Special Session 52: Differential Equations and Dynamical Systems in Mathematical Biology

Period Homeostasis Near Hopf Bifurcation

Janet Best
The Ohio State University
USA
Co-Author(s):    Steve Manns, Martin Golubitsky
Abstract:
Homeostasis is a biological phenomenon in which a function of the state of the system remains approximately constant as an input parameter varies. Recently, Golubitsky and collaborators have developed a mathematical theory of homeostasis based on methods from singularity theory. In this theory, the state is a steady state, and the function is often a coordinate of the steady state. The weaker requirement that a function of the state remain approximately constant is replaced by the requirement that the derivative of the function with respect to the input parameter vanishes at an isolated point. This notion of a zero derivative has the name (steady-state) infinitesimal homeostasis, by which we mean that we are focusing on a function of a system steady state. Homeostasis can also apply to many oscillatory biological processes. For example, the period of circadian rhythms remains approximately constant as parameters such as ambient temperature and gene expression levels vary. In this talk, I will present results on infinitesimal homeostasis for periodic phenomena in models that undergo nondegenerate Hopf bifurcation. Our main result in this setup is that singularity theory can be used to find period homeostasis. This result is illustrated using numerical simulations of a simple model of the mammalian circadian clock. Our theoretical results are based on having two parameters (a bifurcation parameter and an input parameter) and the $S^1$ symmetry of Hopf normal form.

Agent-based and continuum models for spatial dynamics of infection by oncolytic viruses

Marcello Delitala
Politecnico di Torino
Italy
Co-Author(s):    David Morselli and Federico Frascoli
Abstract:
We present a stochastic agent-based model for the spatial dynamics of infection by oncolytic viruses in solid tumours. The model describes the interactions between uninfected and infected tumour cells and considers two alternative movement mechanisms, namely undirected random motion and pressure-driven displacement. For both cases, we obtain the corresponding continuum equations and carry out a systematic comparison between the individual-based and PDE descriptions in one and two space dimensions. We also study the associated one-dimensional travelling waves. In the case of undirected motion, the agreement between agent-based simulations and the numerical and analytical results for the continuum model is good. For pressure-driven motion, instead, marked discrepancies emerge: over a wide parameter range, infection remains confined to the tumour core, even though the continuum model predicts travelling infection waves. These results highlight the significant impact of spatial constraints in the tumour microenvironment on virotherapy, as well as the central role of stochastic effects. The same modelling framework also provides a natural basis for extensions including explicit viral particles, making it possible to assess when reduced quasi-steady descriptions are adequate and when viral kinetics significantly affect front propagation and transient behaviour. This talk is based on results published in Bulletin of Mathematical Biology 85:92 (2023) and 88:66 (2026).

A two-patch SEIR model with prevalence-dependent behavioural response and mobility for Ebola dynamics

Attila Denes
Bolyai Institute, University of Szeged
Hungary
Co-Author(s):    Abba B. Gumel
Abstract:
We develop a two-patch SEIR model for Ebola transmission that incorporates human behaviour and mobility. We consider two patches representing urban and rural environments, and in each patch, the population is further divided into two groups based on their willingness to adhere to intervention measures. Both adherence and travel is influenced by disease prevalence. Unlike traditional approaches assuming permanent migration, we adopt a more realistic framework where individuals retain residency in their home region while spending time in the other region. We analyse key model properties, including the control reproduction number, and show that the disease dies out when this threshold is below one. Parameters are estimated by fitting a simplified version of the model -- excluding behavioural heterogeneity and travel -- to epidemiological data from the 2018--2020 outbreak in the Democratic Republic of Congo. We then conduct scenario-based analyses to investigate the impact of behavioural responses, misinformation, regional resource allocation, and travel duration on epidemic dynamics. The results highlight the importance of adaptive behaviour and mobility in shaping disease spread and control strategies.

