reducing the effectiveness of interventions targeting peak viral load. Considering the half-life of antibodies is 25 days, cycles of reinfections cannot be maintained in small populations, but reinfections become self-sustaining when a circular network exceeds 21 nodes, allowing indefinite circulation. These findings emphasize the need for integrating within-host dynamics in epidemic research.</p>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":" ","pages":"112210"},"PeriodicalIF":2.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762278","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}
Pub Date : 2025-10-07Epub Date: 2025-07-16DOI: 10.1016/j.jtbi.2025.112218
Francesco Di Lauro, William J M Probert, Michael Pickles, Anne Cori, Robert Hinch, Luca Ferretti, Jasmina Panovska-Griffiths, Lucie Abeler-Dörner, Rory Dunbar, Peter Bock, Deborah J Donnell, Helen Ayles, Sarah Fidler, Richard Hayes, Christophe Fraser
The HIV epidemic in sub-Saharan Africa is historically characterised by high levels of prevalence and incidence. With the global effort to reach UNAIDS 95-95-95 targets, the scaling-up of HIV treatment, and focused preventive interventions, incidence has been declining over the past decade, albeit non-consistently across different sex and age groups. Two questions remain to be addressed to help tailor setting-specific interventions and allocate resources optimally. Firstly, are there unidentified demographic groups that are sources of transmission? Secondly, what are the patterns of decline in incidence across different groups? Model-based assessment is a valuable tool for the design of focused interventions and to answer these questions. PopART-IBM, an individual-based model calibrated to (anonymised) age-and-sex stratified data, was developed in the context of the HPTN-071 (PopART) trial, and it offers a unique opportunity to explore such questions in the context of high-burden HIV communities in Zambia and South Africa. The outputs of the model include the full HIV transmission and partnership networks. In this work, we explore these and show that the sexual partnership network exhibits a large connected component, usually comprising over 40 % of the population, in each of the studied communities. An analysis of the large connected component reveals that it is formed by young people (20-40 years old) and is centered around the most sexually active individuals of the community. At the same time, many individuals in the large connected component only have one partner, highlighting the complex dynamics of risk correlations in a population. Inspecting the transmission network reveals that, on average, more than 80% of transmissions occur among individuals belonging to the large connected component. These findings indicate that populations consisting of young and highly sexually active individuals should be given high priority when designing or deploying interventions.
{"title":"Large connected components in sexual networks and their role in HIV transmission in Sub-Saharan Africa: A model-based analysis of HPTN 071(PopART) data.","authors":"Francesco Di Lauro, William J M Probert, Michael Pickles, Anne Cori, Robert Hinch, Luca Ferretti, Jasmina Panovska-Griffiths, Lucie Abeler-Dörner, Rory Dunbar, Peter Bock, Deborah J Donnell, Helen Ayles, Sarah Fidler, Richard Hayes, Christophe Fraser","doi":"10.1016/j.jtbi.2025.112218","DOIUrl":"10.1016/j.jtbi.2025.112218","url":null,"abstract":"<p><p>The HIV epidemic in sub-Saharan Africa is historically characterised by high levels of prevalence and incidence. With the global effort to reach UNAIDS 95-95-95 targets, the scaling-up of HIV treatment, and focused preventive interventions, incidence has been declining over the past decade, albeit non-consistently across different sex and age groups. Two questions remain to be addressed to help tailor setting-specific interventions and allocate resources optimally. Firstly, are there unidentified demographic groups that are sources of transmission? Secondly, what are the patterns of decline in incidence across different groups? Model-based assessment is a valuable tool for the design of focused interventions and to answer these questions. PopART-IBM, an individual-based model calibrated to (anonymised) age-and-sex stratified data, was developed in the context of the HPTN-071 (PopART) trial, and it offers a unique opportunity to explore such questions in the context of high-burden HIV communities in Zambia and South Africa. The outputs of the model include the full HIV transmission and partnership networks. In this work, we explore these and show that the sexual partnership network exhibits a large connected component, usually comprising over 40 % of the population, in each of the studied communities. An analysis of the large connected component reveals that it is formed by young people (20-40 years old) and is centered around the most sexually active individuals of the community. At the same time, many individuals in the large connected component only have one partner, highlighting the complex dynamics of risk correlations in a population. Inspecting the transmission network reveals that, on average, more than 80% of transmissions occur among individuals belonging to the large connected component. These findings indicate that populations consisting of young and highly sexually active individuals should be given high priority when designing or deploying interventions.</p>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":" ","pages":"112218"},"PeriodicalIF":2.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669015","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 : 2025-10-06DOI: 10.1016/j.jtbi.2025.112280
Yafei Zhao , Sabrina Averga , Bruno Buonomo , Jie Lou
This study investigates the dynamics of co-infections during an epidemic, particularly in the absence of official data on co-infected individuals. The research has two primary objectives: first, to assess the robustness of the two-pathogen co-infection model proposed by Fahlena et al. (Chaos Sol. Fract., 2022) in terms of structural and practical identifiability; and second, to evaluate the time variation of co-infection percentages in Italy during the winter of 2023–2024. The identifiability analysis is based on official data regarding influenza and SARS-CoV-2 cases, complemented by estimated co-infection data under two scenarios (high and low levels of co-infection). The study finds that when both weekly infection and co-infection data are available, the model’s parameters are structurally identifiable. However, if only incidence data for each virus are available, five parameters must be fixed to achieve both structural and practical identifiability, with the remaining parameters being identifiable. Additionally, the model suggests that a unimodal time profile of co-infection percentages could have occurred in Italy during the study period. These results emphasize the importance of comprehensive data for model identification and co-infection estimation during epidemics.
