Pub Date : 2026-01-07Epub 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":"2026-01-07","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 : 2026-01-07Epub Date: 2025-09-12DOI: 10.1016/j.jtbi.2025.112251
Anass Bouchnita , Vitaly Volpert
In innate immune response, type I interferons (IFNs) activate interferon-stimulated genes (ISGs), which suppress viral replication and secretion at the intracellular level. Yet, how these ISG-virus interactions shape infection progression and severity remains poorly understood. Here, we introduce a new viral infection model that explicitly incorporates intracellular ISG-virus dynamics. It structures, for the first time, infected cells based on viral load and ISG expression which offers a computationally efficient and adaptable approach to integrating ISG-virus intracellular dynamics into viral kinetics frameworks. We validate this new approach using patient data for pre-alpha COVID-19 strain and an HIV, then we use it to study the impact of ISG-virus kinetics on viral infection severity and persistence. Our simulations reveal that increased ISG induction prolongs infection by suppressing type I IFN production in infected cells and preventing tissue cell depletion. We further show that effective ISG-mediated viral suppression is critical for controlling infection severity. Finally, the model predicts that moderate viral secretion optimizes viral load production. Overall, the developed framework offers a flexible and computationally efficient tool for exploring the impact of intracellular type I interferon signaling on viral infections. It can be easily adapted to specific diseases and extended with pharmacokinetics-pharmacodynamics models to identify key therapeutic targets for drug development.
{"title":"Intracellular ISG-virus interactions determine viral infection severity and persistence","authors":"Anass Bouchnita , Vitaly Volpert","doi":"10.1016/j.jtbi.2025.112251","DOIUrl":"10.1016/j.jtbi.2025.112251","url":null,"abstract":"<div><div>In innate immune response, type I interferons (IFNs) activate interferon-stimulated genes (ISGs), which suppress viral replication and secretion at the intracellular level. Yet, how these ISG-virus interactions shape infection progression and severity remains poorly understood. Here, we introduce a new viral infection model that explicitly incorporates intracellular ISG-virus dynamics. It structures, for the first time, infected cells based on viral load and ISG expression which offers a computationally efficient and adaptable approach to integrating ISG-virus intracellular dynamics into viral kinetics frameworks. We validate this new approach using patient data for pre-alpha COVID-19 strain and an HIV, then we use it to study the impact of ISG-virus kinetics on viral infection severity and persistence. Our simulations reveal that increased ISG induction prolongs infection by suppressing type I IFN production in infected cells and preventing tissue cell depletion. We further show that effective ISG-mediated viral suppression is critical for controlling infection severity. Finally, the model predicts that moderate viral secretion optimizes viral load production. Overall, the developed framework offers a flexible and computationally efficient tool for exploring the impact of intracellular type I interferon signaling on viral infections. It can be easily adapted to specific diseases and extended with pharmacokinetics-pharmacodynamics models to identify key therapeutic targets for drug development.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112251"},"PeriodicalIF":2.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066443","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 : 2026-01-07Epub 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":"2026-01-07","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 : 2026-01-07Epub 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":"2026-01-07","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}
Several computational models are available for representing the gene expression process, with each having their advantages and disadvantages. Phenomenological models are widely used as they make appropriate simplifications that aim to find a middle ground between accuracy and complexity. The existing phenomenological models compete in terms of how the transcription initiation process is approximated, to achieve high accuracy while having the lowest complexity possible. However, most current models still suffer from high parameter complexity in the case of complex promoters. Herein, we formally derive a phenomenological approach to model RNA polymerase recruitment, stating approximations on cooperativity between transcription factors that are applicable to promoters requiring multifactorial input, which reduces parameter complexity. We then apply this method to biologically relevant networks of varying complexities to show that the approximations improved predictive ability compared to existing models. In summary, our reduced parameter model (RPM) had lower complexity while maintaining high accuracy, which leads to better scalability for complex networks.