Evolution into chaos -- implications of the trade-off between transmissibility and immune evasion

Abel Garab
University of Szeged
Hungary
Co-Author(s):    Golsa Sayyar and Gergely R\{o}st
Abstract:
Predicting viral evolution presents a significant challenge and is a critical public health priority. We develop a novel model that selects for a new strain with the highest invasion fitness, assuming a trade-off between immune evasion and transmissibility. In case the trade-off function is linear, we can describe the evolutionary patterns following the emergence of subsequent strains by a non-linear difference equation. We provide sufficient criteria for when evolution converges, and successive strains exhibit similar transmissibility. We also identify scenarios characterized by a two-periodic pattern in upcoming strains, indicating a situation where a highly transmissible but not immune-evasive strain is replaced by a less transmissible but highly immune-evasive strain, and vice versa, creating a cyclic pattern. Finally, we show that under certain conditions, viral evolution becomes chaotic and thus future transmissibility rates become unpredictable in the long run.

Energy-Driven Organization and Positivity in a Beetle-Inspired Droplet System with Non-Newtonian Fluids

Thomas Hagen
The University of Memphis
USA
Co-Author(s):    
Abstract:
Motivated by a surface based defense strategy of the leaf beetle \emph{Hemisphaerota cyanea}, we analyze a biomathematical model for surface tension driven fluid transport in networks of interconnected droplets. Curvature induced pressure differences generate volume exchange through narrow channels, producing competitive growth and extinction dynamics within the droplet ensemble. The model takes the form of a graph based system of ordinary differential equations and incorporates power law rheology to capture non Newtonian biological fluids.\ In the shear thickening regime, solution nonuniqueness challenges the interpretation of biologically meaningful behavior. We establish that positivity of droplet volumes is preserved, ensuring physical relevance despite the lack of uniqueness. Moreover, the long term dynamics are shown to be simple and robust: all trajectories converge to equilibria. An energy functional linked to surface area provides a Lyapunov structure that orders steady states and clarifies long term outcome selection.

Modeling Combination Therapy to Overcome NUPR1-mediated Docetaxel Resistance in Prostate and Pancreatic Cancer

Harsh Jain
University of Minnesota Duluth
USA
Co-Author(s):    
Abstract:
Resistance to the taxane docetaxel is a major obstacle in the treatment of solid tumors, particularly prostate and pancreatic cancer. The stress-response protein NUPR1 has been identified as an important molecular driver of docetaxel resistance, suggesting that pharmacologic inhibition of NUPR1 may restore treatment sensitivity. While the early NUPR1 inhibitor ZZW-115 demonstrated promising anti-tumor activity, concerns about cardiotoxicity motivated the development of next-generation NUPR1-targeting compounds such as AJO14 and related derivatives. \ In this work we develop mechanistic mathematical models to investigate tumor response to combined docetaxel and NUPR1-targeted therapy. The models are informed by preclinical data, including in vitro prostate cancer cell-line experiments and in vivo pancreatic cancer xenografts. Using these calibrated models, we quantify treatment effects, explore potential synergy between therapies, and extend the analysis to heterogeneous virtual populations to examine how variability in tumor growth and drug response influences outcomes. This framework provides a quantitative approach for evaluating strategies to overcome docetaxel resistance and for guiding the development of NUPR1-targeted combination therapies.

Obesity Epidemic: Impact of Genetics, Environment and Social Behavior

Yun Kang
Arizona State University
USA
Co-Author(s):    
Abstract:
This study develops a mathematical model to examine obesity dynamics within a population, incorporating genetics, social, behavioral, and environmental factors. The model segments the population into susceptible, obese, and recovered but at-risk classes, using a system of nonlinear ordinary differential equations to describe transitions between them. Key mechanisms include social influence, diet, physical activity, and public health interventions. Theoretical results identify equilibrium states and the conditions under which obesity prevalence persists or declines. Bifurcation analysis illustrates how intervention strategies-such as promoting healthy lifestyles or reducing social reinforcement of unhealthy behaviors-affect long-term outcomes. The findings suggest that both social norms and individual behavior strongly shape obesity dynamics, and that sustained, multifaceted interventions are required to reduce prevalence. This framework offers a quantitative tool for policymakers to design and evaluate targeted obesity prevention programs.

Is maximum tolerated dose (MTD) chemotherapy scheduling optimal for glioblastoma multiforme?

