Carli Peterson, Darsh Gandhi, Austin Carlson, Aaron Lubkemann, Emma Richardson, John Serralta, Michael S Allen, Souvik Roy, Christopher M Kribs, Hristo V Kojouharov
{"title":"A SIMPL Model of Phage-Bacteria Interactions Accounting for Mutation and Competition.","authors":"Carli Peterson, Darsh Gandhi, Austin Carlson, Aaron Lubkemann, Emma Richardson, John Serralta, Michael S Allen, Souvik Roy, Christopher M Kribs, Hristo V Kojouharov","doi":"10.1007/s11538-025-01478-2","DOIUrl":"10.1007/s11538-025-01478-2","url":null,"abstract":"<p><p>Pseudomonas aeruginosa is an opportunistically pathogenic bacteria that causes fatal infections and outbreaks in hospital environments. Due to the increasing prevalence of antibiotic-resistant strains of P. aeruginosa, the need for alternative therapies is critical. Bacteriophage therapy is emerging as a promising approach; however, it remains unapproved for clinical use and is hindered by limited understanding of the complex interactions between bacterial cells and phage virions. Mathematical models provide insight into these interactions. Through a system of ordinary differential equations, we successfully capture the dynamics observed between susceptible, infected, and mutated bacterial cells and bacteriophage virions in a microwell setting. Data fitting based on this model produced a set of parameter estimates unique to our experimental observations of a specific phage and P. aeruginosa strain. In translating observed optical density readings into bacterial concentrations, we also found that bacterial debris has a significant impact on optical density, with a lysed bacterium contributing roughly <math><mrow><mn>31</mn> <mo>%</mo></mrow> </math> as much to optical density readings as a living cell.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"96"},"PeriodicalIF":2.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144301136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Perfect Taxon Sampling and Fixing Taxon Traceability: Introducing a Class of Phylogenetically Decisive Collections of Taxon Sets.","authors":"Mareike Fischer, Janne Pott","doi":"10.1007/s11538-025-01457-7","DOIUrl":"10.1007/s11538-025-01457-7","url":null,"abstract":"<p><p>Phylogenetically decisive collections of taxon sets have the property that if trees are chosen for each of their elements, as long as these trees are compatible, the resulting supertree is unique. This means that as long as the trees describing the phylogenetic relationships of the (input) species sets are compatible, they can only be combined into a common supertree in precisely one way. This setting is sometimes also referred to as \"perfect taxon sampling\". While for rooted trees, the decision if a given set of input taxon sets is phylogenetically decisive can be made in polynomial time, the decision problem to determine whether a collection of taxon sets is phylogenetically decisive concerning unrooted trees is unfortunately coNP-complete and therefore in practice hard to solve for large instances. This shows that recognizing such sets is often difficult. In this paper, we explain phylogenetic decisiveness and introduce a class of input taxon sets, namely so-called fixing taxon traceable sets, which are guaranteed to be phylogenetically decisive and which can be recognized in polynomial time. Using both combinatorial approaches as well as simulations, we compare properties of fixing taxon traceability and phylogenetic decisiveness, e.g., by deriving lower and upper bounds for the number of quadruple sets (i.e., sets of 4-tuples) needed in the input set for each of these properties. In particular, we correct an erroneous lower bound concerning phylogenetic decisiveness from the literature. We have implemented the algorithm to determine if a given collection of taxon sets is fixing taxon traceable in R and made our software package FixingTaxonTraceR publicly available.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"94"},"PeriodicalIF":2.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12152082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144257348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of Anaerobic Digestion Model With Two Serial Interconnected Chemostats.","authors":"Thamer Hmidhi, Radhouane Fekih-Salem, Jérôme Harmand","doi":"10.1007/s11538-025-01475-5","DOIUrl":"10.1007/s11538-025-01475-5","url":null,"abstract":"<p><p>In this paper, we study a well-known two-step anaerobic digestion model in a configuration of two chemostats in series. The model is an eight-dimensional system of ordinary differential equations. Since the reaction system has a cascade structure, the model can be reduced to a four-dimensional one. Using general growth rates, we provide an in-depth mathematical analysis of the asymptotic behavior of the system. First, we determine all the equilibria of the model where there can be fifteen equilibria with a nonmonotonic growth rate. Then, the necessary and sufficient conditions of existence and local stability of all equilibria are established according to the operating parameters: the dilution rate, the input concentrations of the two nutrients, and the distribution of the total process volume considered. The operating diagrams are then theoretically analyzed to describe the asymptotic behavior of the process according to the four control parameters. The system exhibits a rich behavior with bistability, tri-stability, and the possibility of coexistence of the two microbial species in the two bioreactors.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"95"},"PeriodicalIF":2.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144257347","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}
Ruby E Nixson, Helen M Byrne, Joe M Pitt-Francis, Philip K Maini