{"title":"Phenomenological modeling of gene transcription by approximating cooperativity of transcription factors improves prediction and reduces complexity in gene regulatory network models","authors":"Thiruvickraman Jothiprakasam, Siddharth Jhunjhunwala","doi":"10.1016/j.jtbi.2025.112264","DOIUrl":"10.1016/j.jtbi.2025.112264","url":null,"abstract":"<div><div>Several computational models are available for representing the gene expression process, with each having their advantages and disadvantages. Phenomenological models are widely used as they make appropriate simplifications that aim to find a middle ground between accuracy and complexity. The existing phenomenological models compete in terms of how the transcription initiation process is approximated, to achieve high accuracy while having the lowest complexity possible. However, most current models still suffer from high parameter complexity in the case of complex promoters. Herein, we formally derive a phenomenological approach to model RNA polymerase recruitment, stating approximations on cooperativity between transcription factors that are applicable to promoters requiring multifactorial input, which reduces parameter complexity. We then apply this method to biologically relevant networks of varying complexities to show that the approximations improved predictive ability compared to existing models. In summary, our reduced parameter model (RPM) had lower complexity while maintaining high accuracy, which leads to better scalability for complex networks.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"616 ","pages":"Article 112264"},"PeriodicalIF":2.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030705","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 : 2025-12-07Epub Date: 2025-08-24DOI: 10.1016/j.jtbi.2025.112248
Punya Alahakoon , Peter G. Taylor , James M. McCaw
COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected millions of people around the globe. We studied the spread of SARS-CoV-2 across six rural counties in North and South Dakota in the United States. The study period was from early March 2020 to mid-June 2021, during which non-pharmaceutical interventions (NPIs) were in place. The end of the study period coincided with the emergence of the Delta variant in the United States. We modelled the transmission dynamics in each county using a stochastic compartmental model and analysed the data within a Bayesian hierarchical statistical framework. We estimated key epidemiological and surveillance parameters including the reproduction number and reporting probability. We conducted a series of counterfactual analyses in which NPIs were lifted earlier and by varying degrees, modelled as an increase in the transmission rate. Under this range of plausible alternative responses, increases in case counts varied from negligible to substantial, underscoring the importance of timely public health measures and compliance with them. From a methodological perspective, our study demonstrates that despite the inherent high variability in epidemic behaviour in small rural communities, the combination of stochastic modelling and application of Bayesian hierarchical analyses enables the estimation of key epidemiological and surveillance parameters and consideration of the potential impact of alternative public health measures in small low population density communities.
{"title":"Stochastic modelling of early-stage COVID-19 epidemic dynamics in rural communities in the United States","authors":"Punya Alahakoon , Peter G. Taylor , James M. McCaw","doi":"10.1016/j.jtbi.2025.112248","DOIUrl":"10.1016/j.jtbi.2025.112248","url":null,"abstract":"<div><div>COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected millions of people around the globe. We studied the spread of SARS-CoV-2 across six rural counties in North and South Dakota in the United States. The study period was from early March 2020 to mid-June 2021, during which non-pharmaceutical interventions (NPIs) were in place. The end of the study period coincided with the emergence of the Delta variant in the United States. We modelled the transmission dynamics in each county using a stochastic compartmental model and analysed the data within a Bayesian hierarchical statistical framework. We estimated key epidemiological and surveillance parameters including the reproduction number and reporting probability. We conducted a series of counterfactual analyses in which NPIs were lifted earlier and by varying degrees, modelled as an increase in the transmission rate. Under this range of plausible alternative responses, increases in case counts varied from negligible to substantial, underscoring the importance of timely public health measures and compliance with them. From a methodological perspective, our study demonstrates that despite the inherent high variability in epidemic behaviour in small rural communities, the combination of stochastic modelling and application of Bayesian hierarchical analyses enables the estimation of key epidemiological and surveillance parameters and consideration of the potential impact of alternative public health measures in small low population density communities.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"615 ","pages":"Article 112248"},"PeriodicalIF":2.0,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979427","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 : 2025-12-07Epub Date: 2025-08-10DOI: 10.1016/j.jtbi.2025.112237
George Booth , Christoforos Hadjichrysanthou , Keira L. Rice , Jacopo Frallicciardi , Zoltán Magyarics , Frank de Wolf , Jaap Goudsmit , Anna L. Beukenhorst , Roy Anderson
Introduction
Superspreading events are known to disproportionally contribute to onwards transmission of epidemic and pandemic viruses. Preventing infections in a small number of high-transmission settings is therefore an attractive public health goal.
Methods
We use deterministic and stochastic mathematical modelling to quantify the impact of intranasal sprays in containing outbreaks at a confirmed superspreading event (the 2020 SARS-CoV-2 outbreak at the Diamond Princess cruise ship) and a conference event that led to extensive transmission.