Chiu-Yen Kao
Claremont McKenna College
USA
Co-Author(s):    Chiu-Yen Kao, Seyyed Abbas Mohammadi, Mohsen Yousefnezhad
Abstract:
In this study, we investigate a control problem involving a reaction-diffusion partial differential equation (PDE). Specifically, the focus is on optimizing the chemotherapy scheduling for brain tumor treatment to minimize the remaining tumor cells post-chemotherapy. Our findings establish that a bang-bang increasing function is the unique solution, affirming the MTD scheduling as the optimal chemotherapy profile. Several numerical experiments on a real brain image with parameters from clinics are conducted for tumors located in the frontal lobe, temporal lobe, or occipital lobe. They confirm our theoretical results and suggest a correlation between the proliferation rate of the tumor and the effectiveness of the optimal treatment.

How does asthma prevent optic glioma?

Yangjin Kim
Konkuk University/Brown University
Korea
Co-Author(s):    Donggu Lee, Sean Lawler
Abstract:
Optic glioma, a slow-growing tumor, is associated with Neurofibromatosis type 1 (NF1) mutations and increased midkine (MDK) production. A connection between asthma and optic glioma has previously been observed, but the mechanisms are unclear. To elucidate the role of asthma in the regulation of glioma formation, we investigated the role of T cells and the subsequent pathways in the regulation of microglia, a key player in the glioma tumor microenvironment (TME). While asthma is often linked to chronic inflammation, our mathematical analysis and experimental evidence suggest that inflammation can play a key role in suppressing the proliferation of optic glioma cells via immune reprogramming of T cells and the delicate control of signaling networks in microglia. Our mathematical model unveils the complex interactions between tumor and immune cells in optic glioma. Our results indicate that asthma-induced T cell reprogramming inhibits tumor growth by promoting the release of decorin and a subsequent suppression of CCR8 and the intercellular binding kinetics in microglia, followed by blocking of CCL5 production in TME via suppression of NFkB. We developed anti-cancer strategies by leveraging this asthma-induced immune regulation. We also developed a multi-scale model that take into account spatial dynamics of immune cells and intracellular signaling in multi-scale framework in space and time.

Analysis of a two-phase model describing the growth of solid tumors

Anca Matioc
University of Regensburg
Germany
Co-Author(s):    Joachim Escher
Abstract:
In this talk we consider a two-phase model describing the growth of avascular solid tumors when taking into account the effects of cell-to-cell adhesion and taxis due to nutrient. The tumor is surrounded by healthy tissue which is the source of nutrient for tumor cells. In a three-dimensional context, we prove that the mathematical formulation corresponds to a well-posed problem, and find radially symmetric steady-state solutions of the problem. They appear in the regime where the rate of cell apoptosis to cell proliferation is less than the far field nutrient concentration. Furthermore, we study the stability properties of those radially symmetric equilibria and find, depending on the biophysical parameters involved in the problem, both stable and unstable regimes for tumor growth. (Joint work with Joachim Escher, Hanover)

Travelling Waves in a Mathematical Model for Oncolytic Virotherapy

Negar Mohammadnejad
University of Alberta
Canada
Co-Author(s):    Thomas Hillen
Abstract:
Oncolytic virotherapy (OVT) is a promising cancer treatment strategy in which engineered viruses selectively infect and destroy tumor cells. Motivated by the biological processes governing viral spread and tumor invasion, we study a non-cooperative reaction-diffusion model describing the spatial propagation of oncolytic viruses within tumor tissue. In this work, we establish the existence of positive travelling-wave solutions and identify a minimal wave speed $\bar{c}$ such that travelling waves exist for all speeds $c \ge \bar{c}$. The results provide a rigorous foundation for understanding the spatial dynamics of OVT and reveal parameter regimes where wave existence remains unresolved, pointing to new mathematical challenges in modeling viral spread in complex tumor environments.

The transition to parenthood in a romantic relationship: A modeling approach

Matilde Polizzi
Politecnico di Milano
Italy
Co-Author(s):    Alessandra Gragnani, Fabio Dercole
Abstract:
The transition to parenthood is a major life event that can profoundly reshape couple emotional dynamics. Starting from a mathematical model of love dynamics in a couple, this work introduces two additional parameters representing caregiving load and the allocation of childcare responsibilities between partners. The extended system is used to investigate how the birth of a child modifies couple regimes and average emotional involvement between the partners. The analysis is carried out for different attachment configurations. For secure-secure couples, increasing caregiving load lowers equilibrium involvement, while the distribution of responsibilities produces trade-offs between equity, total involvement, and robustness. For insecure-secure couples, results are similar but increasing caregiving load may increase the equilibrium involvement of the insecure partner. Moreover, when one partner`s reactions are self-biased by their own involvement, caregiving load can either induce or suppress oscillatory regimes. Finally, when caregiving load is periodic, the interaction between endogenous and exogenous oscillations may generate quasi-periodic or chaotic regimes. Overall, the results show that parenthood acts as a perturbation of love dynamics, shaping relational outcomes according to attachment style, caregiving load, and the distribution of parental responsibilities.