{"title":"Characterising the Behaviour of a Structured PDE Model of the Cell Cycle in Contrast to a Corresponding ODE System.","authors":"Ruby E Nixson, Helen M Byrne, Joe M Pitt-Francis, Philip K Maini","doi":"10.1007/s11538-025-01472-8","DOIUrl":"10.1007/s11538-025-01472-8","url":null,"abstract":"<p><p>Experimental results have shown that anti-cancer therapies, such as radiotherapy and chemotherapy, can modulate the cell cycle and generate cell cycle phase-dependent responses. As a result, obtaining a detailed understanding of the cell cycle is one possible path towards improving the efficacy of many of these therapies. Here, we consider a basic structured partial differential equation (PDE) model for cell progression through the cell cycle, and derive expressions for key quantities, such as the population growth rate and cell phase proportions. These quantities are shown to be periodic and, as such, we compare the PDE model to a corresponding ordinary differential equation (ODE) model in which the parameters are linked by ensuring that the long-term ODE behaviour agrees with the average PDE behaviour. By design, we find that the ODE model does an excellent job of representing the mean dynamics of the PDE model within just a few cell cycles. However, by probing the parameter space we find cases in which this mean behaviour is not a good measure of the PDE population growth. Our analytical comparison of two caricature models (one PDE and one ODE system) provides insight into cases in which the simple ODE model is an appropriate approximation to the PDE model.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"93"},"PeriodicalIF":2.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12146229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246501","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}
Ioannis Lampropoulos, Panayotis G Kevrekidis, Christos E Zois, Helen Byrne, Michail Kavousanakis
{"title":"Spatio-Temporal Dynamics of M<sub>1</sub> and M<sub>2</sub> Macrophages in a Multiphase Model of Tumor Growth.","authors":"Ioannis Lampropoulos, Panayotis G Kevrekidis, Christos E Zois, Helen Byrne, Michail Kavousanakis","doi":"10.1007/s11538-025-01466-6","DOIUrl":"10.1007/s11538-025-01466-6","url":null,"abstract":"<p><p>This study investigates the complex dynamics of vascular tumors and their interplay with macrophages, key agents of the innate immune response. We model the tumor microenvironment as a multiphase fluid, with each cellular population treated as a distinct, non-mixing phase. The framework also incorporates diffusible species that are critical for processes such as nutrient transport, angiogenesis, chemotaxis, and macrophage activation. A central contribution of this work is the explicit modeling of macrophage infiltration and polarization within the tumor microenvironment. The model captures the divergent roles of <math><msub><mtext>M</mtext> <mn>1</mn></msub> </math> (anti-tumor) and <math><msub><mtext>M</mtext> <mn>2</mn></msub> </math> (pro-tumor) macrophages and their influence on tumor aggressiveness and progression. Through numerical simulations, we demonstrate the emergence of both spatial and phenotypic heterogeneity in the macrophage population, including their peripheral localization and limited core infiltration -patterns consistent with experimental observations. Furthermore, this is the first multiphase model to incorporate the effects of TGF- <math><mi>β</mi></math> -targeting immunotherapy using vactosertib. Our simulations demonstrate that treatment initially enhances the presence of anti-tumor macrophages, followed by a relapse period where tumor dynamics returns to pre-treatment trends. Model parameters are grounded in experimental data and clinically relevant dosage protocols.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"92"},"PeriodicalIF":2.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Effect of Disease Stage on Melanoma Treatment Efficacy: Insights from Mathematical Modeling and In vivo Clinical Data.","authors":"Mohammad Amini, Ramin Vatankhah","doi":"10.1007/s11538-025-01458-6","DOIUrl":"10.1007/s11538-025-01458-6","url":null,"abstract":"<p><p>Melanoma progression can be effectively modeled through mathematical frameworks, making it a pivotal focus for enhancing our understanding of cancer dynamics and informing personalized treatment strategies. The present research investigates the growth dynamics of melanoma by analyzing 16 individual melanoma cell lines, utilizing in vivo clinical data that spans a range of metastatic stages from primary melanoma to stage IV. The study estimates growth rates across these cell lines by implementing a power law model through nonlinear least squares, uncovering distinct mathematical patterns linked to melanoma stages. Furthermore, the research evaluates the efficacy of various treatment strategies tailored to each disease stage through a chemoimmunotherapy mathematical model. For primary and early-stage melanoma, where tumors are localized, surgical excision is identified as the most effective intervention, often enhanced by CD4<sup>+</sup>T cell immunotherapy. In cases of low to moderately metastatic melanoma, a combination of low-dose chemotherapy with CD8<sup>+</sup>T cell immunotherapy effectively targets metastatic lesions, reducing systemic toxicity while promoting a strong immune response. For highly metastatic melanoma, which presents significant treatment challenges, a combination therapy involving both CD8<sup>+</sup>T and CD4<sup>+</sup>T cell immunotherapy is recommended. This dual approach utilizes the direct tumor-killing effects of CD8<sup>+</sup>T cells alongside the supportive actions of CD4<sup>+</sup>T cells, resulting in improved treatment efficacy and survival outcomes. Overall, this research provides a comprehensive analysis of melanoma cell lines at various stages, integrating mathematical modeling with treatment efficacy to enhance personalized treatment strategies in melanoma management.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"90"},"PeriodicalIF":2.