Results
In the Diamond Princess cruise ship case study, there exists a 7–14-day window of opportunity for widespread prophylactic intranasal spray usage to significantly impact the number of infections averted. Given an immediate response to a known SARS-CoV-2 outbreak, alongside testing and social distancing measures, prophylactic efficacy and coverage greater than 65% could reduce the average number of infections by over 90%. In the conference case study, in the absence of additional public health interventions, analyses suggest much higher prophylactic efficacy and coverage is required to achieve a similar outcome on a population level. However, prophylactic use can halve an individual’s probability of being infected, and significantly reduce the probability of developing a severe infection.
Conclusions
At a known potential superspreading event, early use of intranasal sprays can complement quarantining measures and significantly suppress a SARS-CoV-2 outbreak, even at suboptimal coverage. At a potential superspreading event of short duration, intranasal sprays can reduce individuals’ risk of infection, but in the absence of other interventions, they cannot prevent all infections or all onwards community transmission.
Plain language summary
Where crowds are in close contact in closed spaces, respiratory viruses like coronavirus spread easily. At such events, superspreading may occur: one person transmitting the virus to many other event-goers, fuelling the epidemic or pandemic. We used mathematical modelling to predict whether antiviral nose sprays which act immediately can prevent such superspreading events. We found that early use of nose sprays can suppress a SARS-CoV-2 outbreak, even if not everybody is treated with the nose spray, as long as people are also tested and use social distancing if infected. At a conference where people do not quarantine, it is more difficult to prevent spreading of the virus altogether with nose sprays alone. However, at an individual level, people who take the nose spray have lower chance of getting infected with the virus.
{"title":"Preventing SARS-CoV-2 superspreading events with antiviral intranasal sprays","authors":"George Booth , Christoforos Hadjichrysanthou , Keira L. Rice , Jacopo Frallicciardi , Zoltán Magyarics , Frank de Wolf , Jaap Goudsmit , Anna L. Beukenhorst , Roy Anderson","doi":"10.1016/j.jtbi.2025.112237","DOIUrl":"10.1016/j.jtbi.2025.112237","url":null,"abstract":"<div><h3>Introduction</h3><div>Superspreading events are known to disproportionally contribute to onwards transmission of epidemic and pandemic viruses. Preventing infections in a small number of high-transmission settings is therefore an attractive public health goal.</div></div><div><h3>Methods</h3><div>We use deterministic and stochastic mathematical modelling to quantify the impact of intranasal sprays in containing outbreaks at a confirmed superspreading event (the 2020 SARS-CoV-2 outbreak at the Diamond Princess cruise ship) and a conference event that led to extensive transmission.</div></div><div><h3>Results</h3><div>In the Diamond Princess cruise ship case study, there exists a 7–14-day window of opportunity for widespread prophylactic intranasal spray usage to significantly impact the number of infections averted. Given an immediate response to a known SARS-CoV-2 outbreak, alongside testing and social distancing measures, prophylactic efficacy and coverage greater than 65% could reduce the average number of infections by over 90%. In the conference case study, in the absence of additional public health interventions, analyses suggest much higher prophylactic efficacy and coverage is required to achieve a similar outcome on a population level. However, prophylactic use can halve an individual’s probability of being infected, and significantly reduce the probability of developing a severe infection.</div></div><div><h3>Conclusions</h3><div>At a known potential superspreading event, early use of intranasal sprays can complement quarantining measures and significantly suppress a SARS-CoV-2 outbreak, even at suboptimal coverage. At a <em>potential</em> superspreading event of short duration, intranasal sprays can reduce individuals’ risk of infection, but in the absence of other interventions, they cannot prevent all infections or all onwards community transmission.</div></div><div><h3>Plain language summary</h3><div>Where crowds are in close contact in closed spaces, respiratory viruses like coronavirus spread easily. At such events, superspreading may occur: one person transmitting the virus to many other event-goers, fuelling the epidemic or pandemic. We used mathematical modelling to predict whether antiviral nose sprays which act immediately can prevent such superspreading events. We found that early use of nose sprays can suppress a SARS-CoV-2 outbreak, even if not everybody is treated with the nose spray, as long as people are also tested and use social distancing if infected. At a conference where people do not quarantine, it is more difficult to prevent spreading of the virus altogether with nose sprays alone. However, at an individual level, people who take the nose spray have lower chance of getting infected with the virus.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"615 ","pages":"Article 112237"},"PeriodicalIF":2.0,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838612","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 : 2025-12-07Epub Date: 2025-08-24DOI: 10.1016/j.jtbi.2025.112252
Tadeas Priklopil , Kirsten Bomblies , Alex Widmer