{"title":"Assessing respiratory virus co-infections using an identifiable model: the case of influenza and SARS-CoV-2 in Italy","authors":"Yafei Zhao , Sabrina Averga , Bruno Buonomo , Jie Lou","doi":"10.1016/j.jtbi.2025.112280","DOIUrl":"10.1016/j.jtbi.2025.112280","url":null,"abstract":"<div><div>This study investigates the dynamics of co-infections during an epidemic, particularly in the absence of official data on co-infected individuals. The research has two primary objectives: first, to assess the robustness of the two-pathogen co-infection model proposed by Fahlena et al. (Chaos Sol. Fract., 2022) in terms of structural and practical identifiability; and second, to evaluate the time variation of co-infection percentages in Italy during the winter of 2023–2024. The identifiability analysis is based on official data regarding influenza and SARS-CoV-2 cases, complemented by estimated co-infection data under two scenarios (high and low levels of co-infection). The study finds that when both weekly infection and co-infection data are available, the model’s parameters are structurally identifiable. However, if only incidence data for each virus are available, five parameters must be fixed to achieve both structural and practical identifiability, with the remaining parameters being identifiable. Additionally, the model suggests that a unimodal time profile of co-infection percentages could have occurred in Italy during the study period. These results emphasize the importance of comprehensive data for model identification and co-infection estimation during epidemics.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112280"},"PeriodicalIF":2.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253746","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 : 2025-10-06DOI: 10.1016/j.jtbi.2025.112289
Rena Hayashi , Akane Hara , Yoh Iwasa
Human papillomavirus (HPV), a DNA virus, causes cervical cancer, which is the most common cancer among Japanese women in their forties. Upon infection, HPV temporarily proliferates but is usually eliminated by the immune system. However, if the virus enters the nuclei of epithelial cells, it can evade immune detection and establish a persistent infection. In this state, HPV inhibits apoptosis and allows genomic mutations to accumulate. Over many years, this can lead to dysplasia, genetic abnormalities, and eventually, invasive cancer with metastasis. While many individuals with persistent HPV infections experience spontaneous remission, a small proportion develop cervical cancer. In this study, we aim to understand the sharp contrast between cervical cancer and other solid tumors (cancers of epithelial tissues). We analyze a mathematical model for stochastic transitions between infection states, where the likelihood of persistent infection is proportional to the cumulative viral load, influenced by viral dynamics, immune effectors, and immune memory. We derive formulas for total cancer incidence, mean age at diagnosis, and age variance. Key parameters were estimated from data using the MCMC method. We conclude that major characteristics of cervical cancer arise from the strong age-dependence of viral genome incorporated into the epithelial tissue — shaped by the human sexual behavior — and from the very high rate of spontaneous remission.