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finding Reproduction Numbers for Epidemic Models and Predator-Prey Models of Arbitrary Finite Dimension Using the Generalized Linear Chain Trick.","authors":"Paul J Hurtado, Cameron Richards","doi":"10.1007/s11538-025-01467-5","DOIUrl":"10.1007/s11538-025-01467-5","url":null,"abstract":"<p><p>Reproduction numbers, like the basic reproduction number <math><msub><mi>R</mi> <mn>0</mn></msub> </math> , play an important role in the analysis and application of dynamic models, including contagion models and ecological population models. One difficulty in deriving these quantities is that they must be computed on a model-by-model basis, since it is typically impractical to obtain general reproduction number expressions applicable to a family of related models, especially if these are of different dimensions (i.e., differing numbers of state variables). For example, this is typically the case for SIR-type infectious disease models derived using the linear chain trick. Here we show how to find general reproduction number expressions for such model families (which vary in their number of state variables) using the next generation operator approach in conjunction with the generalized linear chain trick (GLCT). We further show how the GLCT enables modelers to draw insights from these results by leveraging theory and intuition from continuous time Markov chains (CTMCs) and their absorption time distributions (i.e., phase-type probability distributions). To do this, we first review the GLCT and other connections between mean-field ODE model assumptions, CTMCs, and phase-type distributions. We then apply this technique to find reproduction numbers for two sets of models: a family of generalized SEIRS models of arbitrary finite dimension, and a generalized family of finite dimensional predator-prey (Rosenzweig-MacArthur type) models. These results highlight the utility of the GLCT for the derivation and analysis of mean field ODE models, especially when used in conjunction with theory from CTMCs and their associated phase-type distributions.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"89"},"PeriodicalIF":2.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12133974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207806","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}
David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka, Matthew Wheeler, Reinhard Laubenbacher
{"title":"Modular Control of Boolean Network Models.","authors":"David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka, Matthew Wheeler, Reinhard Laubenbacher","doi":"10.1007/s11538-025-01471-9","DOIUrl":"10.1007/s11538-025-01471-9","url":null,"abstract":"<p><p>The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications often focus on model-based control, such as in biomedicine or metabolic engineering. In a recent paper, the authors developed a theoretical framework of modularity in Boolean networks, which led to a canonical semidirect product decomposition of these systems. In this paper, we present an approach to model-based control that exploits this modular structure, as well as the canalizing features of the regulatory mechanisms. We show how to identify control strategies from the individual modules, and we present a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally challenging. Our modular approach leads to an efficient approach to solving this problem. We apply it to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"91"},"PeriodicalIF":2.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12133937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144215001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mathematical Modelling and Optimization of Medication Regimens for Combination Immunotherapy of Breast Cancer.","authors":"Zixiao Xiong, Yunfei Xia, Ling Xue, Jinzhi Lei","doi":"10.1007/s11538-025-01459-5","DOIUrl":"10.1007/s11538-025-01459-5","url":null,"abstract":"<p><p>Immunotherapy is an emerging and effective treatment for cancer. The mRNA-based cancer vaccines enhance the immune response to cancer cells by activating T cells. However, the cytotoxic T-lymphocyte antigen (CTLA-4) receptor signaling inhibits T-cell activation, thereby reducing the effectiveness of the mRNA-based vaccines. Fortunately, the anti-CTLA-4 monoclonal antibody therapy can block CTLA-4 signaling. Nevertheless, the use of anti-CTLA-4 antibodies is also accompanied by immunotoxic side effects. Therefore, an effective and safe medication regimen plays an essential role in the treatment of cancer. First, we develop a mathematical model to describe the interaction of mRNA-based cancer vaccines and anti-CTLA-4 antibodies under the tumor immune microenvironment. Secondly, by employing the method of Markov Chain Monte Carlo (MCMC), the model is parameterized using experimental data, and the simulations are in agreement with experimental results. Finally, the gradient descent method is designed to optimize the medication regimens to inhibit tumor growth and reduce the side effects. Additionally, we find that the anti-CTLA-4 antibody should be administered following vaccination, and the dose of the antibody should positively correlate with the dose of vaccine within a safe range. Our study provides a theoretical basis for the selection of treatment regimens for clinical trials from a mathematical perspective.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"88"},"PeriodicalIF":2.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transmission of Multiple Pathogens Across Species.","authors":"Clotilde Djuikem, Julien Arino","doi":"10.1007/s11538-025-01469-3","DOIUrl":"10.1007/s11538-025-01469-3","url":null,"abstract":"<p><p>We analyse a model that describes the propagation of many pathogens within and between many species. A branching process approximation is used to compute the probability of disease outbreaks. Special cases of aquatic environments with two host species and one or two pathogens are considered both analytically and computationally.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 7","pages":"87"},"PeriodicalIF":2.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172827","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}