Proper protein folding is essential for biological function, and its disruption can lead to disease, reduced fitness, or death. The ability of a protein to maintain its folded conformation is thus critical for life, making it a key target of adaptive evolution. However, protein stability is sensitive to environmental factors, particularly temperature, which can threaten phenotypic integrity and organismal survival under thermal changes. Despite its importance, the influence of complex thermal environments – characterized here by mean temperature, thermal fluctuations, and environmental heterogeneity – on the evolution of protein stability remains poorly understood. To address this, we developed a mathematical framework that combines two well-established models: a population genetic model describing species distributed across habitats with distinct thermal environments, and a thermodynamic model of protein stability incorporating temperature-dependent enthalpy and entropy contributions. We focus on two-state proteins that alternate between folded and unfolded states and assume that allelic fitness is maximized in proteins that achieve an optimal balance between flexibility and rigidity. Using this framework, we performed an invasion analysis of mutations (sensu adaptive dynamics framework) affecting three thermodynamic parameters that fully determine protein stability profiles. Where possible, we derived analytical expressions for evolutionarily optimal thermodynamic parameters and complemented these with numerical solutions. Our results show that mean temperature and thermal fluctuations have orthogonal effects on thermodynamic parameters, underscoring the need to consider both when studying protein stability adaptation. We further examined thermally heterogeneous environments, where subpopulations connected by migration experience different mean temperatures, identifying conditions that favor either local (specialist) or global (generalist) adaptation. Our results may explain why one thermodynamic parameter shows little association with thermal adaptation and suggest that local adaptation is more likely for proteins with stability profiles limited to narrow temperature ranges. Additionally, our analysis reveals whether a locally adapted protein originated in a colder or warmer habitat. Finally, we identified trade-offs in thermodynamic parameters that influence local or global adaptation. This study offers key predictions about protein evolution in complex thermal environments and lays the groundwork for developing practical tools to understand how temperature shapes adaptation and biodiversity.
{"title":"Adaptation of protein stability to thermally heterogeneous environments","authors":"Tadeas Priklopil , Kirsten Bomblies , Alex Widmer","doi":"10.1016/j.jtbi.2025.112252","DOIUrl":"10.1016/j.jtbi.2025.112252","url":null,"abstract":"<div><div>Proper protein folding is essential for biological function, and its disruption can lead to disease, reduced fitness, or death. The ability of a protein to maintain its folded conformation is thus critical for life, making it a key target of adaptive evolution. However, protein stability is sensitive to environmental factors, particularly temperature, which can threaten phenotypic integrity and organismal survival under thermal changes. Despite its importance, the influence of complex thermal environments – characterized here by mean temperature, thermal fluctuations, and environmental heterogeneity – on the evolution of protein stability remains poorly understood. To address this, we developed a mathematical framework that combines two well-established models: a population genetic model describing species distributed across habitats with distinct thermal environments, and a thermodynamic model of protein stability incorporating temperature-dependent enthalpy and entropy contributions. We focus on two-state proteins that alternate between folded and unfolded states and assume that allelic fitness is maximized in proteins that achieve an optimal balance between flexibility and rigidity. Using this framework, we performed an invasion analysis of mutations (<em>sensu</em> adaptive dynamics framework) affecting three thermodynamic parameters that fully determine protein stability profiles. Where possible, we derived analytical expressions for evolutionarily optimal thermodynamic parameters and complemented these with numerical solutions. Our results show that mean temperature and thermal fluctuations have orthogonal effects on thermodynamic parameters, underscoring the need to consider both when studying protein stability adaptation. We further examined thermally heterogeneous environments, where subpopulations connected by migration experience different mean temperatures, identifying conditions that favor either local (specialist) or global (generalist) adaptation. Our results may explain why one thermodynamic parameter shows little association with thermal adaptation and suggest that local adaptation is more likely for proteins with stability profiles limited to narrow temperature ranges. Additionally, our analysis reveals whether a locally adapted protein originated in a colder or warmer habitat. Finally, we identified trade-offs in thermodynamic parameters that influence local or global adaptation. This study offers key predictions about protein evolution in complex thermal environments and lays the groundwork for developing practical tools to understand how temperature shapes adaptation and biodiversity.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"615 ","pages":"Article 112252"},"PeriodicalIF":2.0,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979374","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 : 2025-12-07Epub Date: 2025-08-30DOI: 10.1016/j.jtbi.2025.112256
F.E. Cornes , R.H. Barriga Rubio , M. Otero
We present an extension of a previously developed stochastic, stage-structured model of Dalbulus maidis (corn leafhopper), an important pest and vector in maize crops. The extended model introduces nonlinear density-dependent regulation on the nymphal stage, mediated by a carrying capacity that dynamically depends on the leaf area of maize plants. Both insect and host-plant dynamics are explicitly modeled, but the interaction is asymmetric, as the plant is not affected by the insect in the present formulation. Our main objective is to explore how the interplay between temperature-driven development and host-plant dynamics shapes the long-term behavior of the insect population, leading to either extinction or persistence. Using simulations parameterized with laboratory and field data, we analyze how temperature and maize development affect insect dynamics, and assess whether the model can reproduce observed abundance patterns under realistic conditions. This modeling framework provides a biologically grounded and flexible basis for future extensions, including pathogen transmission and bidirectional feedback between the maize and the insect.