{"title":"Human papillomavirus driving cervical cancer: A mathematical model with persistent infection, cancer progression, and spontaneous remission","authors":"Rena Hayashi , Akane Hara , Yoh Iwasa","doi":"10.1016/j.jtbi.2025.112289","DOIUrl":"10.1016/j.jtbi.2025.112289","url":null,"abstract":"<div><div>Human papillomavirus (HPV), a DNA virus, causes cervical cancer, which is the most common cancer among Japanese women in their forties. Upon infection, HPV temporarily proliferates but is usually eliminated by the immune system. However, if the virus enters the nuclei of epithelial cells, it can evade immune detection and establish a persistent infection. In this state, HPV inhibits apoptosis and allows genomic mutations to accumulate. Over many years, this can lead to dysplasia, genetic abnormalities, and eventually, invasive cancer with metastasis. While many individuals with persistent HPV infections experience spontaneous remission, a small proportion develop cervical cancer. In this study, we aim to understand the sharp contrast between cervical cancer and other solid tumors (cancers of epithelial tissues). We analyze a mathematical model for stochastic transitions between infection states, where the likelihood of persistent infection is proportional to the cumulative viral load, influenced by viral dynamics, immune effectors, and immune memory. We derive formulas for total cancer incidence, mean age at diagnosis, and age variance. Key parameters were estimated from data using the MCMC method. We conclude that major characteristics of cervical cancer arise from the strong age-dependence of viral genome incorporated into the epithelial tissue — shaped by the human sexual behavior — and from the very high rate of spontaneous remission.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"617 ","pages":"Article 112289"},"PeriodicalIF":2.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253781","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 : 2025-09-29DOI: 10.1016/j.jtbi.2025.112283
Irina Kareva , Georgy Karev
While many mechanisms have been proposed to drive Alzheimer’s disease, particularly the accumulation of amyloid plaques and hyperphosphorylation of tau proteins, emerging evidence suggests that they may be the byproducts of earlier damage rather than initiating events. Instead, metabolic dysfunction and the inability of neural cells to support their energetic demands may be a more plausible trigger for subsequent pathological cascade (the neuron energy crisis hypothesis). Here we highlight how type 2 diabetes (T2D) can contribute to neurodegeneration by impairing brain energy metabolism. We present a game-theoretic framework, where neurons face trade-offs between energy efficiency and information fidelity. We show that under metabolic stress, neural networks can evolve toward smaller group sizes that prioritize energy efficiency over information quality, which may underlie the observed collapse of cognitive capacity during neurodegeneration. We conclude with a discussion of interventions, ranging from antidiabetic drugs to cognitive engagement and sensory stimulation, aimed at reducing metabolic stress and preserving cognitive function.
{"title":"Energy constraints and neural strategy transitions in Alzheimer’s: A game-theoretic model","authors":"Irina Kareva , Georgy Karev","doi":"10.1016/j.jtbi.2025.112283","DOIUrl":"10.1016/j.jtbi.2025.112283","url":null,"abstract":"<div><div>While many mechanisms have been proposed to drive Alzheimer’s disease, particularly the accumulation of amyloid plaques and hyperphosphorylation of tau proteins, emerging evidence suggests that they may be the byproducts of earlier damage rather than initiating events. Instead, metabolic dysfunction and the inability of neural cells to support their energetic demands may be a more plausible trigger for subsequent pathological cascade (the neuron energy crisis hypothesis). Here we highlight how type 2 diabetes (T2D) can contribute to neurodegeneration by impairing brain energy metabolism. We present a game-theoretic framework, where neurons face trade-offs between energy efficiency and information fidelity. We show that under metabolic stress, neural networks can evolve toward smaller group sizes that prioritize energy efficiency over information quality, which may underlie the observed collapse of cognitive capacity during neurodegeneration. We conclude with a discussion of interventions, ranging from antidiabetic drugs to cognitive engagement and sensory stimulation, aimed at reducing metabolic stress and preserving cognitive function.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112283"},"PeriodicalIF":2.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208560","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 : 2025-09-19DOI: 10.1016/j.jtbi.2025.112279
Bo-Moon Kim, Atsushi Yamauchi
Plants exhibit plastic responses to the absence or presence of competitors. When competing for soil nutrients, plants often show root overproliferation compared to when they grow without competitors. This excessive investment in roots to acquire more nutrients can reduce reproductive yield (e.g., seed mass), a phenomenon known as the tragedy of the commons (TOC). The mechanisms of this phenomenon have been investigated theoretically, focusing on resource allocation strategies between the aboveground (shoot) and the belowground (roots) parts. The previous studies have primarily considered these strategies in terms of sizes of those parts or static allocation rates to those over the season, overlooking dynamic change of allocation within the season. In this study, we introduced a concept of dynamic resource allocation into the plant competition game and investigate the optimal resource allocation strategy using Pontryagin’s maximum principle. Based on the solutions of schedules, we explored the mechanism causing TOC in nutrient competition. Our findings reveal that plants adopt the singular control (i.e., simultaneous allocation to shoot and root), where the control trajectory is identical regardless of the presence or absence of competitors, although the period of simultaneous allocation become longer in the presence of competitors. This trend associates with increasing the root size and decreasing the shoot size at the end of season in the competitive case. Our analysis demonstrates that TOC in plant nutrient competition arises from differences in the allocation period to roots in the competitive scenario.