Self-regulation and resource dependent growth rates: a size-structured predator-prey mode

Andrea Pugliese
University of Trento
Italy
Co-Author(s):    Simone De Reggi, Shangjiang Guo, Mimmo Iannelli and Xuan Tian
Abstract:
The dynamical behaviour of a size-structured predator-prey model with nonlinear growth rates is studied. In the model prey growth rate decreases with prey population density, while predator growth rate depends on predation. In its most general case the model is described by the following system of quasi-linear equations where $u(t, s)$ and $v(t, s)$, for $t\ge 0$ and $s\in (0, 1)$, denote the prey and predator size-density, respectively, and, by appropriate scaling, 0 denotes the size at birth and 1 the maximal size: \[ \left\{\setlength\arraycolsep{0.1em}\begin{array}{rlll} \displaystyle \frac{\partial}{\partial t} u(t, s) + \frac{\partial}{\partial s}\left[g_1(U(t), s) u(t, s)\right]&=& -\left[d_1(s)+(Hv(t, \cdot))(s)\right]u(t, s), \ [4mm] \displaystyle\frac{\partial}{\partial t} v(t, s) + \frac{\partial}{\partial s} \left[g_2\left( (Ku(t,\cdot))(s), s\right)v(t,s)\right]&=& -d_2(s)v(t, s) ,\[2mm] g_1(U(t), 0)u(t,0)&=&\displaystyle\int_0^{\bar s} \beta_1(s) u(t, s)\,ds, \[3mm] g_2\left((Ku(t,\cdot) )(0), 0\right)v(t,0)&=&\displaystyle\int_0^{\bar s} \beta_2(s) v(t, s)\,ds,\[2mm] \end{array} \right. \] where $H, K\colon L^1((0, 1), \mathbf{R})\to L^1((0, 1), \mathbf{R})$ are \[ (H\phi)(s)=\int_0^{1} k(z, s)\phi(z)\,dz,\qquad (K\phi)(s)=\int_0^{1} z k(s, z)\phi(z)\, dz,\quad s\in (0, 1), \] for $k\in L^1((0, 1)^2, \mathbf{R})$ %(in other words, $H$ is the adjoint of $K$), and \[ U(t)=\int_0^{1} u(t, s)\,ds,\qquad t\ge0. \] Existence and uniqueness of solutions are proved under weak conditions. A threshold is established for the stability of the predator-free equilibrium and the existence of a positive equilbrium. When restricted only to the prey, the model is a special case of the one studied by Farkas and Hagen (2007); in this case it is shown that the positive equilibrium may undergo Hopf bifurcation, a novel feature in this class of models. Finally, numerical exploration of the bifurcations is perfomed through pseudospectral collocation methods.

A new framework for Generalized Lotka-Volterra models

Polly Y. Yu
University of Illinois Urbana-Champaign
USA
Co-Author(s):    Gheorghe Craciun, Diego Rojas La Luz
Abstract:
Generalized Lotka-Volterra (GLV) systems, as models of ecological communities, can display diverse dynamics, ranging from global stability, to periodic orbits, and even chaos. We propose a framework of studying GLV systems by borrowing ideas from reaction network theory, specifically the notions of detailed-balanced and complex-balanced. We associate any GLV system to a directed graph embedded in $\mathbb{R}^n$, and prove theorems of the form: If the graph has property $P$, then the associated GLV system has dynamical property $X$. For example, if the embedded graph is strongly connected, then the GLV system has a globally stable coexistence equilibrium (within each invariance manifold). The stability is guaranteed by a different Lyapunov function than the Volterra Lypaunov function. Other dynamical properties we can infer include persistence, and ruling out limit cycles.