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Pub Date : 2025-12-07Epub Date: 2025-08-28DOI: 10.1016/j.jtbi.2025.112253
Gideon A. Ngwa , Bime M. Ghakanyuy , Miranda I. Teboh-Ewungkem , Jacek Banasiak
A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito’s aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating on mosquito dynamics. The model uses a restricted form of homogeneous mixing based on the idea that the mosquito has a blood-feeding habit determined by its blood-feeding preferences and its gonotrophic cycle. This characterisation allows us to compartmentalise the total mosquito population into distinct compartments according to the spatial location of the mosquito (breeding site, resting places and questing places) as well as blood-fed status. Issues of overcrowding and intraspecific competition both within the aquatic and the terrestrial stages of the mosquito’s life forms are addressed and considered in the model. Results show that the inclusion of mating induces bistability, a phenomenon whereby locally stable trivial and non-trivial equilibria co-exist with an unstable non-zero equilibrium. The local nature of the stable equilibria is demonstrated by numerically showing that the long-term state of the system is sensitive to initial conditions. The bistability state is analogous to the phenomenon of the Allee effect that has been reported in population biology. The model’s results, including the derivation of the threshold parameter of the system, are comprehensively tested via numerical simulations. The output of our model has direct application to mosquito control strategies, for it clearly shows key points in the mosquito’s developmental pathway that can be targeted for control purposes.
{"title":"Mating versus alternative blood sources as determinants to mosquito abundance and population resilience","authors":"Gideon A. Ngwa , Bime M. Ghakanyuy , Miranda I. Teboh-Ewungkem , Jacek Banasiak","doi":"10.1016/j.jtbi.2025.112253","DOIUrl":"10.1016/j.jtbi.2025.112253","url":null,"abstract":"<div><div>A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito’s aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating on mosquito dynamics. The model uses a restricted form of homogeneous mixing based on the idea that the mosquito has a blood-feeding habit determined by its blood-feeding preferences and its gonotrophic cycle. This characterisation allows us to compartmentalise the total mosquito population into distinct compartments according to the spatial location of the mosquito (breeding site, resting places and questing places) as well as blood-fed status. Issues of overcrowding and intraspecific competition both within the aquatic and the terrestrial stages of the mosquito’s life forms are addressed and considered in the model. Results show that the inclusion of mating induces bistability, a phenomenon whereby locally stable trivial and non-trivial equilibria co-exist with an unstable non-zero equilibrium. The local nature of the stable equilibria is demonstrated by numerically showing that the long-term state of the system is sensitive to initial conditions. The bistability state is analogous to the phenomenon of the Allee effect that has been reported in population biology. The model’s results, including the derivation of the threshold parameter of the system, are comprehensively tested via numerical simulations. The output of our model has direct application to mosquito control strategies, for it clearly shows key points in the mosquito’s developmental pathway that can be targeted for control purposes.</div></div>","PeriodicalId":54763,"journal":{"name":"Journal of Theoretical Biology","volume":"615 ","pages":"Article 112253"},"PeriodicalIF":2.0,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979364","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}
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