{"title":"Adaptive dynamic resource allocation can cause tragedy of the commons in plants with nutrient competition","authors":"Bo-Moon Kim, Atsushi Yamauchi","doi":"10.1016/j.jtbi.2025.112279","DOIUrl":"10.1016/j.jtbi.2025.112279","url":null,"abstract":"<div><div>Plants exhibit plastic responses to the absence or presence of competitors. When competing for soil nutrients, plants often show root overproliferation compared to when they grow without competitors. This excessive investment in roots to acquire more nutrients can reduce reproductive yield (e.g., seed mass), a phenomenon known as the tragedy of the commons (TOC). The mechanisms of this phenomenon have been investigated theoretically, focusing on resource allocation strategies between the aboveground (shoot) and the belowground (roots) parts. The previous studies have primarily considered these strategies in terms of sizes of those parts or static allocation rates to those over the season, overlooking dynamic change of allocation within the season. In this study, we introduced a concept of dynamic resource allocation into the plant competition game and investigate the optimal resource allocation strategy using Pontryagin’s maximum principle. Based on the solutions of schedules, we explored the mechanism causing TOC in nutrient competition. Our findings reveal that plants adopt the singular control (i.e., simultaneous allocation to shoot and root), where the control trajectory is identical regardless of the presence or absence of competitors, although the period of simultaneous allocation become longer in the presence of competitors. This trend associates with increasing the root size and decreasing the shoot size at the end of season in the competitive case. Our analysis demonstrates that TOC in plant nutrient competition arises from differences in the allocation period to roots in the competitive scenario.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112279"},"PeriodicalIF":2.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114963","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 : 2025-09-19DOI: 10.1016/j.jtbi.2025.112277
Ruixi Huang , David Waxman
Many biological populations exhibit diversity in their strategy for survival and reproduction in a given environment, and microbes are an example. We explore the fate of different strategies under sustained environmental change by considering a mathematical model for a large population of asexual organisms. Fitness is a bimodal function of a quantitative trait, with two local optima, separated by a local minimum, i.e., a mixture of stabilising and disruptive selection. The optima represent two locally ‘best’ trait values. We consider regimes where, when the environment is unchanging, the equilibrium distribution of the trait is bimodal. A bimodal trait distribution generally requires, for its existence, mutational coupling between the two peaks, and it indicates two coexisting clones with distinct survival and reproduction strategies. When subject to persistent environmental change, the population adapts by utilising mutations that allow it to track the changing environment. The faster the rate of change of the environment, the larger the effect of the mutations that are utilised. Under persistent environmental change, the distribution of trait values takes two different forms. At low rates of change, the distribution remains bimodal. At higher rates, the distribution becomes unimodal. This loss of a clone/biodiversity is driven by a novel mechanism where environmental change decouples a class of mutations.
{"title":"Effective decoupling of mutations and the resulting loss of biodiversity caused by environmental change","authors":"Ruixi Huang , David Waxman","doi":"10.1016/j.jtbi.2025.112277","DOIUrl":"10.1016/j.jtbi.2025.112277","url":null,"abstract":"<div><div>Many biological populations exhibit diversity in their strategy for survival and reproduction in a given environment, and microbes are an example. We explore the fate of different strategies under sustained environmental change by considering a mathematical model for a large population of asexual organisms. Fitness is a bimodal function of a quantitative trait, with two local optima, separated by a local minimum, i.e., a mixture of stabilising and disruptive selection. The optima represent two locally ‘best’ trait values. We consider regimes where, when the environment is unchanging, the equilibrium distribution of the trait is bimodal. A bimodal trait distribution generally requires, for its existence, mutational coupling between the two peaks, and it indicates two coexisting clones with distinct survival and reproduction strategies. When subject to persistent environmental change, the population adapts by utilising mutations that allow it to track the changing environment. The faster the rate of change of the environment, the larger the effect of the mutations that are utilised. Under persistent environmental change, the distribution of trait values takes two different forms. At low rates of change, the distribution remains bimodal. At higher rates, the distribution becomes unimodal. This loss of a clone/biodiversity is driven by a novel mechanism where environmental change decouples a class of mutations.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112277"},"PeriodicalIF":2.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115023","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 : 2025-09-18DOI: 10.1016/j.jtbi.2025.112278
Eva Gunn , Nikhil Sengupta , Ben Swallow
Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta software for Bayesian inference to apply Gaussian process regression to spatio-temporal data of infectious disease outbreaks and predict future outbreaks. Greta builds on Tensorflow, making it comparatively easy to take advantage of the significant gain in speed offered by GPUs. In these complex spatio-temporal models, we show a reduction of up to 70% in computational time relative to fitting the same models on CPUs. We show how the choice of covariance kernel impacts the ability to infer spread and extrapolate to unobserved spatial and temporal units. The inference pipeline is applied to weekly incidence data on tuberculosis in the East and West Midlands regions of England over a period of two years.
{"title":"Gaussian process modelling of infectious diseases using the Greta software package and GPUs","authors":"Eva Gunn , Nikhil Sengupta , Ben Swallow","doi":"10.1016/j.jtbi.2025.112278","DOIUrl":"10.1016/j.jtbi.2025.112278","url":null,"abstract":"<div><div>Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta software for Bayesian inference to apply Gaussian process regression to spatio-temporal data of infectious disease outbreaks and predict future outbreaks. Greta builds on Tensorflow, making it comparatively easy to take advantage of the significant gain in speed offered by GPUs. In these complex spatio-temporal models, we show a reduction of up to 70% in computational time relative to fitting the same models on CPUs. We show how the choice of covariance kernel impacts the ability to infer spread and extrapolate to unobserved spatial and temporal units. The inference pipeline is applied to weekly incidence data on tuberculosis in the East and West Midlands regions of England over a period of two years.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112278"},"PeriodicalIF":2.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103242","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 : 2025-09-18DOI: 10.1016/j.jtbi.2025.112266
Yafei Wang , John Metzcar , Elmar Bucher , Heber L. Rocha , Vikram Jadhao , Randy Heiland , Hermann B. Frieboes , Paul Macklin
Interactions between biological systems and engineered nanomaterials have become an important area of study due to their application in medicine. In particular, the opportunity to apply nanomaterials for cancer diagnosis and treatment presents a challenge due to the complex biology of this disease, which spans multiple time and spatial scales. A systems-level analysis from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated system parameters and patient response would be of benefit. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, inheritance of NPs by daughter cells at cell division, cell pharmacodynamic response to intracellular drug levels, and overall drug effect on tumor growth. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on tumor growth. In particular, through the exploration of NP inheritance at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dose of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by heritable NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.
{"title":"Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study","authors":"Yafei Wang , John Metzcar , Elmar Bucher , Heber L. Rocha , Vikram Jadhao , Randy Heiland , Hermann B. Frieboes , Paul Macklin","doi":"10.1016/j.jtbi.2025.112266","DOIUrl":"10.1016/j.jtbi.2025.112266","url":null,"abstract":"<div><div>Interactions between biological systems and engineered nanomaterials have become an important area of study due to their application in medicine. In particular, the opportunity to apply nanomaterials for cancer diagnosis and treatment presents a challenge due to the complex biology of this disease, which spans multiple time and spatial scales. A systems-level analysis from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated system parameters and patient response would be of benefit. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, inheritance of NPs by daughter cells at cell division, cell pharmacodynamic response to intracellular drug levels, and overall drug effect on tumor growth. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on tumor growth. In particular, through the exploration of NP inheritance at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for <em>cytotoxic</em> chemotherapy. Moreover, smaller dose of <em>cytostatic</em> chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by heritable NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112266"},"PeriodicalIF":2.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103220","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 : 2025-09-16DOI: 10.1016/j.jtbi.2025.112265
Mark M. Tanaka , Ruiting Lan , Andrew R. Francis
As genome sequencing data continue to expand, a persistent research challenge is to accommodate the growth of a phylogeny. This situation arises in molecular epidemiology, for example, where new taxonomic groups can appear in real time as pathogen isolates are sequenced. Efficient computational methods have been developed to place new leaves in existing trees, which removes the need to reconstruct trees from scratch. But for these tree extensions to be fully integrated with classification schemes requires a stable encoding of trees that keeps existing tree structures intact as new branches appear. Here, we propose a tree encoding, which we call a folio, that records the path from a reference vertex to each leaf, giving each leaf an address. We present a simple set of rules to assign new addresses to added leaves. The encoding is stable in the sense that it does not change as further leaf addresses are added to the folio. The tree can be uniquely recovered from a folio of addresses. We illustrate the methods using Salmonella genome data. Due to the properties of our encoding framework, we anticipate that it can be used for a range of different phylogenetic analyses.
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